From a0d796163618cdc6289f4ce3d4227a6862e0fcbd Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Wed, 30 Oct 2024 19:42:31 +0000 Subject: [PATCH] build based on 05d5da5 --- dev/404.html | 21 + dev/api.html | 69 ++ dev/assets/api.md.BVKEWZkd.js | 44 + dev/assets/api.md.BVKEWZkd.lean.js | 44 + dev/assets/app.U_Oek0tF.js | 1 + .../chunks/@localSearchIndexroot.1wBqazEy.js | 1 + .../chunks/VPLocalSearchBox.C3Vo0S4v.js | 7 + dev/assets/chunks/framework.NQCVMzj3.js | 18 + dev/assets/chunks/theme.C7mqWqIN.js | 2 + dev/assets/features.md.Cy11UbG5.js | 117 +++ dev/assets/features.md.Cy11UbG5.lean.js | 117 +++ dev/assets/getstarted.md.eqfmZk8K.js | 48 + dev/assets/getstarted.md.eqfmZk8K.lean.js | 48 + dev/assets/index.md.DFDvIJs_.js | 1 + dev/assets/index.md.DFDvIJs_.lean.js | 1 + .../inter-italic-cyrillic-ext.r48I6akx.woff2 | Bin 0 -> 43112 bytes .../inter-italic-cyrillic.By2_1cv3.woff2 | Bin 0 -> 31300 bytes .../inter-italic-greek-ext.1u6EdAuj.woff2 | Bin 0 -> 17404 bytes 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100644 dev/index.html create mode 100644 dev/iodocs.html create mode 100644 dev/logo.drawio.svg create mode 100644 dev/logo.png create mode 100644 dev/logo.svg create mode 100644 dev/orftype.html create mode 100644 dev/roadmap.html create mode 100644 dev/simplecodingrule.html diff --git a/dev/404.html b/dev/404.html new file mode 100644 index 0000000..85abf40 --- /dev/null +++ b/dev/404.html @@ -0,0 +1,21 @@ + + + + + + 404 | GeneFinder.jl + + + + + + + + + + +
+ + + + \ No newline at end of file diff --git a/dev/api.html b/dev/api.html new file mode 100644 index 0000000..e6f2ef4 --- /dev/null +++ b/dev/api.html @@ -0,0 +1,69 @@ + + + + + + GeneFinder.jl + + + + + + + + + + + + + +
GeneFinder.jl

Appearance

The Main ORF type ​

The main type of the package is ORFI which represents an Open Reading Frame Interval. It is a subtype of the GenomicInterval type from the GenomicFeatures package.

GeneFinder.OpenReadingFrameInterval Type
julia
struct ORFI{N,F} <: AbstractGenomicInterval{F}

The ORFI struct represents an Open Reading Frame Interval (ORFI) in genomics.

Fields

  • groupname::String: The name of the group to which the ORFI belongs.

  • first::Int64: The starting position of the ORFI.

  • last::Int64: The ending position of the ORFI.

  • strand::Strand: The strand on which the ORFI is located.

  • frame::Int: The reading frame of the ORFI.

  • seq::LongSubSeq{DNAAlphabet{N}}: The DNA sequence of the ORFI.

  • features::Features: The features associated with the ORFI.

Main Constructor

julia
ORFI{N,F}(
+    groupname::String,
+    first::Int64,
+    last::Int64,
+    strand::Strand,
+    frame::Int,
+    features::Features,
+    seq::LongSubSeq{DNAAlphabet{N}}
+)

Example

A full instance ORFI

julia
ORFI{4,NaiveFinder}("seq01", 1, 33, STRAND_POS, 1, Features((score = 0.0,)), nothing)

A partial instance ORFI

julia
ORFI{NaiveFinder}(1:33, '+', 1)

source

GeneFinder.features Method
julia
features(i::ORFI{N,F})

Extracts the features from an ORFI object.

Arguments

  • i::ORFI{N,F}: An ORFI object.

Returns

The features of the ORFI object. Those could be defined by each GeneFinderMethod.

source

GeneFinder.sequence Method
julia
sequence(i::ORFI{N,F})

Extracts the DNA sequence corresponding to the given open reading frame (ORFI).

Arguments

  • i::ORFI{N,F}: The open reading frame (ORFI) for which the DNA sequence needs to be extracted.

Returns

  • The DNA sequence corresponding to the given open reading frame (ORFI).

source

GeneFinder.source Method
julia
source(i::ORFI{N,F})

Get the source sequence associated with the given ORFI object.

Arguments

  • i::ORFI{N,F}: The ORFI object for which to retrieve the source sequence.

Returns

The source sequence associated with the ORFI object.

Examples

julia
seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+orfs = findorfs(seq)
+source(orfs[1])
+
+44nt DNA Sequence:
+ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA

Warning

The source method works if the sequence is defined in the global scope. Otherwise it will throw an error. For instance a common failure is to define a simple ORFI that by defualt will have an "unnamedsource" as groupname and then try to get the source sequence.

julia
orf = ORFI{NaiveFinder}(1:33, '+', 1)
+source(orf)
+
+ERROR: UndefVarError: `unnamedsource` not defined
+Stacktrace:
+ [1] source(i::ORFI{4, NaiveFinder})
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:192
+ [2] top-level scope
+   @ REPL[12]:1

source

Finding ORFIs ​

The function findorfs serves as a method interface as it is generic method that can handle different gene finding methods.

GeneFinder.findorfs Method
julia
findorfs(sequence::NucleicSeqOrView{DNAAlphabet{N}}; ::F, kwargs...) where {N, F<:GeneFinderMethod}

This is the main interface method for finding open reading frames (ORFIs) in a DNA sequence.

It takes the following required arguments:

  • sequence: The nucleic acid sequence to search for ORFIs.

  • finder: The algorithm used to find ORFIs. It can be either NaiveFinder, NaiveCollector or yet other implementations.

Keyword Arguments regardless of the finder method:

  • alternative_start::Bool: A boolean indicating whether to consider alternative start codons. Default is false.

  • minlen::Int: The minimum length of an ORFI. Default is 6.

  • scheme::Function: The scoring scheme to use for scoring the sequence from the ORFI. Default is nothing.

Returns

A vector of ORFI objects representing the found ORFIs.

Example

julia
sequence = randdnaseq(120)
+
+120nt DNA Sequence:
+ GCCGGACAGCGAAGGCTAATAAATGCCCGTGCCAGTATC…TCTGAGTTACTGTACACCCGAAAGACGTTGTACGCATTT
+
+findorfs(sequence, finder=NaiveFinder)
+
+1-element Vector{ORFI}:
+ ORFI{NaiveFinder}(77:118, '-', 2)

source

Finding ORFs using BioRegex ​

GeneFinder.NaiveFinder Method
julia
NaiveFinder(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) -> Vector{ORFI{N,F}} where {N,F}

A simple implementation that finds ORFIs in a DNA sequence.

The NaiveFinder method takes a LongSequence{DNAAlphabet{4}} sequence and returns a Vector{ORFI} containing the ORFIs found in the sequence. It searches entire regularly expressed CDS, adding each ORFI it finds to the vector. The function also searches the reverse complement of the sequence, so it finds ORFIs on both strands. Extending the starting codons with the alternative_start = true will search for ATG, GTG, and TTG. Some studies have shown that in E. coli (K-12 strain), ATG, GTG and TTG are used 83 %, 14 % and 3 % respectively.

Note

This function has neither ORFIs scoring scheme by default nor length constraints. Thus it might consider aa"M*" a posible encoding protein from the resulting ORFIs.

Required Arguments

  • seq::NucleicSeqOrView{DNAAlphabet{N}}: The nucleic acid sequence to search for ORFIs.

Keywords Arguments

  • alternative_start::Bool: If true will pass the extended start codons to search. This will increase 3x the execution time. Default is false.

  • minlen::Int64=6: Length of the allowed ORFI. Default value allow aa"M*" a posible encoding protein from the resulting ORFIs.

Note

As the scheme is generally a scoring function that at least requires a sequence, one simple scheme is the log-odds ratio score. This score is a log-odds ratio that compares the probability of the sequence generated by a coding model to the probability of the sequence generated by a non-coding model:

S(x)=∑i=1Lβxix=∑i=1log⁡ai−1m1xiai−1m2xi

If the log-odds ratio exceeds a given threshold (η), the sequence is considered likely to be coding. See lordr for more information about coding creteria.

source

GeneFinder._locationiterator Method
julia
_locationiterator(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) where {N}

This is an iterator function that uses regular expressions to search the entire ORFI (instead of start and stop codons) in a LongSequence{DNAAlphabet{4}} sequence. It uses an anonymous function that will find the first regularly expressed ORFI. Then using this anonymous function it creates an iterator that will apply it until there is no other CDS.

Note

As a note of the implementation we want to expand on how the ORFIs are found:

The expression (?:[N]{3})*? serves as the boundary between the start and stop codons. Within this expression, the character class [N]{3} captures exactly three occurrences of any character (representing nucleotides using IUPAC codes). This portion functions as the regular codon matches. Since it is enclosed within a non-capturing group (?:) and followed by *?, it allows for the matching of intermediate codons, but with a preference for the smallest number of repetitions.

In summary, the regular expression ATG(?:[N]{3})*?T(AG|AA|GA) identifies patterns that start with "ATG," followed by any number of three-character codons (represented by "N" in the IUPAC code), and ends with a stop codon "TAG," "TAA," or "TGA." This pattern is commonly used to identify potential protein-coding regions within genetic sequences.

See more about the discussion here

source

GeneFinder.NaiveCollector Method
julia
NaiveCollector(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) -> Vector{ORFI{N,F}} where {N,F}

The NaiveCollector function searches for open reading frames (ORFs) in a DNA sequence. It takes the following arguments:

Required Arguments

  • seq::NucleicSeqOrView{DNAAlphabet{N}}: The nucleic sequence to search for ORFs.

Keywords Arguments

  • alternative_start::Bool: A flag indicating whether to consider alternative start codons. Default is false.

  • minlen::Int64: The minimum length of an ORF. Default is 6.

  • overlap::Bool: A flag indicating whether to allow overlapping ORFs. Default is false.

The function returns a sorted vector of ORFI{N,NaiveCollector} objects, representing the identified ORFs.

Note

This method finds, by default, non-overlapping ORFs in the given sequence. It is much faster than the NaiveFinder method. Althought it uses the same regular expression to find ORFs in a source sequence, it levarages on the eachmatch function to find all the ORFs in the sequence.

Warning

Using the overlap = true flag will increase the runtime of the function significantly, but some of the ORFs found may display premature stop codons.

source

Writing ORFs to files ​

GeneFinder.write_orfs_bed Method
julia
write_orfs_bed(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)
+write_orfs_bed(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write BED data to a file.

Arguments

  • input: The input DNA sequence NucSeq or a view.

  • output: The otput format, it can be a file (String) or a buffer (IOStream or `IOBuffer)

  • finder: The algorithm used to find ORFIs. It can be either NaiveFinder() or NaiveFinderScored().

Keywords

  • alternative_start::Bool=false: If true, alternative start codons will be used when identifying CDSs. Default is false.

  • minlen::Int64=6: The minimum length that a CDS must have in order to be included in the output file. Default is 6.

source

GeneFinder.write_orfs_faa Method
julia
write_orfs_faa(input::NucleicSeqOrView{DNAAlphabet{4}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)
+write_orfs_faa(input::NucleicSeqOrView{DNAAlphabet{4}}, output::String, finder::F; kwargs...)

Write the protein sequences encoded by the coding sequences (CDSs) of a given DNA sequence to the specified file.

Arguments

  • input: The input DNA sequence NucSeq or a view.

  • output: The otput format, it can be a file (String) or a buffer (IOStream or `IOBuffer)

  • finder: The algorithm used to find ORFIs. It can be either NaiveFinder() or NaiveFinderScored().

Keywords

  • code::GeneticCode=BioSequences.standard_genetic_code: The genetic code by which codons will be translated. See BioSequences.ncbi_trans_table for more info.

  • alternative_start::Bool=false: If true will pass the extended start codons to search. This will increase 3x the exec. time.

  • minlen::Int64=6: Length of the allowed ORFI. Default value allow aa"M*" a posible encoding protein from the resulting ORFIs.

Examples

julia
filename = "output.faa"
+
+seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+
+open(filename, "w") do file
+     write_orfs_faa(seq, file)
+end

source

GeneFinder.write_orfs_fna Method
julia
write_orfs_fna(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)
+write_orfs_fna(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write a file containing the coding sequences (CDSs) of a given DNA sequence to the specified file.

Arguments

  • input::NucleicAcidAlphabet{DNAAlphabet{N}}: The input DNA sequence.

  • output::IO: The otput format, it can be a file (String) or a buffer (IOStream or `IOBuffer)

  • finder::F: The algorithm used to find ORFIs. It can be either NaiveFinder() or NaiveFinderScored().

Keywords

  • alternative_start::Bool=false: If true, alternative start codons will be used when identifying CDSs. Default is false.

  • minlen::Int64=6: The minimum length that a CDS must have in order to be included in the output file. Default is 6.

Examples

julia
filename = "output.fna"
+
+seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+
+open(filename, "w") do file
+     write_orfs_fna(seq, file, NaiveFinder())
+end

source

GeneFinder.write_orfs_gff Method
julia
write_orfs_gff(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)
+write_orfs_gff(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write GFF data to a file.

Arguments

  • input: The input DNA sequence NucSeq or a view.

  • output: The otput format, it can be a file (String) or a buffer (IOStream or `IOBuffer)

  • finder: The algorithm used to find ORFIs. It can be either NaiveFinder() or NaiveFinderScored().

Keywords

  • code::GeneticCode=BioSequences.standard_genetic_code: The genetic code by which codons will be translated. See BioSequences.ncbi_trans_table for more info.

  • alternative_start::Bool=false: If true will pass the extended start codons to search. This will increase 3x the exec. time.

  • minlen::Int64=6: Length of the allowed ORFI. Default value allow aa"M*" a posible encoding protein from the resulting ORFIs.

source

+ + + + \ No newline at end of file diff --git a/dev/assets/api.md.BVKEWZkd.js b/dev/assets/api.md.BVKEWZkd.js new file mode 100644 index 0000000..b888016 --- /dev/null +++ b/dev/assets/api.md.BVKEWZkd.js @@ -0,0 +1,44 @@ +import{_ as p,c as l,j as s,a as e,G as n,a5 as a,B as o,o as h}from"./chunks/framework.NQCVMzj3.js";const j=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),r={name:"api.md"},d={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},m={class:"tip custom-block"},E={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},Q={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-3.033ex"},xmlns:"http://www.w3.org/2000/svg",width:"31.936ex",height:"7.198ex",role:"img",focusable:"false",viewBox:"0 -1840.7 14115.8 3181.3","aria-hidden":"true"},F={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""};function N(D,i,v,w,B,O){const t=o("Badge");return h(),l("div",null,[i[42]||(i[42]=s("h2",{id:"The-Main-ORF-type",tabindex:"-1"},[e("The Main ORF type "),s("a",{class:"header-anchor",href:"#The-Main-ORF-type","aria-label":'Permalink to "The Main ORF type {#The-Main-ORF-type}"'},"​")],-1)),i[43]||(i[43]=s("p",null,[e("The main type of the package is "),s("code",null,"ORFI"),e(" which represents an Open Reading Frame Interval. It is a subtype of the "),s("code",null,"GenomicInterval"),e(" type from the "),s("code",null,"GenomicFeatures"),e(" package.")],-1)),s("details",d,[s("summary",null,[i[0]||(i[0]=s("a",{id:"GeneFinder.OpenReadingFrameInterval",href:"#GeneFinder.OpenReadingFrameInterval"},[s("span",{class:"jlbinding"},"GeneFinder.OpenReadingFrameInterval")],-1)),i[1]||(i[1]=e()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[2]||(i[2]=a(`
julia
struct ORFI{N,F} <: AbstractGenomicInterval{F}

The ORFI struct represents an Open Reading Frame Interval (ORFI) in genomics.

Fields

Main Constructor

julia
ORFI{N,F}(
+    groupname::String,
+    first::Int64,
+    last::Int64,
+    strand::Strand,
+    frame::Int,
+    features::Features,
+    seq::LongSubSeq{DNAAlphabet{N}}
+)

Example

A full instance ORFI

julia
ORFI{4,NaiveFinder}("seq01", 1, 33, STRAND_POS, 1, Features((score = 0.0,)), nothing)

A partial instance ORFI

julia
ORFI{NaiveFinder}(1:33, '+', 1)

source

`,12))]),s("details",k,[s("summary",null,[i[3]||(i[3]=s("a",{id:"GeneFinder.features-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}",href:"#GeneFinder.features-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}"},[s("span",{class:"jlbinding"},"GeneFinder.features")],-1)),i[4]||(i[4]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[5]||(i[5]=a('
julia
features(i::ORFI{N,F})

Extracts the features from an ORFI object.

Arguments

Returns

The features of the ORFI object. Those could be defined by each GeneFinderMethod.

source

',7))]),s("details",c,[s("summary",null,[i[6]||(i[6]=s("a",{id:"GeneFinder.sequence-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}",href:"#GeneFinder.sequence-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}"},[s("span",{class:"jlbinding"},"GeneFinder.sequence")],-1)),i[7]||(i[7]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[8]||(i[8]=a('
julia
sequence(i::ORFI{N,F})

Extracts the DNA sequence corresponding to the given open reading frame (ORFI).

Arguments

Returns

source

',7))]),s("details",g,[s("summary",null,[i[9]||(i[9]=s("a",{id:"GeneFinder.source-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}",href:"#GeneFinder.source-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}"},[s("span",{class:"jlbinding"},"GeneFinder.source")],-1)),i[10]||(i[10]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[11]||(i[11]=a(`
julia
source(i::ORFI{N,F})

Get the source sequence associated with the given ORFI object.

Arguments

Returns

The source sequence associated with the ORFI object.

Examples

julia
seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+orfs = findorfs(seq)
+source(orfs[1])
+
+44nt DNA Sequence:
+ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA

Warning

The source method works if the sequence is defined in the global scope. Otherwise it will throw an error. For instance a common failure is to define a simple ORFI that by defualt will have an "unnamedsource" as groupname and then try to get the source sequence.

julia
orf = ORFI{NaiveFinder}(1:33, '+', 1)
+source(orf)
+
+ERROR: UndefVarError: \`unnamedsource\` not defined
+Stacktrace:
+ [1] source(i::ORFI{4, NaiveFinder})
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:192
+ [2] top-level scope
+   @ REPL[12]:1

source

`,10))]),i[44]||(i[44]=s("h2",{id:"Finding-ORFIs",tabindex:"-1"},[e("Finding ORFIs "),s("a",{class:"header-anchor",href:"#Finding-ORFIs","aria-label":'Permalink to "Finding ORFIs {#Finding-ORFIs}"'},"​")],-1)),i[45]||(i[45]=s("p",null,[e("The function "),s("code",null,"findorfs"),e(" serves as a method interface as it is generic method that can handle different gene finding methods.")],-1)),s("details",u,[s("summary",null,[i[12]||(i[12]=s("a",{id:"GeneFinder.findorfs-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{F}, Tuple{N}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.findorfs-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{F}, Tuple{N}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.findorfs")],-1)),i[13]||(i[13]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[14]||(i[14]=a(`
julia
findorfs(sequence::NucleicSeqOrView{DNAAlphabet{N}}; ::F, kwargs...) where {N, F<:GeneFinderMethod}

This is the main interface method for finding open reading frames (ORFIs) in a DNA sequence.

It takes the following required arguments:

Keyword Arguments regardless of the finder method:

Returns

A vector of ORFI objects representing the found ORFIs.

Example

julia
sequence = randdnaseq(120)
+
+120nt DNA Sequence:
+ GCCGGACAGCGAAGGCTAATAAATGCCCGTGCCAGTATC…TCTGAGTTACTGTACACCCGAAAGACGTTGTACGCATTT
+
+findorfs(sequence, finder=NaiveFinder)
+
+1-element Vector{ORFI}:
+ ORFI{NaiveFinder}(77:118, '-', 2)

source

`,11))]),i[46]||(i[46]=s("h2",{id:"Finding-ORFs-using-BioRegex",tabindex:"-1"},[e("Finding ORFs using BioRegex "),s("a",{class:"header-anchor",href:"#Finding-ORFs-using-BioRegex","aria-label":'Permalink to "Finding ORFs using BioRegex {#Finding-ORFs-using-BioRegex}"'},"​")],-1)),s("details",T,[s("summary",null,[i[15]||(i[15]=s("a",{id:"GeneFinder.NaiveFinder-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N",href:"#GeneFinder.NaiveFinder-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N"},[s("span",{class:"jlbinding"},"GeneFinder.NaiveFinder")],-1)),i[16]||(i[16]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[22]||(i[22]=a('
julia
NaiveFinder(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) -> Vector{ORFI{N,F}} where {N,F}

A simple implementation that finds ORFIs in a DNA sequence.

The NaiveFinder method takes a LongSequence{DNAAlphabet{4}} sequence and returns a Vector{ORFI} containing the ORFIs found in the sequence. It searches entire regularly expressed CDS, adding each ORFI it finds to the vector. The function also searches the reverse complement of the sequence, so it finds ORFIs on both strands. Extending the starting codons with the alternative_start = true will search for ATG, GTG, and TTG. Some studies have shown that in E. coli (K-12 strain), ATG, GTG and TTG are used 83 %, 14 % and 3 % respectively.

Note

This function has neither ORFIs scoring scheme by default nor length constraints. Thus it might consider aa"M*" a posible encoding protein from the resulting ORFIs.

Required Arguments

Keywords Arguments

',8)),s("div",m,[i[19]||(i[19]=s("p",{class:"custom-block-title"},"Note",-1)),i[20]||(i[20]=s("p",null,"As the scheme is generally a scoring function that at least requires a sequence, one simple scheme is the log-odds ratio score. This score is a log-odds ratio that compares the probability of the sequence generated by a coding model to the probability of the sequence generated by a non-coding model:",-1)),s("mjx-container",E,[(h(),l("svg",Q,i[17]||(i[17]=[a('𝓂𝓂',1)]))),i[18]||(i[18]=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mi",null,"S"),s("mo",{stretchy:"false"},"("),s("mi",null,"x"),s("mo",{stretchy:"false"},")"),s("mo",null,"="),s("munderover",null,[s("mo",{"data-mjx-texclass":"OP"},"∑"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"="),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"L")])]),s("msub",null,[s("mi",null,"β"),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mi",null,"x"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i")])]),s("mi",null,"x")])]),s("mo",null,"="),s("munder",null,[s("mo",{"data-mjx-texclass":"OP"},"∑"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"="),s("mn",null,"1")])]),s("mi",null,"log"),s("mo",{"data-mjx-texclass":"NONE"},"⁡"),s("mfrac",null,[s("mrow",null,[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"1")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])]),s("mrow",null,[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"2")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])])])])],-1))]),i[21]||(i[21]=s("p",null,[e("If the log-odds ratio exceeds a given threshold ("),s("code",null,"η"),e("), the sequence is considered likely to be coding. See "),s("a",{href:"./@ref"},[s("code",null,"lordr")]),e(" for more information about coding creteria.")],-1))]),i[23]||(i[23]=s("p",null,[s("a",{href:"https://github.com/camilogarciabotero/GeneFinder.jl/blob/05d5da5b2ec62beb9d091ab415f3efacd8eca929/src/algorithms/naivefinder.jl#L38-L67",target:"_blank",rel:"noreferrer"},"source")],-1))]),s("details",F,[s("summary",null,[i[24]||(i[24]=s("a",{id:"GeneFinder._locationiterator-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N",href:"#GeneFinder._locationiterator-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N"},[s("span",{class:"jlbinding"},"GeneFinder._locationiterator")],-1)),i[25]||(i[25]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[26]||(i[26]=a('
julia
_locationiterator(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) where {N}

This is an iterator function that uses regular expressions to search the entire ORFI (instead of start and stop codons) in a LongSequence{DNAAlphabet{4}} sequence. It uses an anonymous function that will find the first regularly expressed ORFI. Then using this anonymous function it creates an iterator that will apply it until there is no other CDS.

Note

As a note of the implementation we want to expand on how the ORFIs are found:

The expression (?:[N]{3})*? serves as the boundary between the start and stop codons. Within this expression, the character class [N]{3} captures exactly three occurrences of any character (representing nucleotides using IUPAC codes). This portion functions as the regular codon matches. Since it is enclosed within a non-capturing group (?:) and followed by *?, it allows for the matching of intermediate codons, but with a preference for the smallest number of repetitions.

In summary, the regular expression ATG(?:[N]{3})*?T(AG|AA|GA) identifies patterns that start with "ATG," followed by any number of three-character codons (represented by "N" in the IUPAC code), and ends with a stop codon "TAG," "TAA," or "TGA." This pattern is commonly used to identify potential protein-coding regions within genetic sequences.

See more about the discussion here

source

',4))]),s("details",y,[s("summary",null,[i[27]||(i[27]=s("a",{id:"GeneFinder.NaiveCollector-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N",href:"#GeneFinder.NaiveCollector-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N"},[s("span",{class:"jlbinding"},"GeneFinder.NaiveCollector")],-1)),i[28]||(i[28]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[29]||(i[29]=a('
julia
NaiveCollector(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) -> Vector{ORFI{N,F}} where {N,F}

The NaiveCollector function searches for open reading frames (ORFs) in a DNA sequence. It takes the following arguments:

Required Arguments

Keywords Arguments

The function returns a sorted vector of ORFI{N,NaiveCollector} objects, representing the identified ORFs.

Note

This method finds, by default, non-overlapping ORFs in the given sequence. It is much faster than the NaiveFinder method. Althought it uses the same regular expression to find ORFs in a source sequence, it levarages on the eachmatch function to find all the ORFs in the sequence.

Warning

Using the overlap = true flag will increase the runtime of the function significantly, but some of the ORFs found may display premature stop codons.

source

',10))]),i[47]||(i[47]=s("h2",{id:"Writing-ORFs-to-files",tabindex:"-1"},[e("Writing ORFs to files "),s("a",{class:"header-anchor",href:"#Writing-ORFs-to-files","aria-label":'Permalink to "Writing ORFs to files {#Writing-ORFs-to-files}"'},"​")],-1)),s("details",f,[s("summary",null,[i[30]||(i[30]=s("a",{id:"GeneFinder.write_orfs_bed-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.write_orfs_bed-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.write_orfs_bed")],-1)),i[31]||(i[31]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[32]||(i[32]=a('
julia
write_orfs_bed(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)\nwrite_orfs_bed(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write BED data to a file.

Arguments

Keywords

source

',7))]),s("details",b,[s("summary",null,[i[33]||(i[33]=s("a",{id:"GeneFinder.write_orfs_faa-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.write_orfs_faa-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.write_orfs_faa")],-1)),i[34]||(i[34]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[35]||(i[35]=a(`
julia
write_orfs_faa(input::NucleicSeqOrView{DNAAlphabet{4}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)
+write_orfs_faa(input::NucleicSeqOrView{DNAAlphabet{4}}, output::String, finder::F; kwargs...)

Write the protein sequences encoded by the coding sequences (CDSs) of a given DNA sequence to the specified file.

Arguments

Keywords

Examples

julia
filename = "output.faa"
+
+seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+
+open(filename, "w") do file
+     write_orfs_faa(seq, file)
+end

source

`,9))]),s("details",C,[s("summary",null,[i[36]||(i[36]=s("a",{id:"GeneFinder.write_orfs_fna-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.write_orfs_fna-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.write_orfs_fna")],-1)),i[37]||(i[37]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[38]||(i[38]=a(`
julia
write_orfs_fna(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)
+write_orfs_fna(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write a file containing the coding sequences (CDSs) of a given DNA sequence to the specified file.

Arguments

Keywords

Examples

julia
filename = "output.fna"
+
+seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+
+open(filename, "w") do file
+     write_orfs_fna(seq, file, NaiveFinder())
+end

source

`,9))]),s("details",A,[s("summary",null,[i[39]||(i[39]=s("a",{id:"GeneFinder.write_orfs_gff-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.write_orfs_gff-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.write_orfs_gff")],-1)),i[40]||(i[40]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[41]||(i[41]=a('
julia
write_orfs_gff(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)\nwrite_orfs_gff(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write GFF data to a file.

Arguments

Keywords

source

',7))])])}const x=p(r,[["render",N]]);export{j as __pageData,x as default}; diff --git a/dev/assets/api.md.BVKEWZkd.lean.js b/dev/assets/api.md.BVKEWZkd.lean.js new file mode 100644 index 0000000..b888016 --- /dev/null +++ b/dev/assets/api.md.BVKEWZkd.lean.js @@ -0,0 +1,44 @@ +import{_ as p,c as l,j as s,a as e,G as n,a5 as a,B as o,o as h}from"./chunks/framework.NQCVMzj3.js";const j=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),r={name:"api.md"},d={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},m={class:"tip custom-block"},E={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},Q={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-3.033ex"},xmlns:"http://www.w3.org/2000/svg",width:"31.936ex",height:"7.198ex",role:"img",focusable:"false",viewBox:"0 -1840.7 14115.8 3181.3","aria-hidden":"true"},F={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""};function N(D,i,v,w,B,O){const t=o("Badge");return h(),l("div",null,[i[42]||(i[42]=s("h2",{id:"The-Main-ORF-type",tabindex:"-1"},[e("The Main ORF type "),s("a",{class:"header-anchor",href:"#The-Main-ORF-type","aria-label":'Permalink to "The Main ORF type {#The-Main-ORF-type}"'},"​")],-1)),i[43]||(i[43]=s("p",null,[e("The main type of the package is "),s("code",null,"ORFI"),e(" which represents an Open Reading Frame Interval. It is a subtype of the "),s("code",null,"GenomicInterval"),e(" type from the "),s("code",null,"GenomicFeatures"),e(" package.")],-1)),s("details",d,[s("summary",null,[i[0]||(i[0]=s("a",{id:"GeneFinder.OpenReadingFrameInterval",href:"#GeneFinder.OpenReadingFrameInterval"},[s("span",{class:"jlbinding"},"GeneFinder.OpenReadingFrameInterval")],-1)),i[1]||(i[1]=e()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[2]||(i[2]=a(`
julia
struct ORFI{N,F} <: AbstractGenomicInterval{F}

The ORFI struct represents an Open Reading Frame Interval (ORFI) in genomics.

Fields

Main Constructor

julia
ORFI{N,F}(
+    groupname::String,
+    first::Int64,
+    last::Int64,
+    strand::Strand,
+    frame::Int,
+    features::Features,
+    seq::LongSubSeq{DNAAlphabet{N}}
+)

Example

A full instance ORFI

julia
ORFI{4,NaiveFinder}("seq01", 1, 33, STRAND_POS, 1, Features((score = 0.0,)), nothing)

A partial instance ORFI

julia
ORFI{NaiveFinder}(1:33, '+', 1)

source

`,12))]),s("details",k,[s("summary",null,[i[3]||(i[3]=s("a",{id:"GeneFinder.features-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}",href:"#GeneFinder.features-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}"},[s("span",{class:"jlbinding"},"GeneFinder.features")],-1)),i[4]||(i[4]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[5]||(i[5]=a('
julia
features(i::ORFI{N,F})

Extracts the features from an ORFI object.

Arguments

Returns

The features of the ORFI object. Those could be defined by each GeneFinderMethod.

source

',7))]),s("details",c,[s("summary",null,[i[6]||(i[6]=s("a",{id:"GeneFinder.sequence-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}",href:"#GeneFinder.sequence-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}"},[s("span",{class:"jlbinding"},"GeneFinder.sequence")],-1)),i[7]||(i[7]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[8]||(i[8]=a('
julia
sequence(i::ORFI{N,F})

Extracts the DNA sequence corresponding to the given open reading frame (ORFI).

Arguments

Returns

source

',7))]),s("details",g,[s("summary",null,[i[9]||(i[9]=s("a",{id:"GeneFinder.source-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}",href:"#GeneFinder.source-Union{Tuple{OpenReadingFrameInterval{N, F}}, Tuple{F}, Tuple{N}} where {N, F}"},[s("span",{class:"jlbinding"},"GeneFinder.source")],-1)),i[10]||(i[10]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[11]||(i[11]=a(`
julia
source(i::ORFI{N,F})

Get the source sequence associated with the given ORFI object.

Arguments

Returns

The source sequence associated with the ORFI object.

Examples

julia
seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+orfs = findorfs(seq)
+source(orfs[1])
+
+44nt DNA Sequence:
+ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA

Warning

The source method works if the sequence is defined in the global scope. Otherwise it will throw an error. For instance a common failure is to define a simple ORFI that by defualt will have an "unnamedsource" as groupname and then try to get the source sequence.

julia
orf = ORFI{NaiveFinder}(1:33, '+', 1)
+source(orf)
+
+ERROR: UndefVarError: \`unnamedsource\` not defined
+Stacktrace:
+ [1] source(i::ORFI{4, NaiveFinder})
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:192
+ [2] top-level scope
+   @ REPL[12]:1

source

`,10))]),i[44]||(i[44]=s("h2",{id:"Finding-ORFIs",tabindex:"-1"},[e("Finding ORFIs "),s("a",{class:"header-anchor",href:"#Finding-ORFIs","aria-label":'Permalink to "Finding ORFIs {#Finding-ORFIs}"'},"​")],-1)),i[45]||(i[45]=s("p",null,[e("The function "),s("code",null,"findorfs"),e(" serves as a method interface as it is generic method that can handle different gene finding methods.")],-1)),s("details",u,[s("summary",null,[i[12]||(i[12]=s("a",{id:"GeneFinder.findorfs-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{F}, Tuple{N}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.findorfs-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{F}, Tuple{N}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.findorfs")],-1)),i[13]||(i[13]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[14]||(i[14]=a(`
julia
findorfs(sequence::NucleicSeqOrView{DNAAlphabet{N}}; ::F, kwargs...) where {N, F<:GeneFinderMethod}

This is the main interface method for finding open reading frames (ORFIs) in a DNA sequence.

It takes the following required arguments:

Keyword Arguments regardless of the finder method:

Returns

A vector of ORFI objects representing the found ORFIs.

Example

julia
sequence = randdnaseq(120)
+
+120nt DNA Sequence:
+ GCCGGACAGCGAAGGCTAATAAATGCCCGTGCCAGTATC…TCTGAGTTACTGTACACCCGAAAGACGTTGTACGCATTT
+
+findorfs(sequence, finder=NaiveFinder)
+
+1-element Vector{ORFI}:
+ ORFI{NaiveFinder}(77:118, '-', 2)

source

`,11))]),i[46]||(i[46]=s("h2",{id:"Finding-ORFs-using-BioRegex",tabindex:"-1"},[e("Finding ORFs using BioRegex "),s("a",{class:"header-anchor",href:"#Finding-ORFs-using-BioRegex","aria-label":'Permalink to "Finding ORFs using BioRegex {#Finding-ORFs-using-BioRegex}"'},"​")],-1)),s("details",T,[s("summary",null,[i[15]||(i[15]=s("a",{id:"GeneFinder.NaiveFinder-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N",href:"#GeneFinder.NaiveFinder-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N"},[s("span",{class:"jlbinding"},"GeneFinder.NaiveFinder")],-1)),i[16]||(i[16]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[22]||(i[22]=a('
julia
NaiveFinder(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) -> Vector{ORFI{N,F}} where {N,F}

A simple implementation that finds ORFIs in a DNA sequence.

The NaiveFinder method takes a LongSequence{DNAAlphabet{4}} sequence and returns a Vector{ORFI} containing the ORFIs found in the sequence. It searches entire regularly expressed CDS, adding each ORFI it finds to the vector. The function also searches the reverse complement of the sequence, so it finds ORFIs on both strands. Extending the starting codons with the alternative_start = true will search for ATG, GTG, and TTG. Some studies have shown that in E. coli (K-12 strain), ATG, GTG and TTG are used 83 %, 14 % and 3 % respectively.

Note

This function has neither ORFIs scoring scheme by default nor length constraints. Thus it might consider aa"M*" a posible encoding protein from the resulting ORFIs.

Required Arguments

Keywords Arguments

',8)),s("div",m,[i[19]||(i[19]=s("p",{class:"custom-block-title"},"Note",-1)),i[20]||(i[20]=s("p",null,"As the scheme is generally a scoring function that at least requires a sequence, one simple scheme is the log-odds ratio score. This score is a log-odds ratio that compares the probability of the sequence generated by a coding model to the probability of the sequence generated by a non-coding model:",-1)),s("mjx-container",E,[(h(),l("svg",Q,i[17]||(i[17]=[a('𝓂𝓂',1)]))),i[18]||(i[18]=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mi",null,"S"),s("mo",{stretchy:"false"},"("),s("mi",null,"x"),s("mo",{stretchy:"false"},")"),s("mo",null,"="),s("munderover",null,[s("mo",{"data-mjx-texclass":"OP"},"∑"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"="),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"L")])]),s("msub",null,[s("mi",null,"β"),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mi",null,"x"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i")])]),s("mi",null,"x")])]),s("mo",null,"="),s("munder",null,[s("mo",{"data-mjx-texclass":"OP"},"∑"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"="),s("mn",null,"1")])]),s("mi",null,"log"),s("mo",{"data-mjx-texclass":"NONE"},"⁡"),s("mfrac",null,[s("mrow",null,[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"1")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])]),s("mrow",null,[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"2")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])])])])],-1))]),i[21]||(i[21]=s("p",null,[e("If the log-odds ratio exceeds a given threshold ("),s("code",null,"η"),e("), the sequence is considered likely to be coding. See "),s("a",{href:"./@ref"},[s("code",null,"lordr")]),e(" for more information about coding creteria.")],-1))]),i[23]||(i[23]=s("p",null,[s("a",{href:"https://github.com/camilogarciabotero/GeneFinder.jl/blob/05d5da5b2ec62beb9d091ab415f3efacd8eca929/src/algorithms/naivefinder.jl#L38-L67",target:"_blank",rel:"noreferrer"},"source")],-1))]),s("details",F,[s("summary",null,[i[24]||(i[24]=s("a",{id:"GeneFinder._locationiterator-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N",href:"#GeneFinder._locationiterator-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N"},[s("span",{class:"jlbinding"},"GeneFinder._locationiterator")],-1)),i[25]||(i[25]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[26]||(i[26]=a('
julia
_locationiterator(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) where {N}

This is an iterator function that uses regular expressions to search the entire ORFI (instead of start and stop codons) in a LongSequence{DNAAlphabet{4}} sequence. It uses an anonymous function that will find the first regularly expressed ORFI. Then using this anonymous function it creates an iterator that will apply it until there is no other CDS.

Note

As a note of the implementation we want to expand on how the ORFIs are found:

The expression (?:[N]{3})*? serves as the boundary between the start and stop codons. Within this expression, the character class [N]{3} captures exactly three occurrences of any character (representing nucleotides using IUPAC codes). This portion functions as the regular codon matches. Since it is enclosed within a non-capturing group (?:) and followed by *?, it allows for the matching of intermediate codons, but with a preference for the smallest number of repetitions.

In summary, the regular expression ATG(?:[N]{3})*?T(AG|AA|GA) identifies patterns that start with "ATG," followed by any number of three-character codons (represented by "N" in the IUPAC code), and ends with a stop codon "TAG," "TAA," or "TGA." This pattern is commonly used to identify potential protein-coding regions within genetic sequences.

See more about the discussion here

source

',4))]),s("details",y,[s("summary",null,[i[27]||(i[27]=s("a",{id:"GeneFinder.NaiveCollector-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N",href:"#GeneFinder.NaiveCollector-Union{Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}}, Tuple{N}} where N"},[s("span",{class:"jlbinding"},"GeneFinder.NaiveCollector")],-1)),i[28]||(i[28]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[29]||(i[29]=a('
julia
NaiveCollector(seq::NucleicSeqOrView{DNAAlphabet{N}}; kwargs...) -> Vector{ORFI{N,F}} where {N,F}

The NaiveCollector function searches for open reading frames (ORFs) in a DNA sequence. It takes the following arguments:

Required Arguments

Keywords Arguments

The function returns a sorted vector of ORFI{N,NaiveCollector} objects, representing the identified ORFs.

Note

This method finds, by default, non-overlapping ORFs in the given sequence. It is much faster than the NaiveFinder method. Althought it uses the same regular expression to find ORFs in a source sequence, it levarages on the eachmatch function to find all the ORFs in the sequence.

Warning

Using the overlap = true flag will increase the runtime of the function significantly, but some of the ORFs found may display premature stop codons.

source

',10))]),i[47]||(i[47]=s("h2",{id:"Writing-ORFs-to-files",tabindex:"-1"},[e("Writing ORFs to files "),s("a",{class:"header-anchor",href:"#Writing-ORFs-to-files","aria-label":'Permalink to "Writing ORFs to files {#Writing-ORFs-to-files}"'},"​")],-1)),s("details",f,[s("summary",null,[i[30]||(i[30]=s("a",{id:"GeneFinder.write_orfs_bed-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.write_orfs_bed-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.write_orfs_bed")],-1)),i[31]||(i[31]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[32]||(i[32]=a('
julia
write_orfs_bed(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)\nwrite_orfs_bed(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write BED data to a file.

Arguments

Keywords

source

',7))]),s("details",b,[s("summary",null,[i[33]||(i[33]=s("a",{id:"GeneFinder.write_orfs_faa-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.write_orfs_faa-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.write_orfs_faa")],-1)),i[34]||(i[34]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[35]||(i[35]=a(`
julia
write_orfs_faa(input::NucleicSeqOrView{DNAAlphabet{4}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)
+write_orfs_faa(input::NucleicSeqOrView{DNAAlphabet{4}}, output::String, finder::F; kwargs...)

Write the protein sequences encoded by the coding sequences (CDSs) of a given DNA sequence to the specified file.

Arguments

Keywords

Examples

julia
filename = "output.faa"
+
+seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+
+open(filename, "w") do file
+     write_orfs_faa(seq, file)
+end

source

`,9))]),s("details",C,[s("summary",null,[i[36]||(i[36]=s("a",{id:"GeneFinder.write_orfs_fna-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.write_orfs_fna-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.write_orfs_fna")],-1)),i[37]||(i[37]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[38]||(i[38]=a(`
julia
write_orfs_fna(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)
+write_orfs_fna(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write a file containing the coding sequences (CDSs) of a given DNA sequence to the specified file.

Arguments

Keywords

Examples

julia
filename = "output.fna"
+
+seq = dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAGCTAGCTAGTAA"
+
+open(filename, "w") do file
+     write_orfs_fna(seq, file, NaiveFinder())
+end

source

`,9))]),s("details",A,[s("summary",null,[i[39]||(i[39]=s("a",{id:"GeneFinder.write_orfs_gff-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}",href:"#GeneFinder.write_orfs_gff-Union{Tuple{F}, Tuple{N}, Tuple{Union{BioSequences.LongDNA{N}, BioSequences.LongSubSeq{BioSequences.DNAAlphabet{N}}}, Union{IOStream, IOBuffer}}} where {N, F<:GeneFinder.GeneFinderMethod}"},[s("span",{class:"jlbinding"},"GeneFinder.write_orfs_gff")],-1)),i[40]||(i[40]=e()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),i[41]||(i[41]=a('
julia
write_orfs_gff(input::NucleicSeqOrView{DNAAlphabet{N}}, output::Union{IOStream, IOBuffer}, finder::F; kwargs...)\nwrite_orfs_gff(input::NucleicSeqOrView{DNAAlphabet{N}}, output::String, finder::F; kwargs...)

Write GFF data to a file.

Arguments

Keywords

source

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h)this._documentIds.has(m)||(h.size<=1?l.delete(c):h.delete(m));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||Ve.minDirtCount,s=s||Ve.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===ue.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(pt),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(pt),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return ke(this,void 0,void 0,function*(){if(t==null)throw 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c=0;for(const[h,m]of s){const f=new Map;for(const b of Object.keys(m)){let y=m[b];o===1&&(y=y.ds),f.set(parseInt(b,10),yield Ie(y))}++c%1e3===0&&(yield Nt(0)),l._index.set(h,f)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new ue(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new X,c}executeQuery(e,t={}){if(e===ue.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const f=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,f));return this.combineResults(b,f.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,m=o(e).flatMap(f=>l(f)).filter(f=>!!f).map($s(i)).map(f=>this.executeQuerySpec(f,i));return this.combineResults(m,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:Pe(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},ht.weights),i),m=this._index.get(e.term),f=this.termResults(e.term,e.term,1,e.termBoost,m,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const R=x.length-e.term.length;if(!R)continue;y==null||y.delete(x);const C=h*x.length/(x.length+.3*R);this.termResults(e.term,x,C,e.termBoost,w,n,r,l,f)}if(y)for(const x of y.keys()){const[w,R]=y.get(x);if(!R)continue;const C=c*x.length/(x.length+R);this.termResults(e.term,x,C,e.termBoost,w,n,r,l,f)}return f}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=qe){if(e.length===0)return new Map;const s=t.toLowerCase(),n=Ps[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const m=i[h],f=this._fieldIds[h],b=r.get(f);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[f];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(f,w,t),y-=1;continue}const R=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!R)continue;const C=b.get(w),J=this._fieldLength.get(w)[f],Q=Vs(C,y,this._documentCount,J,x,l),W=s*n*m*R*Q,V=c.get(w);if(V){V.score+=W,Ws(V.terms,e);const $=Pe(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,vt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,vt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". Removing a document after it has changed can corrupt the index!`,"version_conflict");return}}addDocumentId(e){const t=this._nextId;return this._idToShortId.set(e,t),this._documentIds.set(t,e),this._documentCount+=1,this._nextId+=1,t}addFields(e){for(let t=0;tObject.prototype.hasOwnProperty.call(a,e)?a[e]:void 0,Ps={[qe]:(a,e)=>{for(const t of e.keys()){const s=a.get(t);if(s==null)a.set(t,e.get(t));else{const{score:n,terms:r,match:i}=e.get(t);s.score=s.score+n,s.match=Object.assign(s.match,i),ft(s.terms,r)}}return a},[kt]:(a,e)=>{const t=new Map;for(const s of e.keys()){const n=a.get(s);if(n==null)continue;const{score:r,terms:i,match:o}=e.get(s);ft(n.terms,i),t.set(s,{score:n.score+r,terms:n.terms,match:Object.assign(n.match,o)})}return t},[zs]:(a,e)=>{for(const t of e.keys())a.delete(t);return a}},js={k:1.2,b:.7,d:.5},Vs=(a,e,t,s,n,r)=>{const{k:i,b:o,d:l}=r;return Math.log(1+(t-e+.5)/(e+.5))*(l+a*(i+1)/(a+i*(1-o+o*s/n)))},$s=a=>(e,t,s)=>{const n=typeof 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Scoring ORFs ​

The ORFI type is designed to be flexible and can store various types of information about ORFs. A very neat feature is that stores a view of the sequence that the ORF represents. Since the the ORFI sequence is stored in the struct, then any method that can be applied to a sequence can be applied to the ORF sequence. This is useful for scoring the ORFs by overloading a method that calculates a score for a BioSequence. For instance the lors function from the BioMarkovChains.jl package can be used to calculate a score of the ORFs predicted for the phi genome.

Take the following example:

julia
phi = dna"GTGTGAGGTTATAACGCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACTTTTATTTCTCGCCATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCTGTTTTACAGACACCTAAAGCTACATCGTCAACGTTATATTTTGATAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCATTCAGATGGATACATCTGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCTATGATGTTTATCCTTTGAATGGTCGCCATGATGGTGGTTATTATACCGTCAAGGACTGTGTGACTATTGACGTCCTTCCCCGTACGCCGGGCAATAACGTTTATGTTGGTTTCATGGTTTGGTCTAACTTTACCGCTACTAAATGCCGCGGATTGGTTTCGCTGAATCAGGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGGCATTCAAGGTGATGTGCTTGCTACCGATAACAATACTGTAGGCATGGGTGATGCTGGTATTAAATCTGCCATTCAAGGCTCTAATGTTCCTAACCCTGATGAGGCCGCCCCTAGTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAAGGATACTCGTGATTATCTTGCTGCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCTCTGCTGGTATGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTTACTAAAATGCAACTGGACAATCAGAAAGAGATTGCCGAGATGCAAAATGAGACTCAAAAAGAGATTGCTGGCATTCAGTCGGCGACTTCACGCCAGAATACGAAAGACCAGGTATATGCACAAAATGAGATGCTTGCTTATCAACAGAAGGAGTCTACTGCTCGCGTTGCGTCTATTATGGAAAACACCAATCTTTCCAAGCAACAGCAGGTTTCCGAGATTATGCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTCAGTATTTTACCAATGACCAAATCAAAGAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAATCAGCGGTATGGCTCTTCTCATATTGGCGCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACAATTTCTGGAAAGACGGTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGGAAATAACCGTCAGGATTGACACCCTCCCAATTGTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCTGAATGTCACGCTGATTATTTTGACTTTGAGCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAGCAGATGGATAACCGCATCAAGCTCTTGGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATGGTGATATGTATGTTGACGGCCATAAGGCTGCTTCTGACGTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTTAATGGATGAATTGGCACAATGCTACAATGTGCTCCCCCAACTTGATATTAATAACACTATAGACCACCGCCCCGAAGGGGACGAAAAATGGTTTTTAGAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTCGCGATGAGTATAATTACCCCAAAAAGAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGATGAATGCAATGCGACAGGCTCATGCTGATGGTTGGTTTATCGTTTTTGACACTCTCACGTTGGCTGACGACCGATTAGAGGCGTTTTATGATAATCCCAATGCTTTGCGTGACTATTTTCGTGATATTGGTCGTATGGTTCTTGCTGCCGAGGGTCGCAAGGCTAATGATTCACACGCCGACTGCTATCAGTATTTTTGTGTGCCTGAGTATGGTACAGCTAATGGCCGTCTTCATTTCCATGCGGTGCACTTTATGCGGACACTTCCTACAGGTAGCGTTGACCCTAATTTTGGTCGTCGGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAAAATACGTGGCCTTATGGTTACAGTATGCCCATCGCAGTTCGCTACACGCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATATGGCTGTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAGTCAGATATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTCACTAAAAACCAAGCTGTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTCGGGATGAAAATGCTCACAATGACAAATCTGTCCACGGAGTGCTTAATCCAACTTACCAAGCTGGGTTACGACGCGACGCCGTTCAACCAGATATTGAAGCAGAACGCAAAAAGAGAGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGCGCAACCTGTGACGACAAATCTGCTCAAATTTATGCGCGCTTCGATAAAAATGATTGGCGTATCCAACCTGCAGAGTTTTATCGCTTCCATGACGCAGAAGTTAACACTTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGGAATTACTACTGCTTGTTTACGAATTAAATCGAAGTGGACTGCTGGCGGAAAATGAGAAAATTCGACCTATCCTTGCGCAGCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGACTGGTTTAGATATGAGTCACATTTTGTTCATGGTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGTGGATTACTATCTGAGTCCGATGCTGTTCAACCACTAATAGGTAAGAAATCATGAGTCAAGTTACTGAACAATCCGTACGTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGCCGTTTTGGATTTAACCGAAGATGATTTCGATTTTCTGACGAGTAACAAAGTTTGGATTGCTACTGACCGCTCTCGTGCTCGTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGGATACCCTCGCTTTCCTGCTCCTGTTGAGTTTATTGCTGCCGTCATTGCTTATTATGTTCATCCCGTCAACATTCAAACGGCCTGTCTCATCATGGAAGGCGCTGAATTTACGGAAAACATTATTAATGGCGTCGAGCGTCCGGTTAAAGCCGCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGCAGGAAACACTGACGTTCTTACTGACGCAGAAGAAAACGTGCGTCAAAAATTACGTGCGGAAGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGTACTAAAGGCAAGCGTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTTACTTGAGGATAAATTATGTCTAATATTCAAACTGGCGCCGAGCGTATGCCGCATGACCTTTCCCATCTTGGCTTCCTTGCTGGTCAGATTGGTCGTCTTATTACCATTTCAACTACTCCGGTTATCGCTGGCGACTCCTTCGAGATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGGCCTTGCTATTGACTCTACTGTAGACATTTTTACTTTTTATGTCCCTCATCGTCACGTTTATGGTGAACAGTGGATTAAGTTCATGAAGGATGGTGTTAATGCCACTCCTCTCCCGACTGTTAACACTACTGGTTATATTGACCATGCCGCTTTTCTTGGCACGATTAACCCTGATACCAATAAAATCCCTAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTAAAGCGCCGTGGATGCCTGACCGTACCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAAGCTGCTTATGCTAATTTGCATACTGACCAAGAACGTGATTACTTCATGCAGCGTTACCATGATGTTATTTCTTCATTTGGAGGTAAAACCTCTTATGACGCTGACAACCGTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCATCTGGCTATGATGTTGATGGAACTGACCAAACGTCGTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCTATAAACATTCTGTGCCGCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTTTGACTTATACCGATATTGCTGGCGACCCTGTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATGTGACCGTTTATCGCAATCTGCCGACCACTCGCGATTCAATCATGACTTCGTGATAAAAGATTGA"
+
+phiorfs = findorfs(phi, finder=NaiveFinder, minlen=75)
+
+124-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(9:101, '-', 3)
+ ORFI{NaiveFinder}(100:627, '+', 1)
+ ORFI{NaiveFinder}(223:447, '-', 1)
+ ORFI{NaiveFinder}(248:436, '+', 2)
+ ORFI{NaiveFinder}(257:436, '+', 2)
+ ORFI{NaiveFinder}(283:627, '+', 1)
+ ORFI{NaiveFinder}(344:436, '+', 2)
+ ORFI{NaiveFinder}(532:627, '+', 1)
+ ORFI{NaiveFinder}(636:1622, '+', 3)
+ ORFI{NaiveFinder}(687:1622, '+', 3)
+ ORFI{NaiveFinder}(774:1622, '+', 3)
+ ORFI{NaiveFinder}(781:1389, '+', 1)
+ ORFI{NaiveFinder}(814:1389, '+', 1)
+ ORFI{NaiveFinder}(829:1389, '+', 1)
+ ORFI{NaiveFinder}(861:1622, '+', 3)
+ ⋮
+ ORFI{NaiveFinder}(4671:5375, '+', 3)
+ ORFI{NaiveFinder}(4690:4866, '+', 1)
+ ORFI{NaiveFinder}(4728:5375, '+', 3)
+ ORFI{NaiveFinder}(4741:4866, '+', 1)
+ ORFI{NaiveFinder}(4744:4866, '+', 1)
+ ORFI{NaiveFinder}(4777:4866, '+', 1)
+ ORFI{NaiveFinder}(4806:5375, '+', 3)
+ ORFI{NaiveFinder}(4863:5258, '-', 3)
+ ORFI{NaiveFinder}(4933:5019, '+', 1)
+ ORFI{NaiveFinder}(4941:5375, '+', 3)
+ ORFI{NaiveFinder}(5082:5375, '+', 3)
+ ORFI{NaiveFinder}(5089:5325, '+', 1)
+ ORFI{NaiveFinder}(5122:5202, '-', 1)
+ ORFI{NaiveFinder}(5152:5325, '+', 1)
+ ORFI{NaiveFinder}(5164:5325, '+', 1)

We can now calculate a score using the lors (logg_odds_ratio_score) scoring scheme (see lors from the BioMarkovChains.jl package).

julia
lors.(phiorfs)
+
+204-element Vector{Float64}:
+  -3.002461366087374
+   0.1063473191426695
+ -10.814621287968222
+   0.0015932011596656963
+  -2.635307521118871
+  -5.344187934894264
+  -1.35785984913167
+  -1.316724559874126
+  -1.796631200562138
+   0.4895532892313278
+  -3.2651518608269856
+  -1.4019264441082822
+  -0.32264884956233186
+  -0.6695213802037128
+   1.1023617306499074
+   ⋮
+  -5.40624241726446
+  -0.8080572222081075
+  -5.571494087742448
+   0.8307089386780881
+  -4.882156920421228
+  -5.639670353834974
+  -0.8764121443326865
+  -4.308687693802273
+  -4.459423419810693
+   0.5077309777574499
+  -2.2808971022330824
+  -2.138892671183213
+   0.6106494455192994
+   1.1981063545591812
+   0.7566845754226063

Briefly, a sequence of DNA could be scored using a Markov model of the transition probabilities of a known sequence. This could be done using a log-odds ratio score, which is the logarithm of the ratio of the transition probabilities of the sequence given two models. The log-odds ratio score is defined as:

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the ")),s("mjx-container",C,[(t(),T("svg",A,i[2]||(i[2]=[a('𝓂',1)]))),i[3]||(i[3]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"1")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])])],-1))]),i[15]||(i[15]=n(" is the transition probability of the first model (in this case the calculated for the given sequence) from the state ")),s("mjx-container",d,[(t(),T("svg",r,i[4]||(i[4]=[a('',1)]))),i[5]||(i[5]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"x"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")])])])],-1))]),i[16]||(i[16]=n(" to the state ")),s("mjx-container",G,[(t(),T("svg",Q,i[6]||(i[6]=[a('',1)]))),i[7]||(i[7]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 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The score is the sum of the log-odds ratio of the transition probabilities of the sequence given the two models."))]),i[22]||(i[22]=a(`

In the lors case, the two models are the coding and non-coding models of the E. coli genome. The coding model is a Markov model of the transition probabilities of the coding regions of the E. coli genome, and the non-coding model is a Markov model of the transition probabilities of the non-coding regions of the E. coli genome.

Analysing Lambda ORFs ​

As mentioned above the lors calculates the log odds ratio of the ORFI sequence given two Markov models (by default: ECOLICDS and ECOLINOCDS), one for the coding region and one for the non-coding region. By default the lors function return the base 2 logarithm of the odds ratio, so it is analogous to the bits of information that the ORFI sequence is coding.

Now we can even analyse how is the distribution of the ORFIs' scores as a function of their lengths compared to random sequences.

julia
using FASTX, CairoMakie
+
+lambdafile = "test/data/NC_001416.1.fasta"
+
+# read the lambda genome as a \`BioSequence\`
+open(FASTA.Reader, lambdafile) do reader
+    lambdaseq = FASTX.sequence(LongDNA{4}, collect(reader)[1])
+end
+
+# find the ORFs in the lambda genome
+lambaorfs = findorfs(lambdaseq, finder=NaiveFinder, minlen=100)
+
+lambdascores = lors.(lambaorfs)
+lambdalengths = length.(lambaorfs)
+
+## get some random sequences of variable lengths
+vseqs = LongDNA[]
+for i in 1:708
+    push!(vseqs, randdnaseq(rand(100:2500)))
+end
+
+## get the lengths and scores of the random generated sequences
+randlengths = length.(vseqs)
+randscores = lors.(vseqs)
+
+## plot the scores as a function of the lengths
+f = Figure()
+ax = Axis(f[1, 1], xlabel="Length", ylabel="Log-odds ratio (Bits)")
+
+scatter!(ax,
+    randlengths,
+    randscores,
+    marker = :circle, 
+    markersize = 6, 
+    color = :black, 
+    label = "Random sequences"
+)
+scatter!(ax,
+    lambdalengths, 
+    lambdascores, 
+    marker = :rect, 
+    markersize = 6, 
+    color = :blue, 
+    label = "Lambda ORFs"
+)
+
+axislegend(ax)
+
+f

What this plot shows is that the ORFs in the lambda genome have a higher scores than random sequences of the same length. The score is a measure of how likely a sequence given the coding model is compared to the non-coding model. In other words, the higher the score the more likely the sequence is coding. So, the plot shows that the ORFs in the lambda genome are more likely to be coding regions than random sequences. It also shows that the longer the ORFI the higher the score, which is expected since longer ORFs are more likely to be coding regions than shorter ones.

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Scoring ORFs ​

The ORFI type is designed to be flexible and can store various types of information about ORFs. A very neat feature is that stores a view of the sequence that the ORF represents. Since the the ORFI sequence is stored in the struct, then any method that can be applied to a sequence can be applied to the ORF sequence. This is useful for scoring the ORFs by overloading a method that calculates a score for a BioSequence. For instance the lors function from the BioMarkovChains.jl package can be used to calculate a score of the ORFs predicted for the phi genome.

Take the following example:

julia
phi = dna"GTGTGAGGTTATAACGCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACTTTTATTTCTCGCCATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCTGTTTTACAGACACCTAAAGCTACATCGTCAACGTTATATTTTGATAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCATTCAGATGGATACATCTGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCTATGATGTTTATCCTTTGAATGGTCGCCATGATGGTGGTTATTATACCGTCAAGGACTGTGTGACTATTGACGTCCTTCCCCGTACGCCGGGCAATAACGTTTATGTTGGTTTCATGGTTTGGTCTAACTTTACCGCTACTAAATGCCGCGGATTGGTTTCGCTGAATCAGGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGGCATTCAAGGTGATGTGCTTGCTACCGATAACAATACTGTAGGCATGGGTGATGCTGGTATTAAATCTGCCATTCAAGGCTCTAATGTTCCTAACCCTGATGAGGCCGCCCCTAGTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAAGGATACTCGTGATTATCTTGCTGCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCTCTGCTGGTATGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTTACTAAAATGCAACTGGACAATCAGAAAGAGATTGCCGAGATGCAAAATGAGACTCAAAAAGAGATTGCTGGCATTCAGTCGGCGACTTCACGCCAGAATACGAAAGACCAGGTATATGCACAAAATGAGATGCTTGCTTATCAACAGAAGGAGTCTACTGCTCGCGTTGCGTCTATTATGGAAAACACCAATCTTTCCAAGCAACAGCAGGTTTCCGAGATTATGCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTCAGTATTTTACCAATGACCAAATCAAAGAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAATCAGCGGTATGGCTCTTCTCATATTGGCGCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACAATTTCTGGAAAGACGGTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGGAAATAACCGTCAGGATTGACACCCTCCCAATTGTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCTGAATGTCACGCTGATTATTTTGACTTTGAGCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAGCAGATGGATAACCGCATCAAGCTCTTGGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATGGTGATATGTATGTTGACGGCCATAAGGCTGCTTCTGACGTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTTAATGGATGAATTGGCACAATGCTACAATGTGCTCCCCCAACTTGATATTAATAACACTATAGACCACCGCCCCGAAGGGGACGAAAAATGGTTTTTAGAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTCGCGATGAGTATAATTACCCCAAAAAGAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGATGAATGCAATGCGACAGGCTCATGCTGATGGTTGGTTTATCGTTTTTGACACTCTCACGTTGGCTGACGACCGATTAGAGGCGTTTTATGATAATCCCAATGCTTTGCGTGACTATTTTCGTGATATTGGTCGTATGGTTCTTGCTGCCGAGGGTCGCAAGGCTAATGATTCACACGCCGACTGCTATCAGTATTTTTGTGTGCCTGAGTATGGTACAGCTAATGGCCGTCTTCATTTCCATGCGGTGCACTTTATGCGGACACTTCCTACAGGTAGCGTTGACCCTAATTTTGGTCGTCGGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAAAATACGTGGCCTTATGGTTACAGTATGCCCATCGCAGTTCGCTACACGCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATATGGCTGTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAGTCAGATATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTCACTAAAAACCAAGCTGTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTCGGGATGAAAATGCTCACAATGACAAATCTGTCCACGGAGTGCTTAATCCAACTTACCAAGCTGGGTTACGACGCGACGCCGTTCAACCAGATATTGAAGCAGAACGCAAAAAGAGAGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGCGCAACCTGTGACGACAAATCTGCTCAAATTTATGCGCGCTTCGATAAAAATGATTGGCGTATCCAACCTGCAGAGTTTTATCGCTTCCATGACGCAGAAGTTAACACTTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGGAATTACTACTGCTTGTTTACGAATTAAATCGAAGTGGACTGCTGGCGGAAAATGAGAAAATTCGACCTATCCTTGCGCAGCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGACTGGTTTAGATATGAGTCACATTTTGTTCATGGTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGTGGATTACTATCTGAGTCCGATGCTGTTCAACCACTAATAGGTAAGAAATCATGAGTCAAGTTACTGAACAATCCGTACGTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGCCGTTTTGGATTTAACCGAAGATGATTTCGATTTTCTGACGAGTAACAAAGTTTGGATTGCTACTGACCGCTCTCGTGCTCGTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGGATACCCTCGCTTTCCTGCTCCTGTTGAGTTTATTGCTGCCGTCATTGCTTATTATGTTCATCCCGTCAACATTCAAACGGCCTGTCTCATCATGGAAGGCGCTGAATTTACGGAAAACATTATTAATGGCGTCGAGCGTCCGGTTAAAGCCGCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGCAGGAAACACTGACGTTCTTACTGACGCAGAAGAAAACGTGCGTCAAAAATTACGTGCGGAAGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGTACTAAAGGCAAGCGTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTTACTTGAGGATAAATTATGTCTAATATTCAAACTGGCGCCGAGCGTATGCCGCATGACCTTTCCCATCTTGGCTTCCTTGCTGGTCAGATTGGTCGTCTTATTACCATTTCAACTACTCCGGTTATCGCTGGCGACTCCTTCGAGATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGGCCTTGCTATTGACTCTACTGTAGACATTTTTACTTTTTATGTCCCTCATCGTCACGTTTATGGTGAACAGTGGATTAAGTTCATGAAGGATGGTGTTAATGCCACTCCTCTCCCGACTGTTAACACTACTGGTTATATTGACCATGCCGCTTTTCTTGGCACGATTAACCCTGATACCAATAAAATCCCTAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTAAAGCGCCGTGGATGCCTGACCGTACCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAAGCTGCTTATGCTAATTTGCATACTGACCAAGAACGTGATTACTTCATGCAGCGTTACCATGATGTTATTTCTTCATTTGGAGGTAAAACCTCTTATGACGCTGACAACCGTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCATCTGGCTATGATGTTGATGGAACTGACCAAACGTCGTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCTATAAACATTCTGTGCCGCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTTTGACTTATACCGATATTGCTGGCGACCCTGTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATGTGACCGTTTATCGCAATCTGCCGACCACTCGCGATTCAATCATGACTTCGTGATAAAAGATTGA"
+
+phiorfs = findorfs(phi, finder=NaiveFinder, minlen=75)
+
+124-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(9:101, '-', 3)
+ ORFI{NaiveFinder}(100:627, '+', 1)
+ ORFI{NaiveFinder}(223:447, '-', 1)
+ ORFI{NaiveFinder}(248:436, '+', 2)
+ ORFI{NaiveFinder}(257:436, '+', 2)
+ ORFI{NaiveFinder}(283:627, '+', 1)
+ ORFI{NaiveFinder}(344:436, '+', 2)
+ ORFI{NaiveFinder}(532:627, '+', 1)
+ ORFI{NaiveFinder}(636:1622, '+', 3)
+ ORFI{NaiveFinder}(687:1622, '+', 3)
+ ORFI{NaiveFinder}(774:1622, '+', 3)
+ ORFI{NaiveFinder}(781:1389, '+', 1)
+ ORFI{NaiveFinder}(814:1389, '+', 1)
+ ORFI{NaiveFinder}(829:1389, '+', 1)
+ ORFI{NaiveFinder}(861:1622, '+', 3)
+ ⋮
+ ORFI{NaiveFinder}(4671:5375, '+', 3)
+ ORFI{NaiveFinder}(4690:4866, '+', 1)
+ ORFI{NaiveFinder}(4728:5375, '+', 3)
+ ORFI{NaiveFinder}(4741:4866, '+', 1)
+ ORFI{NaiveFinder}(4744:4866, '+', 1)
+ ORFI{NaiveFinder}(4777:4866, '+', 1)
+ ORFI{NaiveFinder}(4806:5375, '+', 3)
+ ORFI{NaiveFinder}(4863:5258, '-', 3)
+ ORFI{NaiveFinder}(4933:5019, '+', 1)
+ ORFI{NaiveFinder}(4941:5375, '+', 3)
+ ORFI{NaiveFinder}(5082:5375, '+', 3)
+ ORFI{NaiveFinder}(5089:5325, '+', 1)
+ ORFI{NaiveFinder}(5122:5202, '-', 1)
+ ORFI{NaiveFinder}(5152:5325, '+', 1)
+ ORFI{NaiveFinder}(5164:5325, '+', 1)

We can now calculate a score using the lors (logg_odds_ratio_score) scoring scheme (see lors from the BioMarkovChains.jl package).

julia
lors.(phiorfs)
+
+204-element Vector{Float64}:
+  -3.002461366087374
+   0.1063473191426695
+ -10.814621287968222
+   0.0015932011596656963
+  -2.635307521118871
+  -5.344187934894264
+  -1.35785984913167
+  -1.316724559874126
+  -1.796631200562138
+   0.4895532892313278
+  -3.2651518608269856
+  -1.4019264441082822
+  -0.32264884956233186
+  -0.6695213802037128
+   1.1023617306499074
+   ⋮
+  -5.40624241726446
+  -0.8080572222081075
+  -5.571494087742448
+   0.8307089386780881
+  -4.882156920421228
+  -5.639670353834974
+  -0.8764121443326865
+  -4.308687693802273
+  -4.459423419810693
+   0.5077309777574499
+  -2.2808971022330824
+  -2.138892671183213
+   0.6106494455192994
+   1.1981063545591812
+   0.7566845754226063

Briefly, a sequence of DNA could be scored using a Markov model of the transition probabilities of a known sequence. This could be done using a log-odds ratio score, which is the logarithm of the ratio of the transition probabilities of the sequence given two models. The log-odds ratio score is defined as:

`,7)),s("mjx-container",k,[(t(),T("svg",p,i[0]||(i[0]=[a('𝓂𝓂',1)]))),i[1]||(i[1]=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mtable",{displaystyle:"true",columnalign:"right",columnspacing:"",rowspacing:"3pt"},[s("mtr",null,[s("mtd",null,[s("mi",null,"S"),s("mo",{stretchy:"false"},"("),s("mi",null,"x"),s("mo",{stretchy:"false"},")"),s("mo",null,"="),s("munderover",null,[s("mo",{"data-mjx-texclass":"OP"},"∑"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"="),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"L")])]),s("msub",null,[s("mi",null,"β"),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mi",null,"x"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i")])]),s("mi",null,"x")])]),s("mo",null,"="),s("munder",null,[s("mo",{"data-mjx-texclass":"OP"},"∑"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"="),s("mn",null,"1")])]),s("mi",null,"log"),s("mo",{"data-mjx-texclass":"NONE"},"⁡"),s("mfrac",null,[s("mrow",null,[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"1")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])]),s("mrow",null,[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"2")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])])])])])])])],-1))]),s("p",null,[i[14]||(i[14]=n("Where the ")),s("mjx-container",C,[(t(),T("svg",A,i[2]||(i[2]=[a('𝓂',1)]))),i[3]||(i[3]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"1")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])])],-1))]),i[15]||(i[15]=n(" is the transition probability of the first model (in this case the calculated for the given sequence) from the state ")),s("mjx-container",d,[(t(),T("svg",r,i[4]||(i[4]=[a('',1)]))),i[5]||(i[5]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"x"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")])])])],-1))]),i[16]||(i[16]=n(" to the state ")),s("mjx-container",G,[(t(),T("svg",Q,i[6]||(i[6]=[a('',1)]))),i[7]||(i[7]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])])],-1))]),i[17]||(i[17]=n(" and ")),s("mjx-container",o,[(t(),T("svg",g,i[8]||(i[8]=[a('𝓂',1)]))),i[9]||(i[9]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msubsup",null,[s("mi",null,"a"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",{mathvariant:"script"},"m")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("mn",null,"2")])])])]),s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])])],-1))]),i[18]||(i[18]=n(" is the transition probability of the second model from the state ")),s("mjx-container",m,[(t(),T("svg",E,i[10]||(i[10]=[a('',1)]))),i[11]||(i[11]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"x"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"i"),s("mo",null,"−"),s("mn",null,"1")])])])],-1))]),i[19]||(i[19]=n(" to the state ")),s("mjx-container",F,[(t(),T("svg",y,i[12]||(i[12]=[a('',1)]))),i[13]||(i[13]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"x"),s("mi",null,"i")])])],-1))]),i[20]||(i[20]=n(". The score is the sum of the log-odds ratio of the transition probabilities of the sequence given the two models."))]),i[22]||(i[22]=a(`

In the lors case, the two models are the coding and non-coding models of the E. coli genome. The coding model is a Markov model of the transition probabilities of the coding regions of the E. coli genome, and the non-coding model is a Markov model of the transition probabilities of the non-coding regions of the E. coli genome.

Analysing Lambda ORFs ​

As mentioned above the lors calculates the log odds ratio of the ORFI sequence given two Markov models (by default: ECOLICDS and ECOLINOCDS), one for the coding region and one for the non-coding region. By default the lors function return the base 2 logarithm of the odds ratio, so it is analogous to the bits of information that the ORFI sequence is coding.

Now we can even analyse how is the distribution of the ORFIs' scores as a function of their lengths compared to random sequences.

julia
using FASTX, CairoMakie
+
+lambdafile = "test/data/NC_001416.1.fasta"
+
+# read the lambda genome as a \`BioSequence\`
+open(FASTA.Reader, lambdafile) do reader
+    lambdaseq = FASTX.sequence(LongDNA{4}, collect(reader)[1])
+end
+
+# find the ORFs in the lambda genome
+lambaorfs = findorfs(lambdaseq, finder=NaiveFinder, minlen=100)
+
+lambdascores = lors.(lambaorfs)
+lambdalengths = length.(lambaorfs)
+
+## get some random sequences of variable lengths
+vseqs = LongDNA[]
+for i in 1:708
+    push!(vseqs, randdnaseq(rand(100:2500)))
+end
+
+## get the lengths and scores of the random generated sequences
+randlengths = length.(vseqs)
+randscores = lors.(vseqs)
+
+## plot the scores as a function of the lengths
+f = Figure()
+ax = Axis(f[1, 1], xlabel="Length", ylabel="Log-odds ratio (Bits)")
+
+scatter!(ax,
+    randlengths,
+    randscores,
+    marker = :circle, 
+    markersize = 6, 
+    color = :black, 
+    label = "Random sequences"
+)
+scatter!(ax,
+    lambdalengths, 
+    lambdascores, 
+    marker = :rect, 
+    markersize = 6, 
+    color = :blue, 
+    label = "Lambda ORFs"
+)
+
+axislegend(ax)
+
+f

What this plot shows is that the ORFs in the lambda genome have a higher scores than random sequences of the same length. The score is a measure of how likely a sequence given the coding model is compared to the non-coding model. In other words, the higher the score the more likely the sequence is coding. So, the plot shows that the ORFs in the lambda genome are more likely to be coding regions than random sequences. It also shows that the longer the ORFI the higher the score, which is expected since longer ORFs are more likely to be coding regions than shorter ones.

',7))])}const D=l(h,[["render",c]]);export{H as __pageData,D as default}; diff --git a/dev/assets/getstarted.md.eqfmZk8K.js b/dev/assets/getstarted.md.eqfmZk8K.js new file mode 100644 index 0000000..63891cd --- /dev/null +++ b/dev/assets/getstarted.md.eqfmZk8K.js @@ -0,0 +1,48 @@ +import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.NQCVMzj3.js";const E=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"getstarted.md","filePath":"getstarted.md","lastUpdated":null}'),e={name:"getstarted.md"};function t(l,s,k,p,C,r){return h(),a("div",null,s[0]||(s[0]=[n(`

Finding complete and overlapped ORFIs ​

The main function in the GeneFinder package is findorfs, which serves as an interface to various gene-finding algorithms. By default, findorfs uses a NaiveFinder algorithm, a simple approach that detects all non-outbounded Open Reading Frames (ORFs) in a DNA sequence. You can also specify a different algorithm by setting the finder keyword argument. For more details on the NaiveFinder algorithm, see NaiveFinder documentation for more details.

Note

The minlen keyword argument in NaiveFinder is set to a minimum length of 6 nucleotides (nt). As a result, it may identify short ORFs that aren't necessarily genes, such as dna"ATGTGA" producing the amino acid sequence aa"M*".

Usage example ​

Here's an example of using findorfs with the NaiveFinder algorithm to identify ORFs in a DNA sequence:

julia
julia> using BioSequences, GeneFinder
+
+# > 180195.SAMN03785337.LFLS01000089 -> finds only 1 gene in Prodigal (from Pyrodigal tests)
+seq = dna"AACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAACAGCACTGGCAATCTGACTGTGGGCGGTGTTACCAACGGCACTGCTACTACTGGCAACATCGCACTGACCGGTAACAATGCGCTGAGCGGTCCGGTCAATCTGAATGCGTCGAATGGCACGGTGACCTTGAACACGACCGGCAATACCACGCTCGGTAACGTGACGGCACAAGGCAATGTGACGACCAATGTGTCCAACGGCAGTCTGACGGTTACCGGCAATACGACAGGTGCCAACACCAACCTCAGTGCCAGCGGCAACCTGACCGTGGGTAACCAGGGCAATATCAGTACCGCAGGCAATGCAACCCTGACGGCCGGCGACAACCTGACGAGCACTGGCAATCTGACTGTGGGCGGCGTCACCAACGGCACGGCCACCACCGGCAACATCGCGCTGACCGGTAACAATGCACTGGCTGGTCCTGTCAATCTGAACGCGCCGAACGGCACCGTGACCCTGAACACAACCGGCAATACCACGCTGGGTAATGTCACCGCACAAGGCAATGTGACGACTAATGTGTCCAACGGCAGCCTGACAGTCGCTGGCAATACCACAGGTGCCAACACCAACCTGAGTGCCAGCGGCAATCTGACCGTGGGCAACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAGC"
+
+orfs = findorfs(seq, finder=NaiveFinder) # use finder=NaiveCollector as an alternative
+
+12-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(29:40, '+', 2)
+ ORFI{NaiveFinder}(137:145, '+', 2)
+ ORFI{NaiveFinder}(164:184, '+', 2)
+ ORFI{NaiveFinder}(173:184, '+', 2)
+ ORFI{NaiveFinder}(236:241, '+', 2)
+ ORFI{NaiveFinder}(248:268, '+', 2)
+ ORFI{NaiveFinder}(362:373, '+', 2)
+ ORFI{NaiveFinder}(470:496, '+', 2)
+ ORFI{NaiveFinder}(551:574, '+', 2)
+ ORFI{NaiveFinder}(569:574, '+', 2)
+ ORFI{NaiveFinder}(581:601, '+', 2)
+ ORFI{NaiveFinder}(695:706, '+', 2)

Extracting Sequences from ORFIs ​

The ORFI structure displays the location, frame, and strand, but currently does not include the sequence per se. To extract the sequence of an ORFI instance, you can use the sequence method directly on it, or you can also broadcast it over the orfs collection using the dot syntax .:

julia
julia> sequence.(orfs)
+
+12-element Vector{LongSubSeq{DNAAlphabet{4}}}:
+ ATGCAACCCTGA
+ ATGCGCTGA
+ ATGCGTCGAATGGCACGGTGA
+ ATGGCACGGTGA
+ ATGTGA
+ ATGTGTCCAACGGCAGTCTGA
+ ATGCAACCCTGA
+ ATGCACTGGCTGGTCCTGTCAATCTGA
+ ATGTCACCGCACAAGGCAATGTGA
+ ATGTGA
+ ATGTGTCCAACGGCAGCCTGA
+ ATGCAACCCTGA

Translating ORFIs to Amino Acid Sequences ​

Similarly, you can extract the amino acid sequences of the ORFIs using the translate function.

julia
julia> translate.(orfs)
+
+12-element Vector{LongAA}:
+ MQP*
+ MR*
+ MRRMAR*
+ MAR*
+ M*
+ MCPTAV*
+ MQP*
+ MHWLVLSI*
+ MSPHKAM*
+ M*
+ MCPTAA*
+ MQP*

This returns a vector of translated amino acid sequences, allowing for easy interpretation of each ORF's potential protein product.

`,13)]))}const A=i(e,[["render",t]]);export{E as __pageData,A as default}; diff --git a/dev/assets/getstarted.md.eqfmZk8K.lean.js b/dev/assets/getstarted.md.eqfmZk8K.lean.js new file mode 100644 index 0000000..63891cd --- /dev/null +++ b/dev/assets/getstarted.md.eqfmZk8K.lean.js @@ -0,0 +1,48 @@ +import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.NQCVMzj3.js";const E=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"getstarted.md","filePath":"getstarted.md","lastUpdated":null}'),e={name:"getstarted.md"};function t(l,s,k,p,C,r){return h(),a("div",null,s[0]||(s[0]=[n(`

Finding complete and overlapped ORFIs ​

The main function in the GeneFinder package is findorfs, which serves as an interface to various gene-finding algorithms. By default, findorfs uses a NaiveFinder algorithm, a simple approach that detects all non-outbounded Open Reading Frames (ORFs) in a DNA sequence. You can also specify a different algorithm by setting the finder keyword argument. For more details on the NaiveFinder algorithm, see NaiveFinder documentation for more details.

Note

The minlen keyword argument in NaiveFinder is set to a minimum length of 6 nucleotides (nt). As a result, it may identify short ORFs that aren't necessarily genes, such as dna"ATGTGA" producing the amino acid sequence aa"M*".

Usage example ​

Here's an example of using findorfs with the NaiveFinder algorithm to identify ORFs in a DNA sequence:

julia
julia> using BioSequences, GeneFinder
+
+# > 180195.SAMN03785337.LFLS01000089 -> finds only 1 gene in Prodigal (from Pyrodigal tests)
+seq = dna"AACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAACAGCACTGGCAATCTGACTGTGGGCGGTGTTACCAACGGCACTGCTACTACTGGCAACATCGCACTGACCGGTAACAATGCGCTGAGCGGTCCGGTCAATCTGAATGCGTCGAATGGCACGGTGACCTTGAACACGACCGGCAATACCACGCTCGGTAACGTGACGGCACAAGGCAATGTGACGACCAATGTGTCCAACGGCAGTCTGACGGTTACCGGCAATACGACAGGTGCCAACACCAACCTCAGTGCCAGCGGCAACCTGACCGTGGGTAACCAGGGCAATATCAGTACCGCAGGCAATGCAACCCTGACGGCCGGCGACAACCTGACGAGCACTGGCAATCTGACTGTGGGCGGCGTCACCAACGGCACGGCCACCACCGGCAACATCGCGCTGACCGGTAACAATGCACTGGCTGGTCCTGTCAATCTGAACGCGCCGAACGGCACCGTGACCCTGAACACAACCGGCAATACCACGCTGGGTAATGTCACCGCACAAGGCAATGTGACGACTAATGTGTCCAACGGCAGCCTGACAGTCGCTGGCAATACCACAGGTGCCAACACCAACCTGAGTGCCAGCGGCAATCTGACCGTGGGCAACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAGC"
+
+orfs = findorfs(seq, finder=NaiveFinder) # use finder=NaiveCollector as an alternative
+
+12-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(29:40, '+', 2)
+ ORFI{NaiveFinder}(137:145, '+', 2)
+ ORFI{NaiveFinder}(164:184, '+', 2)
+ ORFI{NaiveFinder}(173:184, '+', 2)
+ ORFI{NaiveFinder}(236:241, '+', 2)
+ ORFI{NaiveFinder}(248:268, '+', 2)
+ ORFI{NaiveFinder}(362:373, '+', 2)
+ ORFI{NaiveFinder}(470:496, '+', 2)
+ ORFI{NaiveFinder}(551:574, '+', 2)
+ ORFI{NaiveFinder}(569:574, '+', 2)
+ ORFI{NaiveFinder}(581:601, '+', 2)
+ ORFI{NaiveFinder}(695:706, '+', 2)

Extracting Sequences from ORFIs ​

The ORFI structure displays the location, frame, and strand, but currently does not include the sequence per se. To extract the sequence of an ORFI instance, you can use the sequence method directly on it, or you can also broadcast it over the orfs collection using the dot syntax .:

julia
julia> sequence.(orfs)
+
+12-element Vector{LongSubSeq{DNAAlphabet{4}}}:
+ ATGCAACCCTGA
+ ATGCGCTGA
+ ATGCGTCGAATGGCACGGTGA
+ ATGGCACGGTGA
+ ATGTGA
+ ATGTGTCCAACGGCAGTCTGA
+ ATGCAACCCTGA
+ ATGCACTGGCTGGTCCTGTCAATCTGA
+ ATGTCACCGCACAAGGCAATGTGA
+ ATGTGA
+ ATGTGTCCAACGGCAGCCTGA
+ ATGCAACCCTGA

Translating ORFIs to Amino Acid Sequences ​

Similarly, you can extract the amino acid sequences of the ORFIs using the translate function.

julia
julia> translate.(orfs)
+
+12-element Vector{LongAA}:
+ MQP*
+ MR*
+ MRRMAR*
+ MAR*
+ M*
+ MCPTAV*
+ MQP*
+ MHWLVLSI*
+ MSPHKAM*
+ M*
+ MCPTAA*
+ MQP*

This returns a vector of translated amino acid sequences, allowing for easy interpretation of each ORF's potential protein product.

`,13)]))}const A=i(e,[["render",t]]);export{E as __pageData,A as default}; diff --git a/dev/assets/index.md.DFDvIJs_.js b/dev/assets/index.md.DFDvIJs_.js new file mode 100644 index 0000000..c7eea9e --- /dev/null +++ b/dev/assets/index.md.DFDvIJs_.js @@ -0,0 +1 @@ +import{_ as t,c as a,a5 as i,o as s}from"./chunks/framework.NQCVMzj3.js";const k=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"GeneFinder.jl","text":"A Gene Finding Framework for the Julia Programming Language","tagline":null,"image":{"src":"/logo.png","alt":"GeneFinder"},"actions":[{"theme":"brand","text":"Get Started","link":"/getstarted.md"},{"theme":"alt","text":"View on Github","link":"https://github.com/camilogarciabotero/GeneFinder.jl"}]}},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function d(l,e,o,r,p,h){return s(),a("div",null,e[0]||(e[0]=[i('

Install GeneFinder from the julia REPL. Press ] to enter pkg mode, and enter the add GeneFinder command.

julia
(@v1.11) pkg> add GeneFinder
',2)]))}const g=t(n,[["render",d]]);export{k as __pageData,g as default}; diff --git a/dev/assets/index.md.DFDvIJs_.lean.js b/dev/assets/index.md.DFDvIJs_.lean.js new file mode 100644 index 0000000..c7eea9e --- /dev/null +++ b/dev/assets/index.md.DFDvIJs_.lean.js @@ -0,0 +1 @@ +import{_ as t,c as a,a5 as i,o as s}from"./chunks/framework.NQCVMzj3.js";const k=JSON.parse('{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"GeneFinder.jl","text":"A Gene Finding Framework for the Julia Programming Language","tagline":null,"image":{"src":"/logo.png","alt":"GeneFinder"},"actions":[{"theme":"brand","text":"Get Started","link":"/getstarted.md"},{"theme":"alt","text":"View on Github","link":"https://github.com/camilogarciabotero/GeneFinder.jl"}]}},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function d(l,e,o,r,p,h){return s(),a("div",null,e[0]||(e[0]=[i('

Install GeneFinder from the julia REPL. Press ] to enter pkg mode, and enter the add GeneFinder command.

julia
(@v1.11) pkg> add GeneFinder
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For convenience, the ORFI type is more stringent in preventing the creation of incompatible instances. As a result, attempting to create an instance with incompatible parameters will result in an error. For instance, the following code snippet will trigger an error:

julia
ORFI{4,NaiveFinder}(1:10, '+', 4) # Or any F <: GeneFinderMethod
+
+ERROR: MethodError: no method matching OpenReadingFrameInterval{4, NaiveFinder}(::UnitRange{Int64}, ::Char, ::Int64)
+
+Closest candidates are:
+  (::Type{OpenReadingFrameInterval{N, F}} where {N, F})(::Any, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any)
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:49
+  OpenReadingFrameInterval{N, F}(::Type{F}, ::String, ::Int64, ::Int64, ::Strand, ::Int8, ::LongSubSeq{DNAAlphabet{N}}, ::NamedTuple) where {N, F<:GeneFinder.GeneFinderMethod}
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:58
+
+Stacktrace:
+ [1] top-level scope
+   @ REPL[21]:1

Similar behavior will be encountered when the strand is neither + nor -. This precautionary measure helps prevent the creation of invalid ORFs, ensuring greater stability and enabling the extension of its interface. For example, after creating a specific ORFI, users can seamlessly iterate over a sequence of interest and verify whether the ORFI is contained within the sequence.

julia
ORFI{4,NaiveFinder}("seq", 1, 33, STRAND_POS, 1, convert(LongSubSeq, dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAG"), NamedTuple())

Warning

It is still possible to create an ORFI and pass it to a sequence that does not necessarily contain an actual open reading frame. This will be addressed in future versions of the package. But the benefit of having it is that it will retrieve the corresponding subsequence of the sequence in a convinient way (5' to 3') regardless of the strand.

`,6)]))}const y=i(t,[["render",k]]);export{g as __pageData,y as default}; diff --git a/dev/assets/orftype.md.BFnL661u.lean.js b/dev/assets/orftype.md.BFnL661u.lean.js new file mode 100644 index 0000000..b7dc77c --- /dev/null +++ b/dev/assets/orftype.md.BFnL661u.lean.js @@ -0,0 +1,13 @@ +import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.NQCVMzj3.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"orftype.md","filePath":"orftype.md","lastUpdated":null}'),t={name:"orftype.md"};function k(e,s,l,p,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`

The ORFI type ​

For convenience, the ORFI type is more stringent in preventing the creation of incompatible instances. As a result, attempting to create an instance with incompatible parameters will result in an error. For instance, the following code snippet will trigger an error:

julia
ORFI{4,NaiveFinder}(1:10, '+', 4) # Or any F <: GeneFinderMethod
+
+ERROR: MethodError: no method matching OpenReadingFrameInterval{4, NaiveFinder}(::UnitRange{Int64}, ::Char, ::Int64)
+
+Closest candidates are:
+  (::Type{OpenReadingFrameInterval{N, F}} where {N, F})(::Any, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any)
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:49
+  OpenReadingFrameInterval{N, F}(::Type{F}, ::String, ::Int64, ::Int64, ::Strand, ::Int8, ::LongSubSeq{DNAAlphabet{N}}, ::NamedTuple) where {N, F<:GeneFinder.GeneFinderMethod}
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:58
+
+Stacktrace:
+ [1] top-level scope
+   @ REPL[21]:1

Similar behavior will be encountered when the strand is neither + nor -. This precautionary measure helps prevent the creation of invalid ORFs, ensuring greater stability and enabling the extension of its interface. For example, after creating a specific ORFI, users can seamlessly iterate over a sequence of interest and verify whether the ORFI is contained within the sequence.

julia
ORFI{4,NaiveFinder}("seq", 1, 33, STRAND_POS, 1, convert(LongSubSeq, dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAG"), NamedTuple())

Warning

It is still possible to create an ORFI and pass it to a sequence that does not necessarily contain an actual open reading frame. This will be addressed in future versions of the package. But the benefit of having it is that it will retrieve the corresponding subsequence of the sequence in a convinient way (5' to 3') regardless of the strand.

`,6)]))}const y=i(t,[["render",k]]);export{g as __pageData,y as default}; diff --git a/dev/assets/roadmap.md.D8FYvId5.js b/dev/assets/roadmap.md.D8FYvId5.js new file mode 100644 index 0000000..7f2059b --- /dev/null +++ b/dev/assets/roadmap.md.D8FYvId5.js @@ -0,0 +1 @@ +import{_ as i,c as a,a5 as l,o as p}from"./chunks/framework.NQCVMzj3.js";const h=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"roadmap.md","filePath":"roadmap.md","lastUpdated":null}'),t={name:"roadmap.md"};function r(n,e,o,s,d,c){return p(),a("div",null,e[0]||(e[0]=[l('

Roadmap ​

Coding genes (CDS - ORFIs) ​

  • ☒ Finding ORFIs

  • ☐ EasyGene

  • ☐ GLIMMER

  • ☐ Prodigal - Pyrodigal

  • ☐ PHANOTATE

  • ☐ k-mer based gene finders (?)

  • ☐ Augustus (?)

Non-coding genes (RNA) ​

  • ☐ Infernal

  • ☐ tRNAscan

Other features ​

  • ☐ parallelism SIMD ?

  • ☐ memory management (?)

  • ☐ incorporate Ribosime Binding Sites (RBS)

  • ☐ incorporate Programmed Reading Frame Shifting (PRFS)

  • ☐ specialized types

    • ☒ Gene

    • ☒ ORFI

    • ☒ Codon

    • ☒ CDS

    • ☐ EukaryoticGene (?)

    • ☐ ProkaryoticGene (?)

    • ☐ Intron

    • ☐ Exon

    • ☐ GFF –&gt; See other packages

    • ☐ FASTX –&gt; See I/O in other packages

Compatibilities ​

Must interact with or extend:

  • GenomicAnnotations.jl

  • BioSequences.jl

  • SequenceVariation.jl

  • GenomicFeatures.jl

  • FASTX.jl

  • Kmers.jl

  • Graphs.jl

',10)]))}const u=i(t,[["render",r]]);export{h as __pageData,u as default}; diff --git a/dev/assets/roadmap.md.D8FYvId5.lean.js b/dev/assets/roadmap.md.D8FYvId5.lean.js new file mode 100644 index 0000000..7f2059b --- /dev/null +++ b/dev/assets/roadmap.md.D8FYvId5.lean.js @@ -0,0 +1 @@ +import{_ as i,c as a,a5 as l,o as p}from"./chunks/framework.NQCVMzj3.js";const h=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"roadmap.md","filePath":"roadmap.md","lastUpdated":null}'),t={name:"roadmap.md"};function r(n,e,o,s,d,c){return p(),a("div",null,e[0]||(e[0]=[l('

Roadmap ​

Coding genes (CDS - ORFIs) ​

  • ☒ Finding ORFIs

  • ☐ EasyGene

  • ☐ GLIMMER

  • ☐ Prodigal - Pyrodigal

  • ☐ PHANOTATE

  • ☐ k-mer based gene finders (?)

  • ☐ Augustus (?)

Non-coding genes (RNA) ​

  • ☐ Infernal

  • ☐ tRNAscan

Other features ​

  • ☐ parallelism SIMD ?

  • ☐ memory management (?)

  • ☐ incorporate Ribosime Binding Sites (RBS)

  • ☐ incorporate Programmed Reading Frame Shifting (PRFS)

  • ☐ specialized types

    • ☒ Gene

    • ☒ ORFI

    • ☒ Codon

    • ☒ CDS

    • ☐ EukaryoticGene (?)

    • ☐ ProkaryoticGene (?)

    • ☐ Intron

    • ☐ Exon

    • ☐ GFF –&gt; See other packages

    • ☐ FASTX –&gt; See I/O in other packages

Compatibilities ​

Must interact with or extend:

  • GenomicAnnotations.jl

  • BioSequences.jl

  • SequenceVariation.jl

  • GenomicFeatures.jl

  • FASTX.jl

  • Kmers.jl

  • Graphs.jl

',10)]))}const u=i(t,[["render",r]]);export{h as __pageData,u as default}; diff --git a/dev/assets/simplecodingrule.md.BGGoeOYF.js b/dev/assets/simplecodingrule.md.BGGoeOYF.js new file mode 100644 index 0000000..65d3076 --- /dev/null +++ b/dev/assets/simplecodingrule.md.BGGoeOYF.js @@ -0,0 +1,95 @@ +import{_ as C,c as a,j as s,a as i,a5 as t,o as A}from"./chunks/framework.NQCVMzj3.js";const F=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"simplecodingrule.md","filePath":"simplecodingrule.md","lastUpdated":null}'),n={name:"simplecodingrule.md"},l={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},h={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-2.172ex"},xmlns:"http://www.w3.org/2000/svg",width:"58.657ex",height:"5.475ex",role:"img",focusable:"false",viewBox:"0 -1460 25926.5 2420","aria-hidden":"true"},G={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},k={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.375ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.856ex",height:"1.92ex",role:"img",focusable:"false",viewBox:"0 -683 1262.4 848.6","aria-hidden":"true"},e={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.339ex"},xmlns:"http://www.w3.org/2000/svg",width:"3.061ex",height:"1.885ex",role:"img",focusable:"false",viewBox:"0 -683 1352.9 833","aria-hidden":"true"};function Q(d,T,r,E,g,o){return A(),a("div",null,[T[11]||(T[11]=s("h2",{id:"The-log-odds-ratio-decision-rule",tabindex:"-1"},[i("The log-odds ratio decision rule "),s("a",{class:"header-anchor",href:"#The-log-odds-ratio-decision-rule","aria-label":'Permalink to "The log-odds ratio decision rule {#The-log-odds-ratio-decision-rule}"'},"​")],-1)),T[12]||(T[12]=s("p",null,[i("The sequence probability given a transition probability model could be used as the source of a sequence classification based on a decision rule to classify whether a sequence correspond to a model or another. Now, imagine we got two DNA sequence transition models, a CDS model and a No-CDS model. The "),s("em",null,"log-odds ratio"),i(" decision rule could be establish as:")],-1)),s("mjx-container",l,[(A(),a("svg",h,T[0]||(T[0]=[t('',1)]))),T[1]||(T[1]=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mtable",{displaystyle:"true",columnalign:"right",columnspacing:"",rowspacing:"3pt"},[s("mtr",null,[s("mtd",null,[s("mi",null,"S"),s("mo",{stretchy:"false"},"("),s("mi",null,"X"),s("mo",{stretchy:"false"},")"),s("mo",null,"="),s("mi",null,"log"),s("mo",{"data-mjx-texclass":"NONE"},"⁡"),s("mfrac",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mi",null,"P"),s("mi",null,"C")]),s("mo",{stretchy:"false"},"("),s("msub",null,[s("mi",null,"X"),s("mn",null,"1")]),s("mo",null,"="),s("msub",null,[s("mi",null,"i"),s("mn",null,"1")]),s("mo",null,","),s("mo",null,"…"),s("mo",null,","),s("msub",null,[s("mi",null,"X"),s("mi",null,"T")]),s("mo",null,"="),s("msub",null,[s("mi",null,"i"),s("mi",null,"T")]),s("mo",{stretchy:"false"},")")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mi",null,"P"),s("mi",null,"N")]),s("mo",{stretchy:"false"},"("),s("msub",null,[s("mi",null,"X"),s("mn",null,"1")]),s("mo",null,"="),s("msub",null,[s("mi",null,"i"),s("mn",null,"1")]),s("mo",null,","),s("mo",null,"…"),s("mo",null,","),s("msub",null,[s("mi",null,"X"),s("mi",null,"T")]),s("mo",null,"="),s("msub",null,[s("mi",null,"i"),s("mi",null,"T")]),s("mo",{stretchy:"false"},")")])]),s("mrow",{"data-mjx-texclass":"INNER"},[s("mo",{"data-mjx-texclass":"OPEN"},"{"),s("mtable",{columnalign:"left left",columnspacing:"1em",rowspacing:".2em"},[s("mtr",null,[s("mtd",null,[s("mo",null,">"),s("mi",null,"η")]),s("mtd",null,[s("mo",{stretchy:"false"},"⇒"),s("mtext",null,"coding")])]),s("mtr",null,[s("mtd",null,[s("mo",null,"<"),s("mi",null,"η")]),s("mtd",null,[s("mo",{stretchy:"false"},"⇒"),s("mtext",null,"noncoding")])])]),s("mo",{"data-mjx-texclass":"CLOSE",fence:"true",stretchy:"true",symmetric:"true"})])])])])])],-1))]),s("p",null,[T[6]||(T[6]=i("Where the ")),s("mjx-container",G,[(A(),a("svg",k,T[2]||(T[2]=[t('',1)]))),T[3]||(T[3]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"P"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"C")])])])],-1))]),T[7]||(T[7]=i(" is the probability of the sequence given a CDS model, ")),s("mjx-container",e,[(A(),a("svg",p,T[4]||(T[4]=[t('',1)]))),T[5]||(T[5]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"P"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"N")])])])],-1))]),T[8]||(T[8]=i(" is the probability of the sequence given a No-CDS model, the decision rule is finally based on whether the ratio is greater or lesser than a given threshold ")),T[9]||(T[9]=s("em",null,"η",-1)),T[10]||(T[10]=i(" of significance level."))]),T[13]||(T[13]=t(`

In this package we have implemented this rule and call some basic models of CDS and No-CDS of E. coli from Axelson-Fisk (2015) work (implemented in BioMarkovChains.jl package). To check whether a random sequence could be coding based on these decision we use the predicate log_odds_ratio_decision_rule with the ECOLICDS and ECOLINOCDS models:

julia
julia> using GeneFinder, BioSequences
+
+seq = dna"TTCGTCAGTCGTTCTGTTTCATTCAATACGATAGTAATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCCAGCATAGCCTCCAGATATCCCCTTATAGAGGTCAGAGGGGAACGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGATCATCCTCGGAACGGGCAATTTCGCTTGCCATATAATTCAGACAGGAAGCCAGATAACCGATTTCATCCTCACTATCGACCTGAAATTCATAATGCATATTACCGGCAGCATACTGCTCTGTGGCATGAGTGATCTTCCTCAGAGGAATATATACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAGCAGGAACAGGATTGCCAGGGTGATATAGGAAATATTCAGCAGGTTGTTACAGGATTTCTGAATATCATTCATATCAGTATGGATGACTACATAGCCTTTTACCTTGTAGTTGGAGGTAATGGGAGCAAATACAGTAAGTACATCCGAATCAAAATTACCGAAGAAATCACCAACAATGTAATAGGAGCCGCTGGTTACGGTCGAATCAAAATTCTCAATGACAACCACATTCTCCACATCTAAGGGACTATTGGTATCCAGTACCAGTCGTCCGGAGGGATTGATGATGCGAATCTCGGAATTCAGGTAGACCGCCAGGGAGTCCAGCTGCATTTTAACGGTCTCCAAAGTTGTTTCACTGGTGTACAATCCGCCGGCATAGGTTCCGGCGATCAGGGTTGCTTCGGAATAGAGACTTTCTGCCTTTTCCCGGATCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAACAATGATGAAACCAAATACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAAAGTGTTTTTTTCATAACAAATCCTGCTTTTGGTATGACTTAATTACGTACTTCGAATTTATAGCCGATGCCCCAGATGGTGCTGATCTTCCAGTTGGCATGATCCTTGATCTTCTC"
+
+orfsdna = findorfs(seq, finder=NaiveFinder, minlen=75, alternative_start=true) .|> sequence
+
+20-element Vector{NucSeq{4, DNAAlphabet{4}}}
+ ATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCC…CGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGA
+ GTGCATTTCCGGTGGAATCGTGCCGTCCAGCATAGCCTC…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ GTGCCGTCCAGCATAGCCTCCAGATATCCCCTTATAGAG…CGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGA
+ GTGGGATACATTGGCTACAAACTTTTTCTGATCATCCTC…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ TTGCCATATAATTCAGACAGGAAGCCAGATAACCGATTT…GCATATTACCGGCAGCATACTGCTCTGTGGCATGAGTGA
+ ATGCTGCCGGTAATATGCATTATGAATTTCAGGTCGATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCACAGAGCAGTATGCTGCCGGTAATATGCATTATG…ATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCATATTACCGGCAGCATACTGCTCTGTGGCATGAGT…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ GTGATCTTCCTCAGAGGAATATATACGATCTCAGTGAAA…ATCAGGGATAGCAGGAACAGGATTGCCAGGGTGATATAG
+ ATGGATGACTACATAGCCTTTTACCTTGTAGTTGGAGGT…ATCAAAATTCTCAATGACAACCACATTCTCCACATCTAA
+ TTGGTGATTTCTTCGGTAATTTTGATTCGGATGTACTTACTGTATTTGCTCCCATTACCTCCAACTACAAGGTAA
+ TTGTTGGTGATTTCTTCGGTAATTTTGATTCGGATGTACTTACTGTATTTGCTCCCATTACCTCCAACTACAAGGTAA
+ ATGACAACCACATTCTCCACATCTAAGGGACTATTGGTA…CCGGAGGGATTGATGATGCGAATCTCGGAATTCAGGTAG
+ ATGCCGGCGGATTGTACACCAGTGAAACAACTTTGGAGACCGTTAAAATGCAGCTGGACTCCCTGGCGGTCTACCTGA
+ TTGTTTCACTGGTGTACAATCCGCCGGCATAGGTTCCGG…TCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAA
+ TTGCTTCGGAATAGAGACTTTCTGCCTTTTCCCGGATCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAA
+ ATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAACAAT…AAATACACCAAAAATAAAATATGCGAGTATAAATTTTAG
+ TTGGTCATATTGGGAACAAAAGTTGTAACAATGATGAAA…ACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAA
+ TTGGGAACAAAAGTTGTAACAATGATGAAACCAAATACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAA
+ ATGCCAACTGGAAGATCAGCACCATCTGGGGCATCGGCT…TACGTAATTAAGTCATACCAAAAGCAGGATTTGTTATGA

Now the question is which of those sequences can we consider as coding sequences. We can use the iscoding predicate to check whether a sequence is coding or not based on the log-odds ratio decision rule:

julia
julia> iscoding.(orfsdna) # criteria = log_odds_ratio_decision_rule
+
+20-element BitVector:
+ 0
+ 0
+ 0
+ 0
+ 0
+ 1
+ 1
+ 0
+ 0
+ 0
+ 0
+ 0
+ 0
+ 1
+ 0
+ 0
+ 0
+ 0
+ 0
+ 0

In this case, the sequence has 20 ORFIs and only 3 of them are classified as coding sequences. The classification is based on the log-odds ratio decision rule and the transition probability models of E. coli CDS and No-CDS. The log_odds_ratio_decision_rule method will return a boolean vector with the classification of each ORFI in the sequence. Now we can simply filter the ORFIs that are coding sequences:

julia
orfs = filter(orf -> iscoding(orf), orfsdna)
+
+3-element Vector{NucSeq{4, DNAAlphabet{4}}}
+ ATGCTGCCGGTAATATGCATTATGAATTTCAGGTCGATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCACAGAGCAGTATGCTGCCGGTAATATGCATTATG…ATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCGGCGGATTGTACACCAGTGAAACAACTTTGGAGACCGTTAAAATGCAGCTGGACTCCCTGGCGGTCTACCTGA

Or in terms of the ORFI object:

julia
julia> orfs = findorfs(seq, minlen=75, finder=NaiveFinder, alternative_start=true) # find ORFIs with alternative start as well
+julia> orfs[iscoding.(orfsdna)]
+
+3-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(194:268, '-', 2)
+ ORFI{NaiveFinder}(194:283, '-', 2)
+ ORFI{NaiveFinder}(650:727, '-', 2)

Or in a single line using another genome sequence:

julia
julia> phi = dna"GTGTGAGGTTATAACGCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACTTTTATTTCTCGCCATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCTGTTTTACAGACACCTAAAGCTACATCGTCAACGTTATATTTTGATAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCATTCAGATGGATACATCTGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCTATGATGTTTATCCTTTGAATGGTCGCCATGATGGTGGTTATTATACCGTCAAGGACTGTGTGACTATTGACGTCCTTCCCCGTACGCCGGGCAATAACGTTTATGTTGGTTTCATGGTTTGGTCTAACTTTACCGCTACTAAATGCCGCGGATTGGTTTCGCTGAATCAGGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGGCATTCAAGGTGATGTGCTTGCTACCGATAACAATACTGTAGGCATGGGTGATGCTGGTATTAAATCTGCCATTCAAGGCTCTAATGTTCCTAACCCTGATGAGGCCGCCCCTAGTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAAGGATACTCGTGATTATCTTGCTGCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCTCTGCTGGTATGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTTACTAAAATGCAACTGGACAATCAGAAAGAGATTGCCGAGATGCAAAATGAGACTCAAAAAGAGATTGCTGGCATTCAGTCGGCGACTTCACGCCAGAATACGAAAGACCAGGTATATGCACAAAATGAGATGCTTGCTTATCAACAGAAGGAGTCTACTGCTCGCGTTGCGTCTATTATGGAAAACACCAATCTTTCCAAGCAACAGCAGGTTTCCGAGATTATGCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTCAGTATTTTACCAATGACCAAATCAAAGAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAATCAGCGGTATGGCTCTTCTCATATTGGCGCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACAATTTCTGGAAAGACGGTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGGAAATAACCGTCAGGATTGACACCCTCCCAATTGTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCTGAATGTCACGCTGATTATTTTGACTTTGAGCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAGCAGATGGATAACCGCATCAAGCTCTTGGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATGGTGATATGTATGTTGACGGCCATAAGGCTGCTTCTGACGTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTTAATGGATGAATTGGCACAATGCTACAATGTGCTCCCCCAACTTGATATTAATAACACTATAGACCACCGCCCCGAAGGGGACGAAAAATGGTTTTTAGAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTCGCGATGAGTATAATTACCCCAAAAAGAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGATGAATGCAATGCGACAGGCTCATGCTGATGGTTGGTTTATCGTTTTTGACACTCTCACGTTGGCTGACGACCGATTAGAGGCGTTTTATGATAATCCCAATGCTTTGCGTGACTATTTTCGTGATATTGGTCGTATGGTTCTTGCTGCCGAGGGTCGCAAGGCTAATGATTCACACGCCGACTGCTATCAGTATTTTTGTGTGCCTGAGTATGGTACAGCTAATGGCCGTCTTCATTTCCATGCGGTGCACTTTATGCGGACACTTCCTACAGGTAGCGTTGACCCTAATTTTGGTCGTCGGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAAAATACGTGGCCTTATGGTTACAGTATGCCCATCGCAGTTCGCTACACGCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATATGGCTGTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAGTCAGATATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTCACTAAAAACCAAGCTGTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTCGGGATGAAAATGCTCACAATGACAAATCTGTCCACGGAGTGCTTAATCCAACTTACCAAGCTGGGTTACGACGCGACGCCGTTCAACCAGATATTGAAGCAGAACGCAAAAAGAGAGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGCGCAACCTGTGACGACAAATCTGCTCAAATTTATGCGCGCTTCGATAAAAATGATTGGCGTATCCAACCTGCAGAGTTTTATCGCTTCCATGACGCAGAAGTTAACACTTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGGAATTACTACTGCTTGTTTACGAATTAAATCGAAGTGGACTGCTGGCGGAAAATGAGAAAATTCGACCTATCCTTGCGCAGCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGACTGGTTTAGATATGAGTCACATTTTGTTCATGGTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGTGGATTACTATCTGAGTCCGATGCTGTTCAACCACTAATAGGTAAGAAATCATGAGTCAAGTTACTGAACAATCCGTACGTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGCCGTTTTGGATTTAACCGAAGATGATTTCGATTTTCTGACGAGTAACAAAGTTTGGATTGCTACTGACCGCTCTCGTGCTCGTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGGATACCCTCGCTTTCCTGCTCCTGTTGAGTTTATTGCTGCCGTCATTGCTTATTATGTTCATCCCGTCAACATTCAAACGGCCTGTCTCATCATGGAAGGCGCTGAATTTACGGAAAACATTATTAATGGCGTCGAGCGTCCGGTTAAAGCCGCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGCAGGAAACACTGACGTTCTTACTGACGCAGAAGAAAACGTGCGTCAAAAATTACGTGCGGAAGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGTACTAAAGGCAAGCGTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTTACTTGAGGATAAATTATGTCTAATATTCAAACTGGCGCCGAGCGTATGCCGCATGACCTTTCCCATCTTGGCTTCCTTGCTGGTCAGATTGGTCGTCTTATTACCATTTCAACTACTCCGGTTATCGCTGGCGACTCCTTCGAGATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGGCCTTGCTATTGACTCTACTGTAGACATTTTTACTTTTTATGTCCCTCATCGTCACGTTTATGGTGAACAGTGGATTAAGTTCATGAAGGATGGTGTTAATGCCACTCCTCTCCCGACTGTTAACACTACTGGTTATATTGACCATGCCGCTTTTCTTGGCACGATTAACCCTGATACCAATAAAATCCCTAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTAAAGCGCCGTGGATGCCTGACCGTACCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAAGCTGCTTATGCTAATTTGCATACTGACCAAGAACGTGATTACTTCATGCAGCGTTACCATGATGTTATTTCTTCATTTGGAGGTAAAACCTCTTATGACGCTGACAACCGTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCATCTGGCTATGATGTTGATGGAACTGACCAAACGTCGTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCTATAAACATTCTGTGCCGCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTTTGACTTATACCGATATTGCTGGCGACCCTGTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATGTGACCGTTTATCGCAATCTGCCGACCACTCGCGATTCAATCATGACTTCGTGATAAAAGATTGA"
+
+julia> filter(x -> iscoding(sequence(x), η=1e-10) && length(x) > 100, findorfs(phi))
+
+34-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(636:1622, '+', 3)
+ ORFI{NaiveFinder}(687:1622, '+', 3)
+ ORFI{NaiveFinder}(774:1622, '+', 3)
+ ORFI{NaiveFinder}(781:1389, '+', 1)
+ ORFI{NaiveFinder}(814:1389, '+', 1)
+ ORFI{NaiveFinder}(829:1389, '+', 1)
+ ORFI{NaiveFinder}(861:1622, '+', 3)
+ ORFI{NaiveFinder}(1021:1389, '+', 1)
+ ORFI{NaiveFinder}(1386:1622, '+', 3)
+ ORFI{NaiveFinder}(1447:1635, '+', 1)
+ ORFI{NaiveFinder}(1489:1635, '+', 1)
+ ORFI{NaiveFinder}(1501:1635, '+', 1)
+ ORFI{NaiveFinder}(1531:1635, '+', 1)
+ ORFI{NaiveFinder}(2697:3227, '+', 3)
+ ORFI{NaiveFinder}(2745:3227, '+', 3)
+ ⋮
+ ORFI{NaiveFinder}(2874:3227, '+', 3)
+ ORFI{NaiveFinder}(2973:3227, '+', 3)
+ ORFI{NaiveFinder}(3108:3227, '+', 3)
+ ORFI{NaiveFinder}(3142:3312, '+', 1)
+ ORFI{NaiveFinder}(3481:3939, '+', 1)
+ ORFI{NaiveFinder}(3659:3934, '+', 2)
+ ORFI{NaiveFinder}(3734:3934, '+', 2)
+ ORFI{NaiveFinder}(3772:3939, '+', 1)
+ ORFI{NaiveFinder}(3806:3934, '+', 2)
+ ORFI{NaiveFinder}(4129:4287, '+', 1)
+ ORFI{NaiveFinder}(4160:4291, '-', 2)
+ ORFI{NaiveFinder}(4540:4644, '+', 1)
+ ORFI{NaiveFinder}(4690:4866, '+', 1)
+ ORFI{NaiveFinder}(4741:4866, '+', 1)
+ ORFI{NaiveFinder}(4744:4866, '+', 1)
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The "),s("em",null,"log-odds ratio"),i(" decision rule could be establish as:")],-1)),s("mjx-container",l,[(A(),a("svg",h,T[0]||(T[0]=[t('',1)]))),T[1]||(T[1]=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mtable",{displaystyle:"true",columnalign:"right",columnspacing:"",rowspacing:"3pt"},[s("mtr",null,[s("mtd",null,[s("mi",null,"S"),s("mo",{stretchy:"false"},"("),s("mi",null,"X"),s("mo",{stretchy:"false"},")"),s("mo",null,"="),s("mi",null,"log"),s("mo",{"data-mjx-texclass":"NONE"},"⁡"),s("mfrac",null,[s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mi",null,"P"),s("mi",null,"C")]),s("mo",{stretchy:"false"},"("),s("msub",null,[s("mi",null,"X"),s("mn",null,"1")]),s("mo",null,"="),s("msub",null,[s("mi",null,"i"),s("mn",null,"1")]),s("mo",null,","),s("mo",null,"…"),s("mo",null,","),s("msub",null,[s("mi",null,"X"),s("mi",null,"T")]),s("mo",null,"="),s("msub",null,[s("mi",null,"i"),s("mi",null,"T")]),s("mo",{stretchy:"false"},")")]),s("mrow",{"data-mjx-texclass":"ORD"},[s("msub",null,[s("mi",null,"P"),s("mi",null,"N")]),s("mo",{stretchy:"false"},"("),s("msub",null,[s("mi",null,"X"),s("mn",null,"1")]),s("mo",null,"="),s("msub",null,[s("mi",null,"i"),s("mn",null,"1")]),s("mo",null,","),s("mo",null,"…"),s("mo",null,","),s("msub",null,[s("mi",null,"X"),s("mi",null,"T")]),s("mo",null,"="),s("msub",null,[s("mi",null,"i"),s("mi",null,"T")]),s("mo",{stretchy:"false"},")")])]),s("mrow",{"data-mjx-texclass":"INNER"},[s("mo",{"data-mjx-texclass":"OPEN"},"{"),s("mtable",{columnalign:"left left",columnspacing:"1em",rowspacing:".2em"},[s("mtr",null,[s("mtd",null,[s("mo",null,">"),s("mi",null,"η")]),s("mtd",null,[s("mo",{stretchy:"false"},"⇒"),s("mtext",null,"coding")])]),s("mtr",null,[s("mtd",null,[s("mo",null,"<"),s("mi",null,"η")]),s("mtd",null,[s("mo",{stretchy:"false"},"⇒"),s("mtext",null,"noncoding")])])]),s("mo",{"data-mjx-texclass":"CLOSE",fence:"true",stretchy:"true",symmetric:"true"})])])])])])],-1))]),s("p",null,[T[6]||(T[6]=i("Where the ")),s("mjx-container",G,[(A(),a("svg",k,T[2]||(T[2]=[t('',1)]))),T[3]||(T[3]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"P"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"C")])])])],-1))]),T[7]||(T[7]=i(" is the probability of the sequence given a CDS model, ")),s("mjx-container",e,[(A(),a("svg",p,T[4]||(T[4]=[t('',1)]))),T[5]||(T[5]=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("msub",null,[s("mi",null,"P"),s("mrow",{"data-mjx-texclass":"ORD"},[s("mi",null,"N")])])])],-1))]),T[8]||(T[8]=i(" is the probability of the sequence given a No-CDS model, the decision rule is finally based on whether the ratio is greater or lesser than a given threshold ")),T[9]||(T[9]=s("em",null,"η",-1)),T[10]||(T[10]=i(" of significance level."))]),T[13]||(T[13]=t(`

In this package we have implemented this rule and call some basic models of CDS and No-CDS of E. coli from Axelson-Fisk (2015) work (implemented in BioMarkovChains.jl package). To check whether a random sequence could be coding based on these decision we use the predicate log_odds_ratio_decision_rule with the ECOLICDS and ECOLINOCDS models:

julia
julia> using GeneFinder, BioSequences
+
+seq = dna"TTCGTCAGTCGTTCTGTTTCATTCAATACGATAGTAATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCCAGCATAGCCTCCAGATATCCCCTTATAGAGGTCAGAGGGGAACGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGATCATCCTCGGAACGGGCAATTTCGCTTGCCATATAATTCAGACAGGAAGCCAGATAACCGATTTCATCCTCACTATCGACCTGAAATTCATAATGCATATTACCGGCAGCATACTGCTCTGTGGCATGAGTGATCTTCCTCAGAGGAATATATACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAGCAGGAACAGGATTGCCAGGGTGATATAGGAAATATTCAGCAGGTTGTTACAGGATTTCTGAATATCATTCATATCAGTATGGATGACTACATAGCCTTTTACCTTGTAGTTGGAGGTAATGGGAGCAAATACAGTAAGTACATCCGAATCAAAATTACCGAAGAAATCACCAACAATGTAATAGGAGCCGCTGGTTACGGTCGAATCAAAATTCTCAATGACAACCACATTCTCCACATCTAAGGGACTATTGGTATCCAGTACCAGTCGTCCGGAGGGATTGATGATGCGAATCTCGGAATTCAGGTAGACCGCCAGGGAGTCCAGCTGCATTTTAACGGTCTCCAAAGTTGTTTCACTGGTGTACAATCCGCCGGCATAGGTTCCGGCGATCAGGGTTGCTTCGGAATAGAGACTTTCTGCCTTTTCCCGGATCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAACAATGATGAAACCAAATACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAAAGTGTTTTTTTCATAACAAATCCTGCTTTTGGTATGACTTAATTACGTACTTCGAATTTATAGCCGATGCCCCAGATGGTGCTGATCTTCCAGTTGGCATGATCCTTGATCTTCTC"
+
+orfsdna = findorfs(seq, finder=NaiveFinder, minlen=75, alternative_start=true) .|> sequence
+
+20-element Vector{NucSeq{4, DNAAlphabet{4}}}
+ ATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCC…CGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGA
+ GTGCATTTCCGGTGGAATCGTGCCGTCCAGCATAGCCTC…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ GTGCCGTCCAGCATAGCCTCCAGATATCCCCTTATAGAG…CGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGA
+ GTGGGATACATTGGCTACAAACTTTTTCTGATCATCCTC…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ TTGCCATATAATTCAGACAGGAAGCCAGATAACCGATTT…GCATATTACCGGCAGCATACTGCTCTGTGGCATGAGTGA
+ ATGCTGCCGGTAATATGCATTATGAATTTCAGGTCGATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCACAGAGCAGTATGCTGCCGGTAATATGCATTATG…ATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCATATTACCGGCAGCATACTGCTCTGTGGCATGAGT…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ GTGATCTTCCTCAGAGGAATATATACGATCTCAGTGAAA…ATCAGGGATAGCAGGAACAGGATTGCCAGGGTGATATAG
+ ATGGATGACTACATAGCCTTTTACCTTGTAGTTGGAGGT…ATCAAAATTCTCAATGACAACCACATTCTCCACATCTAA
+ TTGGTGATTTCTTCGGTAATTTTGATTCGGATGTACTTACTGTATTTGCTCCCATTACCTCCAACTACAAGGTAA
+ TTGTTGGTGATTTCTTCGGTAATTTTGATTCGGATGTACTTACTGTATTTGCTCCCATTACCTCCAACTACAAGGTAA
+ ATGACAACCACATTCTCCACATCTAAGGGACTATTGGTA…CCGGAGGGATTGATGATGCGAATCTCGGAATTCAGGTAG
+ ATGCCGGCGGATTGTACACCAGTGAAACAACTTTGGAGACCGTTAAAATGCAGCTGGACTCCCTGGCGGTCTACCTGA
+ TTGTTTCACTGGTGTACAATCCGCCGGCATAGGTTCCGG…TCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAA
+ TTGCTTCGGAATAGAGACTTTCTGCCTTTTCCCGGATCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAA
+ ATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAACAAT…AAATACACCAAAAATAAAATATGCGAGTATAAATTTTAG
+ TTGGTCATATTGGGAACAAAAGTTGTAACAATGATGAAA…ACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAA
+ TTGGGAACAAAAGTTGTAACAATGATGAAACCAAATACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAA
+ ATGCCAACTGGAAGATCAGCACCATCTGGGGCATCGGCT…TACGTAATTAAGTCATACCAAAAGCAGGATTTGTTATGA

Now the question is which of those sequences can we consider as coding sequences. We can use the iscoding predicate to check whether a sequence is coding or not based on the log-odds ratio decision rule:

julia
julia> iscoding.(orfsdna) # criteria = log_odds_ratio_decision_rule
+
+20-element BitVector:
+ 0
+ 0
+ 0
+ 0
+ 0
+ 1
+ 1
+ 0
+ 0
+ 0
+ 0
+ 0
+ 0
+ 1
+ 0
+ 0
+ 0
+ 0
+ 0
+ 0

In this case, the sequence has 20 ORFIs and only 3 of them are classified as coding sequences. The classification is based on the log-odds ratio decision rule and the transition probability models of E. coli CDS and No-CDS. The log_odds_ratio_decision_rule method will return a boolean vector with the classification of each ORFI in the sequence. Now we can simply filter the ORFIs that are coding sequences:

julia
orfs = filter(orf -> iscoding(orf), orfsdna)
+
+3-element Vector{NucSeq{4, DNAAlphabet{4}}}
+ ATGCTGCCGGTAATATGCATTATGAATTTCAGGTCGATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCACAGAGCAGTATGCTGCCGGTAATATGCATTATG…ATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCGGCGGATTGTACACCAGTGAAACAACTTTGGAGACCGTTAAAATGCAGCTGGACTCCCTGGCGGTCTACCTGA

Or in terms of the ORFI object:

julia
julia> orfs = findorfs(seq, minlen=75, finder=NaiveFinder, alternative_start=true) # find ORFIs with alternative start as well
+julia> orfs[iscoding.(orfsdna)]
+
+3-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(194:268, '-', 2)
+ ORFI{NaiveFinder}(194:283, '-', 2)
+ ORFI{NaiveFinder}(650:727, '-', 2)

Or in a single line using another genome sequence:

julia
julia> phi = dna"GTGTGAGGTTATAACGCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACTTTTATTTCTCGCCATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCTGTTTTACAGACACCTAAAGCTACATCGTCAACGTTATATTTTGATAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCATTCAGATGGATACATCTGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCTATGATGTTTATCCTTTGAATGGTCGCCATGATGGTGGTTATTATACCGTCAAGGACTGTGTGACTATTGACGTCCTTCCCCGTACGCCGGGCAATAACGTTTATGTTGGTTTCATGGTTTGGTCTAACTTTACCGCTACTAAATGCCGCGGATTGGTTTCGCTGAATCAGGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGGCATTCAAGGTGATGTGCTTGCTACCGATAACAATACTGTAGGCATGGGTGATGCTGGTATTAAATCTGCCATTCAAGGCTCTAATGTTCCTAACCCTGATGAGGCCGCCCCTAGTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAAGGATACTCGTGATTATCTTGCTGCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCTCTGCTGGTATGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTTACTAAAATGCAACTGGACAATCAGAAAGAGATTGCCGAGATGCAAAATGAGACTCAAAAAGAGATTGCTGGCATTCAGTCGGCGACTTCACGCCAGAATACGAAAGACCAGGTATATGCACAAAATGAGATGCTTGCTTATCAACAGAAGGAGTCTACTGCTCGCGTTGCGTCTATTATGGAAAACACCAATCTTTCCAAGCAACAGCAGGTTTCCGAGATTATGCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTCAGTATTTTACCAATGACCAAATCAAAGAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAATCAGCGGTATGGCTCTTCTCATATTGGCGCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACAATTTCTGGAAAGACGGTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGGAAATAACCGTCAGGATTGACACCCTCCCAATTGTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCTGAATGTCACGCTGATTATTTTGACTTTGAGCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAGCAGATGGATAACCGCATCAAGCTCTTGGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATGGTGATATGTATGTTGACGGCCATAAGGCTGCTTCTGACGTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTTAATGGATGAATTGGCACAATGCTACAATGTGCTCCCCCAACTTGATATTAATAACACTATAGACCACCGCCCCGAAGGGGACGAAAAATGGTTTTTAGAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTCGCGATGAGTATAATTACCCCAAAAAGAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGATGAATGCAATGCGACAGGCTCATGCTGATGGTTGGTTTATCGTTTTTGACACTCTCACGTTGGCTGACGACCGATTAGAGGCGTTTTATGATAATCCCAATGCTTTGCGTGACTATTTTCGTGATATTGGTCGTATGGTTCTTGCTGCCGAGGGTCGCAAGGCTAATGATTCACACGCCGACTGCTATCAGTATTTTTGTGTGCCTGAGTATGGTACAGCTAATGGCCGTCTTCATTTCCATGCGGTGCACTTTATGCGGACACTTCCTACAGGTAGCGTTGACCCTAATTTTGGTCGTCGGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAAAATACGTGGCCTTATGGTTACAGTATGCCCATCGCAGTTCGCTACACGCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATATGGCTGTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAGTCAGATATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTCACTAAAAACCAAGCTGTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTCGGGATGAAAATGCTCACAATGACAAATCTGTCCACGGAGTGCTTAATCCAACTTACCAAGCTGGGTTACGACGCGACGCCGTTCAACCAGATATTGAAGCAGAACGCAAAAAGAGAGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGCGCAACCTGTGACGACAAATCTGCTCAAATTTATGCGCGCTTCGATAAAAATGATTGGCGTATCCAACCTGCAGAGTTTTATCGCTTCCATGACGCAGAAGTTAACACTTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGGAATTACTACTGCTTGTTTACGAATTAAATCGAAGTGGACTGCTGGCGGAAAATGAGAAAATTCGACCTATCCTTGCGCAGCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGACTGGTTTAGATATGAGTCACATTTTGTTCATGGTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGTGGATTACTATCTGAGTCCGATGCTGTTCAACCACTAATAGGTAAGAAATCATGAGTCAAGTTACTGAACAATCCGTACGTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGCCGTTTTGGATTTAACCGAAGATGATTTCGATTTTCTGACGAGTAACAAAGTTTGGATTGCTACTGACCGCTCTCGTGCTCGTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGGATACCCTCGCTTTCCTGCTCCTGTTGAGTTTATTGCTGCCGTCATTGCTTATTATGTTCATCCCGTCAACATTCAAACGGCCTGTCTCATCATGGAAGGCGCTGAATTTACGGAAAACATTATTAATGGCGTCGAGCGTCCGGTTAAAGCCGCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGCAGGAAACACTGACGTTCTTACTGACGCAGAAGAAAACGTGCGTCAAAAATTACGTGCGGAAGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGTACTAAAGGCAAGCGTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTTACTTGAGGATAAATTATGTCTAATATTCAAACTGGCGCCGAGCGTATGCCGCATGACCTTTCCCATCTTGGCTTCCTTGCTGGTCAGATTGGTCGTCTTATTACCATTTCAACTACTCCGGTTATCGCTGGCGACTCCTTCGAGATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGGCCTTGCTATTGACTCTACTGTAGACATTTTTACTTTTTATGTCCCTCATCGTCACGTTTATGGTGAACAGTGGATTAAGTTCATGAAGGATGGTGTTAATGCCACTCCTCTCCCGACTGTTAACACTACTGGTTATATTGACCATGCCGCTTTTCTTGGCACGATTAACCCTGATACCAATAAAATCCCTAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTAAAGCGCCGTGGATGCCTGACCGTACCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAAGCTGCTTATGCTAATTTGCATACTGACCAAGAACGTGATTACTTCATGCAGCGTTACCATGATGTTATTTCTTCATTTGGAGGTAAAACCTCTTATGACGCTGACAACCGTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCATCTGGCTATGATGTTGATGGAACTGACCAAACGTCGTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCTATAAACATTCTGTGCCGCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTTTGACTTATACCGATATTGCTGGCGACCCTGTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATGTGACCGTTTATCGCAATCTGCCGACCACTCGCGATTCAATCATGACTTCGTGATAAAAGATTGA"
+
+julia> filter(x -> iscoding(sequence(x), η=1e-10) && length(x) > 100, findorfs(phi))
+
+34-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(636:1622, '+', 3)
+ ORFI{NaiveFinder}(687:1622, '+', 3)
+ ORFI{NaiveFinder}(774:1622, '+', 3)
+ ORFI{NaiveFinder}(781:1389, '+', 1)
+ ORFI{NaiveFinder}(814:1389, '+', 1)
+ ORFI{NaiveFinder}(829:1389, '+', 1)
+ ORFI{NaiveFinder}(861:1622, '+', 3)
+ ORFI{NaiveFinder}(1021:1389, '+', 1)
+ ORFI{NaiveFinder}(1386:1622, '+', 3)
+ ORFI{NaiveFinder}(1447:1635, '+', 1)
+ ORFI{NaiveFinder}(1489:1635, '+', 1)
+ ORFI{NaiveFinder}(1501:1635, '+', 1)
+ ORFI{NaiveFinder}(1531:1635, '+', 1)
+ ORFI{NaiveFinder}(2697:3227, '+', 3)
+ ORFI{NaiveFinder}(2745:3227, '+', 3)
+ ⋮
+ ORFI{NaiveFinder}(2874:3227, '+', 3)
+ ORFI{NaiveFinder}(2973:3227, '+', 3)
+ ORFI{NaiveFinder}(3108:3227, '+', 3)
+ ORFI{NaiveFinder}(3142:3312, '+', 1)
+ ORFI{NaiveFinder}(3481:3939, '+', 1)
+ ORFI{NaiveFinder}(3659:3934, '+', 2)
+ ORFI{NaiveFinder}(3734:3934, '+', 2)
+ ORFI{NaiveFinder}(3772:3939, '+', 1)
+ ORFI{NaiveFinder}(3806:3934, '+', 2)
+ ORFI{NaiveFinder}(4129:4287, '+', 1)
+ ORFI{NaiveFinder}(4160:4291, '-', 2)
+ ORFI{NaiveFinder}(4540:4644, '+', 1)
+ ORFI{NaiveFinder}(4690:4866, '+', 1)
+ ORFI{NaiveFinder}(4741:4866, '+', 1)
+ ORFI{NaiveFinder}(4744:4866, '+', 1)
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var(--vp-c-success-1);--vp-code-line-diff-remove-color: var(--vp-c-danger-soft);--vp-code-line-diff-remove-symbol-color: var(--vp-c-danger-1);--vp-code-line-warning-color: var(--vp-c-warning-soft);--vp-code-line-error-color: var(--vp-c-danger-soft);--vp-code-copy-code-border-color: var(--vp-c-divider);--vp-code-copy-code-bg: var(--vp-c-bg-soft);--vp-code-copy-code-hover-border-color: var(--vp-c-divider);--vp-code-copy-code-hover-bg: var(--vp-c-bg);--vp-code-copy-code-active-text: var(--vp-c-text-2);--vp-code-copy-copied-text-content: "Copied";--vp-code-tab-divider: var(--vp-code-block-divider-color);--vp-code-tab-text-color: var(--vp-c-text-2);--vp-code-tab-bg: var(--vp-code-block-bg);--vp-code-tab-hover-text-color: var(--vp-c-text-1);--vp-code-tab-active-text-color: var(--vp-c-text-1);--vp-code-tab-active-bar-color: var(--vp-c-brand-1)}:root{--vp-button-brand-border: transparent;--vp-button-brand-text: var(--vp-c-white);--vp-button-brand-bg: var(--vp-c-brand-3);--vp-button-brand-hover-border: transparent;--vp-button-brand-hover-text: var(--vp-c-white);--vp-button-brand-hover-bg: var(--vp-c-brand-2);--vp-button-brand-active-border: transparent;--vp-button-brand-active-text: var(--vp-c-white);--vp-button-brand-active-bg: var(--vp-c-brand-1);--vp-button-alt-border: transparent;--vp-button-alt-text: var(--vp-c-text-1);--vp-button-alt-bg: var(--vp-c-default-3);--vp-button-alt-hover-border: transparent;--vp-button-alt-hover-text: var(--vp-c-text-1);--vp-button-alt-hover-bg: var(--vp-c-default-2);--vp-button-alt-active-border: transparent;--vp-button-alt-active-text: var(--vp-c-text-1);--vp-button-alt-active-bg: var(--vp-c-default-1);--vp-button-sponsor-border: var(--vp-c-text-2);--vp-button-sponsor-text: var(--vp-c-text-2);--vp-button-sponsor-bg: transparent;--vp-button-sponsor-hover-border: var(--vp-c-sponsor);--vp-button-sponsor-hover-text: var(--vp-c-sponsor);--vp-button-sponsor-hover-bg: 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GeneFinder.jl

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Scoring ORFs ​

The ORFI type is designed to be flexible and can store various types of information about ORFs. A very neat feature is that stores a view of the sequence that the ORF represents. Since the the ORFI sequence is stored in the struct, then any method that can be applied to a sequence can be applied to the ORF sequence. This is useful for scoring the ORFs by overloading a method that calculates a score for a BioSequence. For instance the lors function from the BioMarkovChains.jl package can be used to calculate a score of the ORFs predicted for the phi genome.

Take the following example:

julia
phi = dna"GTGTGAGGTTATAACGCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACTTTTATTTCTCGCCATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCTGTTTTACAGACACCTAAAGCTACATCGTCAACGTTATATTTTGATAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCATTCAGATGGATACATCTGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCTATGATGTTTATCCTTTGAATGGTCGCCATGATGGTGGTTATTATACCGTCAAGGACTGTGTGACTATTGACGTCCTTCCCCGTACGCCGGGCAATAACGTTTATGTTGGTTTCATGGTTTGGTCTAACTTTACCGCTACTAAATGCCGCGGATTGGTTTCGCTGAATCAGGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGGCATTCAAGGTGATGTGCTTGCTACCGATAACAATACTGTAGGCATGGGTGATGCTGGTATTAAATCTGCCATTCAAGGCTCTAATGTTCCTAACCCTGATGAGGCCGCCCCTAGTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAAGGATACTCGTGATTATCTTGCTGCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCTCTGCTGGTATGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTTACTAAAATGCAACTGGACAATCAGAAAGAGATTGCCGAGATGCAAAATGAGACTCAAAAAGAGATTGCTGGCATTCAGTCGGCGACTTCACGCCAGAATACGAAAGACCAGGTATATGCACAAAATGAGATGCTTGCTTATCAACAGAAGGAGTCTACTGCTCGCGTTGCGTCTATTATGGAAAACACCAATCTTTCCAAGCAACAGCAGGTTTCCGAGATTATGCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTCAGTATTTTACCAATGACCAAATCAAAGAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAATCAGCGGTATGGCTCTTCTCATATTGGCGCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACAATTTCTGGAAAGACGGTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGGAAATAACCGTCAGGATTGACACCCTCCCAATTGTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCTGAATGTCACGCTGATTATTTTGACTTTGAGCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAGCAGATGGATAACCGCATCAAGCTCTTGGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATGGTGATATGTATGTTGACGGCCATAAGGCTGCTTCTGACGTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTTAATGGATGAATTGGCACAATGCTACAATGTGCTCCCCCAACTTGATATTAATAACACTATAGACCACCGCCCCGAAGGGGACGAAAAATGGTTTTTAGAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTCGCGATGAGTATAATTACCCCAAAAAGAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGATGAATGCAATGCGACAGGCTCATGCTGATGGTTGGTTTATCGTTTTTGACACTCTCACGTTGGCTGACGACCGATTAGAGGCGTTTTATGATAATCCCAATGCTTTGCGTGACTATTTTCGTGATATTGGTCGTATGGTTCTTGCTGCCGAGGGTCGCAAGGCTAATGATTCACACGCCGACTGCTATCAGTATTTTTGTGTGCCTGAGTATGGTACAGCTAATGGCCGTCTTCATTTCCATGCGGTGCACTTTATGCGGACACTTCCTACAGGTAGCGTTGACCCTAATTTTGGTCGTCGGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAAAATACGTGGCCTTATGGTTACAGTATGCCCATCGCAGTTCGCTACACGCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATATGGCTGTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAGTCAGATATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTCACTAAAAACCAAGCTGTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTCGGGATGAAAATGCTCACAATGACAAATCTGTCCACGGAGTGCTTAATCCAACTTACCAAGCTGGGTTACGACGCGACGCCGTTCAACCAGATATTGAAGCAGAACGCAAAAAGAGAGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGCGCAACCTGTGACGACAAATCTGCTCAAATTTATGCGCGCTTCGATAAAAATGATTGGCGTATCCAACCTGCAGAGTTTTATCGCTTCCATGACGCAGAAGTTAACACTTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGGAATTACTACTGCTTGTTTACGAATTAAATCGAAGTGGACTGCTGGCGGAAAATGAGAAAATTCGACCTATCCTTGCGCAGCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGACTGGTTTAGATATGAGTCACATTTTGTTCATGGTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGTGGATTACTATCTGAGTCCGATGCTGTTCAACCACTAATAGGTAAGAAATCATGAGTCAAGTTACTGAACAATCCGTACGTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGCCGTTTTGGATTTAACCGAAGATGATTTCGATTTTCTGACGAGTAACAAAGTTTGGATTGCTACTGACCGCTCTCGTGCTCGTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGGATACCCTCGCTTTCCTGCTCCTGTTGAGTTTATTGCTGCCGTCATTGCTTATTATGTTCATCCCGTCAACATTCAAACGGCCTGTCTCATCATGGAAGGCGCTGAATTTACGGAAAACATTATTAATGGCGTCGAGCGTCCGGTTAAAGCCGCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGCAGGAAACACTGACGTTCTTACTGACGCAGAAGAAAACGTGCGTCAAAAATTACGTGCGGAAGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGTACTAAAGGCAAGCGTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTTACTTGAGGATAAATTATGTCTAATATTCAAACTGGCGCCGAGCGTATGCCGCATGACCTTTCCCATCTTGGCTTCCTTGCTGGTCAGATTGGTCGTCTTATTACCATTTCAACTACTCCGGTTATCGCTGGCGACTCCTTCGAGATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGGCCTTGCTATTGACTCTACTGTAGACATTTTTACTTTTTATGTCCCTCATCGTCACGTTTATGGTGAACAGTGGATTAAGTTCATGAAGGATGGTGTTAATGCCACTCCTCTCCCGACTGTTAACACTACTGGTTATATTGACCATGCCGCTTTTCTTGGCACGATTAACCCTGATACCAATAAAATCCCTAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTAAAGCGCCGTGGATGCCTGACCGTACCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAAGCTGCTTATGCTAATTTGCATACTGACCAAGAACGTGATTACTTCATGCAGCGTTACCATGATGTTATTTCTTCATTTGGAGGTAAAACCTCTTATGACGCTGACAACCGTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCATCTGGCTATGATGTTGATGGAACTGACCAAACGTCGTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCTATAAACATTCTGTGCCGCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTTTGACTTATACCGATATTGCTGGCGACCCTGTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATGTGACCGTTTATCGCAATCTGCCGACCACTCGCGATTCAATCATGACTTCGTGATAAAAGATTGA"
+
+phiorfs = findorfs(phi, finder=NaiveFinder, minlen=75)
+
+124-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(9:101, '-', 3)
+ ORFI{NaiveFinder}(100:627, '+', 1)
+ ORFI{NaiveFinder}(223:447, '-', 1)
+ ORFI{NaiveFinder}(248:436, '+', 2)
+ ORFI{NaiveFinder}(257:436, '+', 2)
+ ORFI{NaiveFinder}(283:627, '+', 1)
+ ORFI{NaiveFinder}(344:436, '+', 2)
+ ORFI{NaiveFinder}(532:627, '+', 1)
+ ORFI{NaiveFinder}(636:1622, '+', 3)
+ ORFI{NaiveFinder}(687:1622, '+', 3)
+ ORFI{NaiveFinder}(774:1622, '+', 3)
+ ORFI{NaiveFinder}(781:1389, '+', 1)
+ ORFI{NaiveFinder}(814:1389, '+', 1)
+ ORFI{NaiveFinder}(829:1389, '+', 1)
+ ORFI{NaiveFinder}(861:1622, '+', 3)
+ ⋮
+ ORFI{NaiveFinder}(4671:5375, '+', 3)
+ ORFI{NaiveFinder}(4690:4866, '+', 1)
+ ORFI{NaiveFinder}(4728:5375, '+', 3)
+ ORFI{NaiveFinder}(4741:4866, '+', 1)
+ ORFI{NaiveFinder}(4744:4866, '+', 1)
+ ORFI{NaiveFinder}(4777:4866, '+', 1)
+ ORFI{NaiveFinder}(4806:5375, '+', 3)
+ ORFI{NaiveFinder}(4863:5258, '-', 3)
+ ORFI{NaiveFinder}(4933:5019, '+', 1)
+ ORFI{NaiveFinder}(4941:5375, '+', 3)
+ ORFI{NaiveFinder}(5082:5375, '+', 3)
+ ORFI{NaiveFinder}(5089:5325, '+', 1)
+ ORFI{NaiveFinder}(5122:5202, '-', 1)
+ ORFI{NaiveFinder}(5152:5325, '+', 1)
+ ORFI{NaiveFinder}(5164:5325, '+', 1)

We can now calculate a score using the lors (logg_odds_ratio_score) scoring scheme (see lors from the BioMarkovChains.jl package).

julia
lors.(phiorfs)
+
+204-element Vector{Float64}:
+  -3.002461366087374
+   0.1063473191426695
+ -10.814621287968222
+   0.0015932011596656963
+  -2.635307521118871
+  -5.344187934894264
+  -1.35785984913167
+  -1.316724559874126
+  -1.796631200562138
+   0.4895532892313278
+  -3.2651518608269856
+  -1.4019264441082822
+  -0.32264884956233186
+  -0.6695213802037128
+   1.1023617306499074
+   ⋮
+  -5.40624241726446
+  -0.8080572222081075
+  -5.571494087742448
+   0.8307089386780881
+  -4.882156920421228
+  -5.639670353834974
+  -0.8764121443326865
+  -4.308687693802273
+  -4.459423419810693
+   0.5077309777574499
+  -2.2808971022330824
+  -2.138892671183213
+   0.6106494455192994
+   1.1981063545591812
+   0.7566845754226063

Briefly, a sequence of DNA could be scored using a Markov model of the transition probabilities of a known sequence. This could be done using a log-odds ratio score, which is the logarithm of the ratio of the transition probabilities of the sequence given two models. The log-odds ratio score is defined as:

S(x)=∑i=1Lβxix=∑i=1log⁡ai−1m1xiai−1m2xi

Where the ai−1m1xi is the transition probability of the first model (in this case the calculated for the given sequence) from the state xi−1 to the state xi and ai−1m2xi is the transition probability of the second model from the state xi−1 to the state xi. The score is the sum of the log-odds ratio of the transition probabilities of the sequence given the two models.

In the lors case, the two models are the coding and non-coding models of the E. coli genome. The coding model is a Markov model of the transition probabilities of the coding regions of the E. coli genome, and the non-coding model is a Markov model of the transition probabilities of the non-coding regions of the E. coli genome.

Analysing Lambda ORFs ​

As mentioned above the lors calculates the log odds ratio of the ORFI sequence given two Markov models (by default: ECOLICDS and ECOLINOCDS), one for the coding region and one for the non-coding region. By default the lors function return the base 2 logarithm of the odds ratio, so it is analogous to the bits of information that the ORFI sequence is coding.

Now we can even analyse how is the distribution of the ORFIs' scores as a function of their lengths compared to random sequences.

julia
using FASTX, CairoMakie
+
+lambdafile = "test/data/NC_001416.1.fasta"
+
+# read the lambda genome as a `BioSequence`
+open(FASTA.Reader, lambdafile) do reader
+    lambdaseq = FASTX.sequence(LongDNA{4}, collect(reader)[1])
+end
+
+# find the ORFs in the lambda genome
+lambaorfs = findorfs(lambdaseq, finder=NaiveFinder, minlen=100)
+
+lambdascores = lors.(lambaorfs)
+lambdalengths = length.(lambaorfs)
+
+## get some random sequences of variable lengths
+vseqs = LongDNA[]
+for i in 1:708
+    push!(vseqs, randdnaseq(rand(100:2500)))
+end
+
+## get the lengths and scores of the random generated sequences
+randlengths = length.(vseqs)
+randscores = lors.(vseqs)
+
+## plot the scores as a function of the lengths
+f = Figure()
+ax = Axis(f[1, 1], xlabel="Length", ylabel="Log-odds ratio (Bits)")
+
+scatter!(ax,
+    randlengths,
+    randscores,
+    marker = :circle, 
+    markersize = 6, 
+    color = :black, 
+    label = "Random sequences"
+)
+scatter!(ax,
+    lambdalengths, 
+    lambdascores, 
+    marker = :rect, 
+    markersize = 6, 
+    color = :blue, 
+    label = "Lambda ORFs"
+)
+
+axislegend(ax)
+
+f

What this plot shows is that the ORFs in the lambda genome have a higher scores than random sequences of the same length. The score is a measure of how likely a sequence given the coding model is compared to the non-coding model. In other words, the higher the score the more likely the sequence is coding. So, the plot shows that the ORFs in the lambda genome are more likely to be coding regions than random sequences. It also shows that the longer the ORFI the higher the score, which is expected since longer ORFs are more likely to be coding regions than shorter ones.

+ + + + \ No newline at end of file diff --git a/dev/getstarted.html b/dev/getstarted.html new file mode 100644 index 0000000..27eb492 --- /dev/null +++ b/dev/getstarted.html @@ -0,0 +1,71 @@ + + + + + + GeneFinder.jl + + + + + + + + + + + + + +
GeneFinder.jl

Appearance

Finding complete and overlapped ORFIs ​

The main function in the GeneFinder package is findorfs, which serves as an interface to various gene-finding algorithms. By default, findorfs uses a NaiveFinder algorithm, a simple approach that detects all non-outbounded Open Reading Frames (ORFs) in a DNA sequence. You can also specify a different algorithm by setting the finder keyword argument. For more details on the NaiveFinder algorithm, see NaiveFinder documentation for more details.

Note

The minlen keyword argument in NaiveFinder is set to a minimum length of 6 nucleotides (nt). As a result, it may identify short ORFs that aren't necessarily genes, such as dna"ATGTGA" producing the amino acid sequence aa"M*".

Usage example ​

Here's an example of using findorfs with the NaiveFinder algorithm to identify ORFs in a DNA sequence:

julia
julia> using BioSequences, GeneFinder
+
+# > 180195.SAMN03785337.LFLS01000089 -> finds only 1 gene in Prodigal (from Pyrodigal tests)
+seq = dna"AACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAACAGCACTGGCAATCTGACTGTGGGCGGTGTTACCAACGGCACTGCTACTACTGGCAACATCGCACTGACCGGTAACAATGCGCTGAGCGGTCCGGTCAATCTGAATGCGTCGAATGGCACGGTGACCTTGAACACGACCGGCAATACCACGCTCGGTAACGTGACGGCACAAGGCAATGTGACGACCAATGTGTCCAACGGCAGTCTGACGGTTACCGGCAATACGACAGGTGCCAACACCAACCTCAGTGCCAGCGGCAACCTGACCGTGGGTAACCAGGGCAATATCAGTACCGCAGGCAATGCAACCCTGACGGCCGGCGACAACCTGACGAGCACTGGCAATCTGACTGTGGGCGGCGTCACCAACGGCACGGCCACCACCGGCAACATCGCGCTGACCGGTAACAATGCACTGGCTGGTCCTGTCAATCTGAACGCGCCGAACGGCACCGTGACCCTGAACACAACCGGCAATACCACGCTGGGTAATGTCACCGCACAAGGCAATGTGACGACTAATGTGTCCAACGGCAGCCTGACAGTCGCTGGCAATACCACAGGTGCCAACACCAACCTGAGTGCCAGCGGCAATCTGACCGTGGGCAACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAGC"
+
+orfs = findorfs(seq, finder=NaiveFinder) # use finder=NaiveCollector as an alternative
+
+12-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(29:40, '+', 2)
+ ORFI{NaiveFinder}(137:145, '+', 2)
+ ORFI{NaiveFinder}(164:184, '+', 2)
+ ORFI{NaiveFinder}(173:184, '+', 2)
+ ORFI{NaiveFinder}(236:241, '+', 2)
+ ORFI{NaiveFinder}(248:268, '+', 2)
+ ORFI{NaiveFinder}(362:373, '+', 2)
+ ORFI{NaiveFinder}(470:496, '+', 2)
+ ORFI{NaiveFinder}(551:574, '+', 2)
+ ORFI{NaiveFinder}(569:574, '+', 2)
+ ORFI{NaiveFinder}(581:601, '+', 2)
+ ORFI{NaiveFinder}(695:706, '+', 2)

Extracting Sequences from ORFIs ​

The ORFI structure displays the location, frame, and strand, but currently does not include the sequence per se. To extract the sequence of an ORFI instance, you can use the sequence method directly on it, or you can also broadcast it over the orfs collection using the dot syntax .:

julia
julia> sequence.(orfs)
+
+12-element Vector{LongSubSeq{DNAAlphabet{4}}}:
+ ATGCAACCCTGA
+ ATGCGCTGA
+ ATGCGTCGAATGGCACGGTGA
+ ATGGCACGGTGA
+ ATGTGA
+ ATGTGTCCAACGGCAGTCTGA
+ ATGCAACCCTGA
+ ATGCACTGGCTGGTCCTGTCAATCTGA
+ ATGTCACCGCACAAGGCAATGTGA
+ ATGTGA
+ ATGTGTCCAACGGCAGCCTGA
+ ATGCAACCCTGA

Translating ORFIs to Amino Acid Sequences ​

Similarly, you can extract the amino acid sequences of the ORFIs using the translate function.

julia
julia> translate.(orfs)
+
+12-element Vector{LongAA}:
+ MQP*
+ MR*
+ MRRMAR*
+ MAR*
+ M*
+ MCPTAV*
+ MQP*
+ MHWLVLSI*
+ MSPHKAM*
+ M*
+ MCPTAA*
+ MQP*

This returns a vector of translated amino acid sequences, allowing for easy interpretation of each ORF's potential protein product.

+ + + + \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json new file mode 100644 index 0000000..14e0e38 --- /dev/null +++ b/dev/hashmap.json @@ -0,0 +1 @@ +{"api.md":"BVKEWZkd","features.md":"Cy11UbG5","getstarted.md":"eqfmZk8K","index.md":"DFDvIJs_","iodocs.md":"CF-VJtx2","orftype.md":"BFnL661u","roadmap.md":"D8FYvId5","simplecodingrule.md":"BGGoeOYF"} diff --git a/dev/index.html b/dev/index.html new file mode 100644 index 0000000..f572ace --- /dev/null +++ b/dev/index.html @@ -0,0 +1,24 @@ + + + + + + GeneFinder.jl + + + + + + + + + + + + + +
GeneFinder.jl

Appearance

GeneFinder.jl

A Gene Finding Framework for the Julia Programming Language

Get Started
View on Github
GeneFinder

Install GeneFinder from the julia REPL. Press ] to enter pkg mode, and enter the add GeneFinder command.

julia
(@v1.11) pkg> add GeneFinder
+ + + + \ No newline at end of file diff --git a/dev/iodocs.html b/dev/iodocs.html new file mode 100644 index 0000000..c7c1c5a --- /dev/null +++ b/dev/iodocs.html @@ -0,0 +1,56 @@ + + + + + + GeneFinder.jl + + + + + + + + + + + + + +
GeneFinder.jl

Appearance

Writting ORFIs into bioinformatic formats ​

This package facilitates the creation of FASTA, BED, and GFF files, specifically extracting Open Reading Frame (ORF) information from BioSequence instances, particularly those of type NucleicSeqOrView{A} where A, and then writing the information into the desired format.

Functionality:

The package provides four distinct functions for writing files in different formats:

FunctionDescription
write_orfs_fnaWrites nucleotide sequences in FASTA format.
write_orfs_faaWrites amino acid sequences in FASTA format.
write_orfs_bedOutputs information in BED format.
write_orfs_gffGenerates files in GFF format.

All these functions support processing both BioSequence instances and external FASTA files. In the case of a BioSequence instace into external files, simply provide the path to the FASTA file using a String to the path. To demonstrate the use of the write_* methods with a BioSequence, consider the following example:

julia
using BioSequences, GeneFinder
+
+# > 180195.SAMN03785337.LFLS01000089 -> finds only 1 gene in Prodigal (from Pyrodigal tests)
+seq = dna"AACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAACAGCACTGGCAATCTGACTGTGGGCGGTGTTACCAACGGCACTGCTACTACTGGCAACATCGCACTGACCGGTAACAATGCGCTGAGCGGTCCGGTCAATCTGAATGCGTCGAATGGCACGGTGACCTTGAACACGACCGGCAATACCACGCTCGGTAACGTGACGGCACAAGGCAATGTGACGACCAATGTGTCCAACGGCAGTCTGACGGTTACCGGCAATACGACAGGTGCCAACACCAACCTCAGTGCCAGCGGCAACCTGACCGTGGGTAACCAGGGCAATATCAGTACCGCAGGCAATGCAACCCTGACGGCCGGCGACAACCTGACGAGCACTGGCAATCTGACTGTGGGCGGCGTCACCAACGGCACGGCCACCACCGGCAACATCGCGCTGACCGGTAACAATGCACTGGCTGGTCCTGTCAATCTGAACGCGCCGAACGGCACCGTGACCCTGAACACAACCGGCAATACCACGCTGGGTAATGTCACCGCACAAGGCAATGTGACGACTAATGTGTCCAACGGCAGCCTGACAGTCGCTGGCAATACCACAGGTGCCAACACCAACCTGAGTGCCAGCGGCAATCTGACCGTGGGCAACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAGC"

Once a BioSequence object has been instantiated, the write_orfs_fna function proves useful for generating a FASTA file containing the nucleotide sequences of the ORFIs. Notably, the write_orfs* methods support either an IOStream or an IOBuffer as an output argument, allowing flexibility in directing the output either to a file or a buffer. In the following example, we demonstrate writing the output directly to a file.

julia
outfile = "LFLS01000089.fna"
+
+open(outfile, "w") do io
+    write_orfs_fna(seq, io, NaiveFinder())
+end
bash
cat LFLS01000089.fna
+
+>seq id=01 start=29 stop=40 strand=+ frame=2 features=[]
+ATGCAACCCTGA
+>seq id=02 start=137 stop=145 strand=+ frame=2 features=[]
+ATGCGCTGA
+>seq id=03 start=164 stop=184 strand=+ frame=2 features=[]
+ATGCGTCGAATGGCACGGTGA
+>seq id=04 start=173 stop=184 strand=+ frame=2 features=[]
+ATGGCACGGTGA
+>seq id=05 start=236 stop=241 strand=+ frame=2 features=[]
+ATGTGA
+>seq id=06 start=248 stop=268 strand=+ frame=2 features=[]
+ATGTGTCCAACGGCAGTCTGA
+>seq id=07 start=362 stop=373 strand=+ frame=2 features=[]
+ATGCAACCCTGA
+>seq id=08 start=470 stop=496 strand=+ frame=2 features=[]
+ATGCACTGGCTGGTCCTGTCAATCTGA
+>seq id=09 start=551 stop=574 strand=+ frame=2 features=[]
+ATGTCACCGCACAAGGCAATGTGA
+>seq id=10 start=569 stop=574 strand=+ frame=2 features=[]
+ATGTGA
+>seq id=11 start=581 stop=601 strand=+ frame=2 features=[]
+ATGTGTCCAACGGCAGCCTGA
+>seq id=12 start=695 stop=706 strand=+ frame=2 features=[]
+ATGCAACCCTGA

This could also be done to writting a FASTA file with the nucleotide sequences of the ORFIs using the write_orfs_fna function. Similarly for the BED and GFF files using the write_orfs_bed and write_orfs_gff functions respectively.

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GeneFinder.jl

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The ORFI type ​

For convenience, the ORFI type is more stringent in preventing the creation of incompatible instances. As a result, attempting to create an instance with incompatible parameters will result in an error. For instance, the following code snippet will trigger an error:

julia
ORFI{4,NaiveFinder}(1:10, '+', 4) # Or any F <: GeneFinderMethod
+
+ERROR: MethodError: no method matching OpenReadingFrameInterval{4, NaiveFinder}(::UnitRange{Int64}, ::Char, ::Int64)
+
+Closest candidates are:
+  (::Type{OpenReadingFrameInterval{N, F}} where {N, F})(::Any, ::Any, ::Any, ::Any, ::Any, ::Any, ::Any)
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:49
+  OpenReadingFrameInterval{N, F}(::Type{F}, ::String, ::Int64, ::Int64, ::Strand, ::Int8, ::LongSubSeq{DNAAlphabet{N}}, ::NamedTuple) where {N, F<:GeneFinder.GeneFinderMethod}
+   @ GeneFinder ~/.julia/dev/GeneFinder/src/types.jl:58
+
+Stacktrace:
+ [1] top-level scope
+   @ REPL[21]:1

Similar behavior will be encountered when the strand is neither + nor -. This precautionary measure helps prevent the creation of invalid ORFs, ensuring greater stability and enabling the extension of its interface. For example, after creating a specific ORFI, users can seamlessly iterate over a sequence of interest and verify whether the ORFI is contained within the sequence.

julia
ORFI{4,NaiveFinder}("seq", 1, 33, STRAND_POS, 1, convert(LongSubSeq, dna"ATGATGCATGCATGCATGCTAGTAACTAGCTAG"), NamedTuple())

Warning

It is still possible to create an ORFI and pass it to a sequence that does not necessarily contain an actual open reading frame. This will be addressed in future versions of the package. But the benefit of having it is that it will retrieve the corresponding subsequence of the sequence in a convinient way (5' to 3') regardless of the strand.

+ + + + \ No newline at end of file diff --git a/dev/roadmap.html b/dev/roadmap.html new file mode 100644 index 0000000..fb5c563 --- /dev/null +++ b/dev/roadmap.html @@ -0,0 +1,24 @@ + + + + + + GeneFinder.jl + + + + + + + + + + + + + +
GeneFinder.jl

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Roadmap ​

Coding genes (CDS - ORFIs) ​

  • ☒ Finding ORFIs

  • ☐ EasyGene

  • ☐ GLIMMER

  • ☐ Prodigal - Pyrodigal

  • ☐ PHANOTATE

  • ☐ k-mer based gene finders (?)

  • ☐ Augustus (?)

Non-coding genes (RNA) ​

  • ☐ Infernal

  • ☐ tRNAscan

Other features ​

  • ☐ parallelism SIMD ?

  • ☐ memory management (?)

  • ☐ incorporate Ribosime Binding Sites (RBS)

  • ☐ incorporate Programmed Reading Frame Shifting (PRFS)

  • ☐ specialized types

    • ☒ Gene

    • ☒ ORFI

    • ☒ Codon

    • ☒ CDS

    • ☐ EukaryoticGene (?)

    • ☐ ProkaryoticGene (?)

    • ☐ Intron

    • ☐ Exon

    • ☐ GFF –&gt; See other packages

    • ☐ FASTX –&gt; See I/O in other packages

Compatibilities ​

Must interact with or extend:

  • GenomicAnnotations.jl

  • BioSequences.jl

  • SequenceVariation.jl

  • GenomicFeatures.jl

  • FASTX.jl

  • Kmers.jl

  • Graphs.jl

+ + + + \ No newline at end of file diff --git a/dev/simplecodingrule.html b/dev/simplecodingrule.html new file mode 100644 index 0000000..188ea53 --- /dev/null +++ b/dev/simplecodingrule.html @@ -0,0 +1,118 @@ + + + + + + GeneFinder.jl + + + + + + + + + + + + + +
GeneFinder.jl

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The log-odds ratio decision rule ​

The sequence probability given a transition probability model could be used as the source of a sequence classification based on a decision rule to classify whether a sequence correspond to a model or another. Now, imagine we got two DNA sequence transition models, a CDS model and a No-CDS model. The log-odds ratio decision rule could be establish as:

S(X)=log⁡PC(X1=i1,…,XT=iT)PN(X1=i1,…,XT=iT){>η⇒coding<η⇒noncoding

Where the PC is the probability of the sequence given a CDS model, PN is the probability of the sequence given a No-CDS model, the decision rule is finally based on whether the ratio is greater or lesser than a given threshold η of significance level.

In this package we have implemented this rule and call some basic models of CDS and No-CDS of E. coli from Axelson-Fisk (2015) work (implemented in BioMarkovChains.jl package). To check whether a random sequence could be coding based on these decision we use the predicate log_odds_ratio_decision_rule with the ECOLICDS and ECOLINOCDS models:

julia
julia> using GeneFinder, BioSequences
+
+seq = dna"TTCGTCAGTCGTTCTGTTTCATTCAATACGATAGTAATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCCAGCATAGCCTCCAGATATCCCCTTATAGAGGTCAGAGGGGAACGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGATCATCCTCGGAACGGGCAATTTCGCTTGCCATATAATTCAGACAGGAAGCCAGATAACCGATTTCATCCTCACTATCGACCTGAAATTCATAATGCATATTACCGGCAGCATACTGCTCTGTGGCATGAGTGATCTTCCTCAGAGGAATATATACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAGCAGGAACAGGATTGCCAGGGTGATATAGGAAATATTCAGCAGGTTGTTACAGGATTTCTGAATATCATTCATATCAGTATGGATGACTACATAGCCTTTTACCTTGTAGTTGGAGGTAATGGGAGCAAATACAGTAAGTACATCCGAATCAAAATTACCGAAGAAATCACCAACAATGTAATAGGAGCCGCTGGTTACGGTCGAATCAAAATTCTCAATGACAACCACATTCTCCACATCTAAGGGACTATTGGTATCCAGTACCAGTCGTCCGGAGGGATTGATGATGCGAATCTCGGAATTCAGGTAGACCGCCAGGGAGTCCAGCTGCATTTTAACGGTCTCCAAAGTTGTTTCACTGGTGTACAATCCGCCGGCATAGGTTCCGGCGATCAGGGTTGCTTCGGAATAGAGACTTTCTGCCTTTTCCCGGATCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAACAATGATGAAACCAAATACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAAAGTGTTTTTTTCATAACAAATCCTGCTTTTGGTATGACTTAATTACGTACTTCGAATTTATAGCCGATGCCCCAGATGGTGCTGATCTTCCAGTTGGCATGATCCTTGATCTTCTC"
+
+orfsdna = findorfs(seq, finder=NaiveFinder, minlen=75, alternative_start=true) .|> sequence
+
+20-element Vector{NucSeq{4, DNAAlphabet{4}}}
+ ATGTATTTTTCGTGCATTTCCGGTGGAATCGTGCCGTCC…CGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGA
+ GTGCATTTCCGGTGGAATCGTGCCGTCCAGCATAGCCTC…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ GTGCCGTCCAGCATAGCCTCCAGATATCCCCTTATAGAG…CGGAAATCGTGGGATACATTGGCTACAAACTTTTTCTGA
+ GTGGGATACATTGGCTACAAACTTTTTCTGATCATCCTC…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ TTGCCATATAATTCAGACAGGAAGCCAGATAACCGATTT…GCATATTACCGGCAGCATACTGCTCTGTGGCATGAGTGA
+ ATGCTGCCGGTAATATGCATTATGAATTTCAGGTCGATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCACAGAGCAGTATGCTGCCGGTAATATGCATTATG…ATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCATATTACCGGCAGCATACTGCTCTGTGGCATGAGT…TACGATCTCAGTGAAAAAGATCAGAATGATCAGGGATAG
+ GTGATCTTCCTCAGAGGAATATATACGATCTCAGTGAAA…ATCAGGGATAGCAGGAACAGGATTGCCAGGGTGATATAG
+ ATGGATGACTACATAGCCTTTTACCTTGTAGTTGGAGGT…ATCAAAATTCTCAATGACAACCACATTCTCCACATCTAA
+ TTGGTGATTTCTTCGGTAATTTTGATTCGGATGTACTTACTGTATTTGCTCCCATTACCTCCAACTACAAGGTAA
+ TTGTTGGTGATTTCTTCGGTAATTTTGATTCGGATGTACTTACTGTATTTGCTCCCATTACCTCCAACTACAAGGTAA
+ ATGACAACCACATTCTCCACATCTAAGGGACTATTGGTA…CCGGAGGGATTGATGATGCGAATCTCGGAATTCAGGTAG
+ ATGCCGGCGGATTGTACACCAGTGAAACAACTTTGGAGACCGTTAAAATGCAGCTGGACTCCCTGGCGGTCTACCTGA
+ TTGTTTCACTGGTGTACAATCCGCCGGCATAGGTTCCGG…TCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAA
+ TTGCTTCGGAATAGAGACTTTCTGCCTTTTCCCGGATCAGATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAA
+ ATGTTCTTTGGTCATATTGGGAACAAAAGTTGTAACAAT…AAATACACCAAAAATAAAATATGCGAGTATAAATTTTAG
+ TTGGTCATATTGGGAACAAAAGTTGTAACAATGATGAAA…ACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAA
+ TTGGGAACAAAAGTTGTAACAATGATGAAACCAAATACACCAAAAATAAAATATGCGAGTATAAATTTTAGATAA
+ ATGCCAACTGGAAGATCAGCACCATCTGGGGCATCGGCT…TACGTAATTAAGTCATACCAAAAGCAGGATTTGTTATGA

Now the question is which of those sequences can we consider as coding sequences. We can use the iscoding predicate to check whether a sequence is coding or not based on the log-odds ratio decision rule:

julia
julia> iscoding.(orfsdna) # criteria = log_odds_ratio_decision_rule
+
+20-element BitVector:
+ 0
+ 0
+ 0
+ 0
+ 0
+ 1
+ 1
+ 0
+ 0
+ 0
+ 0
+ 0
+ 0
+ 1
+ 0
+ 0
+ 0
+ 0
+ 0
+ 0

In this case, the sequence has 20 ORFIs and only 3 of them are classified as coding sequences. The classification is based on the log-odds ratio decision rule and the transition probability models of E. coli CDS and No-CDS. The log_odds_ratio_decision_rule method will return a boolean vector with the classification of each ORFI in the sequence. Now we can simply filter the ORFIs that are coding sequences:

julia
orfs = filter(orf -> iscoding(orf), orfsdna)
+
+3-element Vector{NucSeq{4, DNAAlphabet{4}}}
+ ATGCTGCCGGTAATATGCATTATGAATTTCAGGTCGATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCACAGAGCAGTATGCTGCCGGTAATATGCATTATG…ATAGTGAGGATGAAATCGGTTATCTGGCTTCCTGTCTGA
+ ATGCCGGCGGATTGTACACCAGTGAAACAACTTTGGAGACCGTTAAAATGCAGCTGGACTCCCTGGCGGTCTACCTGA

Or in terms of the ORFI object:

julia
julia> orfs = findorfs(seq, minlen=75, finder=NaiveFinder, alternative_start=true) # find ORFIs with alternative start as well
+julia> orfs[iscoding.(orfsdna)]
+
+3-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(194:268, '-', 2)
+ ORFI{NaiveFinder}(194:283, '-', 2)
+ ORFI{NaiveFinder}(650:727, '-', 2)

Or in a single line using another genome sequence:

julia
julia> phi = dna"GTGTGAGGTTATAACGCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACTTTTATTTCTCGCCATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCTGTTTTACAGACACCTAAAGCTACATCGTCAACGTTATATTTTGATAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCATTCAGATGGATACATCTGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCTATGATGTTTATCCTTTGAATGGTCGCCATGATGGTGGTTATTATACCGTCAAGGACTGTGTGACTATTGACGTCCTTCCCCGTACGCCGGGCAATAACGTTTATGTTGGTTTCATGGTTTGGTCTAACTTTACCGCTACTAAATGCCGCGGATTGGTTTCGCTGAATCAGGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGCTGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGGCATTCAAGGTGATGTGCTTGCTACCGATAACAATACTGTAGGCATGGGTGATGCTGGTATTAAATCTGCCATTCAAGGCTCTAATGTTCCTAACCCTGATGAGGCCGCCCCTAGTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAAGGATACTCGTGATTATCTTGCTGCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCTCTGCTGGTATGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTTACTAAAATGCAACTGGACAATCAGAAAGAGATTGCCGAGATGCAAAATGAGACTCAAAAAGAGATTGCTGGCATTCAGTCGGCGACTTCACGCCAGAATACGAAAGACCAGGTATATGCACAAAATGAGATGCTTGCTTATCAACAGAAGGAGTCTACTGCTCGCGTTGCGTCTATTATGGAAAACACCAATCTTTCCAAGCAACAGCAGGTTTCCGAGATTATGCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTCAGTATTTTACCAATGACCAAATCAAAGAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAATCAGCGGTATGGCTCTTCTCATATTGGCGCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACAATTTCTGGAAAGACGGTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGGAAATAACCGTCAGGATTGACACCCTCCCAATTGTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCTGAATGTCACGCTGATTATTTTGACTTTGAGCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAGCAGATGGATAACCGCATCAAGCTCTTGGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATGGTGATATGTATGTTGACGGCCATAAGGCTGCTTCTGACGTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTTAATGGATGAATTGGCACAATGCTACAATGTGCTCCCCCAACTTGATATTAATAACACTATAGACCACCGCCCCGAAGGGGACGAAAAATGGTTTTTAGAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTCGCGATGAGTATAATTACCCCAAAAAGAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGATGAATGCAATGCGACAGGCTCATGCTGATGGTTGGTTTATCGTTTTTGACACTCTCACGTTGGCTGACGACCGATTAGAGGCGTTTTATGATAATCCCAATGCTTTGCGTGACTATTTTCGTGATATTGGTCGTATGGTTCTTGCTGCCGAGGGTCGCAAGGCTAATGATTCACACGCCGACTGCTATCAGTATTTTTGTGTGCCTGAGTATGGTACAGCTAATGGCCGTCTTCATTTCCATGCGGTGCACTTTATGCGGACACTTCCTACAGGTAGCGTTGACCCTAATTTTGGTCGTCGGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAAAATACGTGGCCTTATGGTTACAGTATGCCCATCGCAGTTCGCTACACGCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATATGGCTGTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAGTCAGATATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTCACTAAAAACCAAGCTGTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTCGGGATGAAAATGCTCACAATGACAAATCTGTCCACGGAGTGCTTAATCCAACTTACCAAGCTGGGTTACGACGCGACGCCGTTCAACCAGATATTGAAGCAGAACGCAAAAAGAGAGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGCGCAACCTGTGACGACAAATCTGCTCAAATTTATGCGCGCTTCGATAAAAATGATTGGCGTATCCAACCTGCAGAGTTTTATCGCTTCCATGACGCAGAAGTTAACACTTTCGGATATTTCTGATGAGTCGAAAAATTATCTTGATAAAGCAGGAATTACTACTGCTTGTTTACGAATTAAATCGAAGTGGACTGCTGGCGGAAAATGAGAAAATTCGACCTATCCTTGCGCAGCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCATCAACTAACGATTCTGTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGACTGGTTTAGATATGAGTCACATTTTGTTCATGGTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGTGGATTACTATCTGAGTCCGATGCTGTTCAACCACTAATAGGTAAGAAATCATGAGTCAAGTTACTGAACAATCCGTACGTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGCCGTTTTGGATTTAACCGAAGATGATTTCGATTTTCTGACGAGTAACAAAGTTTGGATTGCTACTGACCGCTCTCGTGCTCGTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGGATACCCTCGCTTTCCTGCTCCTGTTGAGTTTATTGCTGCCGTCATTGCTTATTATGTTCATCCCGTCAACATTCAAACGGCCTGTCTCATCATGGAAGGCGCTGAATTTACGGAAAACATTATTAATGGCGTCGAGCGTCCGGTTAAAGCCGCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGCAGGAAACACTGACGTTCTTACTGACGCAGAAGAAAACGTGCGTCAAAAATTACGTGCGGAAGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGTACTAAAGGCAAGCGTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTTACTTGAGGATAAATTATGTCTAATATTCAAACTGGCGCCGAGCGTATGCCGCATGACCTTTCCCATCTTGGCTTCCTTGCTGGTCAGATTGGTCGTCTTATTACCATTTCAACTACTCCGGTTATCGCTGGCGACTCCTTCGAGATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGGCCTTGCTATTGACTCTACTGTAGACATTTTTACTTTTTATGTCCCTCATCGTCACGTTTATGGTGAACAGTGGATTAAGTTCATGAAGGATGGTGTTAATGCCACTCCTCTCCCGACTGTTAACACTACTGGTTATATTGACCATGCCGCTTTTCTTGGCACGATTAACCCTGATACCAATAAAATCCCTAAGCATTTGTTTCAGGGTTATTTGAATATCTATAACAACTATTTTAAAGCGCCGTGGATGCCTGACCGTACCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTCGCCAAATGACGACTTCTACCACATCTATTGACATTATGGGTCTGCAAGCTGCTTATGCTAATTTGCATACTGACCAAGAACGTGATTACTTCATGCAGCGTTACCATGATGTTATTTCTTCATTTGGAGGTAAAACCTCTTATGACGCTGACAACCGTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCATCTGGCTATGATGTTGATGGAACTGACCAAACGTCGTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCTATAAACATTCTGTGCCGCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTTTGACTTATACCGATATTGCTGGCGACCCTGTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGTGATTTGCAAGAACGCGTACTTATTCGCCACCATGATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATGTGACCGTTTATCGCAATCTGCCGACCACTCGCGATTCAATCATGACTTCGTGATAAAAGATTGA"
+
+julia> filter(x -> iscoding(sequence(x), η=1e-10) && length(x) > 100, findorfs(phi))
+
+34-element Vector{ORFI{4, NaiveFinder}}:
+ ORFI{NaiveFinder}(636:1622, '+', 3)
+ ORFI{NaiveFinder}(687:1622, '+', 3)
+ ORFI{NaiveFinder}(774:1622, '+', 3)
+ ORFI{NaiveFinder}(781:1389, '+', 1)
+ ORFI{NaiveFinder}(814:1389, '+', 1)
+ ORFI{NaiveFinder}(829:1389, '+', 1)
+ ORFI{NaiveFinder}(861:1622, '+', 3)
+ ORFI{NaiveFinder}(1021:1389, '+', 1)
+ ORFI{NaiveFinder}(1386:1622, '+', 3)
+ ORFI{NaiveFinder}(1447:1635, '+', 1)
+ ORFI{NaiveFinder}(1489:1635, '+', 1)
+ ORFI{NaiveFinder}(1501:1635, '+', 1)
+ ORFI{NaiveFinder}(1531:1635, '+', 1)
+ ORFI{NaiveFinder}(2697:3227, '+', 3)
+ ORFI{NaiveFinder}(2745:3227, '+', 3)
+ ⋮
+ ORFI{NaiveFinder}(2874:3227, '+', 3)
+ ORFI{NaiveFinder}(2973:3227, '+', 3)
+ ORFI{NaiveFinder}(3108:3227, '+', 3)
+ ORFI{NaiveFinder}(3142:3312, '+', 1)
+ ORFI{NaiveFinder}(3481:3939, '+', 1)
+ ORFI{NaiveFinder}(3659:3934, '+', 2)
+ ORFI{NaiveFinder}(3734:3934, '+', 2)
+ ORFI{NaiveFinder}(3772:3939, '+', 1)
+ ORFI{NaiveFinder}(3806:3934, '+', 2)
+ ORFI{NaiveFinder}(4129:4287, '+', 1)
+ ORFI{NaiveFinder}(4160:4291, '-', 2)
+ ORFI{NaiveFinder}(4540:4644, '+', 1)
+ ORFI{NaiveFinder}(4690:4866, '+', 1)
+ ORFI{NaiveFinder}(4741:4866, '+', 1)
+ ORFI{NaiveFinder}(4744:4866, '+', 1)
+ + + + \ No newline at end of file
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zizzp}Vpq>(cbwuS_@9HIZtsc|D5OR4;l-OjO419|G9^OaSh#HmuQn3__U4^%zn z3zgSqZxa>eaxJTKUNVOv6Uo z`X5fo9Yr~HBKacaY3`IbR;|aks-P`>O1VkOG_I>{NTDuSv`m+}h$cZj>CC@RMLedAtNKe_txQ}ii6@>>mJd?Ca++0|y0RHN9eQpvSJW5EsC|Cvl}f(r qg8F^wNJLW9HXag++f*3l>qLgABq|+GP_C@lsDY~<*Z+^97XScAUqWF3 literal 0 HcmV?d00001 diff --git a/dev/assets/iodocs.md.CF-VJtx2.js b/dev/assets/iodocs.md.CF-VJtx2.js new file mode 100644 index 0000000..d009aa9 --- /dev/null +++ b/dev/assets/iodocs.md.CF-VJtx2.js @@ -0,0 +1,33 @@ +import{_ as i,c as a,a5 as t,o as h}from"./chunks/framework.NQCVMzj3.js";const F=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"iodocs.md","filePath":"iodocs.md","lastUpdated":null}'),n={name:"iodocs.md"};function k(l,s,p,e,r,d){return h(),a("div",null,s[0]||(s[0]=[t(`

This package facilitates the creation of FASTA, BED, and GFF files, specifically extracting Open Reading Frame (ORF) information from BioSequence instances, particularly those of type NucleicSeqOrView{A} where A, and then writing the information into the desired format.

Functionality:

The package provides four distinct functions for writing files in different formats:

FunctionDescription
write_orfs_fnaWrites nucleotide sequences in FASTA format.
write_orfs_faaWrites amino acid sequences in FASTA format.
write_orfs_bedOutputs information in BED format.
write_orfs_gffGenerates files in GFF format.

All these functions support processing both BioSequence instances and external FASTA files. In the case of a BioSequence instace into external files, simply provide the path to the FASTA file using a String to the path. To demonstrate the use of the write_* methods with a BioSequence, consider the following example:

julia
using BioSequences, GeneFinder
+
+# > 180195.SAMN03785337.LFLS01000089 -> finds only 1 gene in Prodigal (from Pyrodigal tests)
+seq = dna"AACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAACAGCACTGGCAATCTGACTGTGGGCGGTGTTACCAACGGCACTGCTACTACTGGCAACATCGCACTGACCGGTAACAATGCGCTGAGCGGTCCGGTCAATCTGAATGCGTCGAATGGCACGGTGACCTTGAACACGACCGGCAATACCACGCTCGGTAACGTGACGGCACAAGGCAATGTGACGACCAATGTGTCCAACGGCAGTCTGACGGTTACCGGCAATACGACAGGTGCCAACACCAACCTCAGTGCCAGCGGCAACCTGACCGTGGGTAACCAGGGCAATATCAGTACCGCAGGCAATGCAACCCTGACGGCCGGCGACAACCTGACGAGCACTGGCAATCTGACTGTGGGCGGCGTCACCAACGGCACGGCCACCACCGGCAACATCGCGCTGACCGGTAACAATGCACTGGCTGGTCCTGTCAATCTGAACGCGCCGAACGGCACCGTGACCCTGAACACAACCGGCAATACCACGCTGGGTAATGTCACCGCACAAGGCAATGTGACGACTAATGTGTCCAACGGCAGCCTGACAGTCGCTGGCAATACCACAGGTGCCAACACCAACCTGAGTGCCAGCGGCAATCTGACCGTGGGCAACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAGC"

Once a BioSequence object has been instantiated, the write_orfs_fna function proves useful for generating a FASTA file containing the nucleotide sequences of the ORFIs. Notably, the write_orfs* methods support either an IOStream or an IOBuffer as an output argument, allowing flexibility in directing the output either to a file or a buffer. In the following example, we demonstrate writing the output directly to a file.

julia
outfile = "LFLS01000089.fna"
+
+open(outfile, "w") do io
+    write_orfs_fna(seq, io, NaiveFinder())
+end
bash
cat LFLS01000089.fna
+
+>seq id=01 start=29 stop=40 strand=+ frame=2 features=[]
+ATGCAACCCTGA
+>seq id=02 start=137 stop=145 strand=+ frame=2 features=[]
+ATGCGCTGA
+>seq id=03 start=164 stop=184 strand=+ frame=2 features=[]
+ATGCGTCGAATGGCACGGTGA
+>seq id=04 start=173 stop=184 strand=+ frame=2 features=[]
+ATGGCACGGTGA
+>seq id=05 start=236 stop=241 strand=+ frame=2 features=[]
+ATGTGA
+>seq id=06 start=248 stop=268 strand=+ frame=2 features=[]
+ATGTGTCCAACGGCAGTCTGA
+>seq id=07 start=362 stop=373 strand=+ frame=2 features=[]
+ATGCAACCCTGA
+>seq id=08 start=470 stop=496 strand=+ frame=2 features=[]
+ATGCACTGGCTGGTCCTGTCAATCTGA
+>seq id=09 start=551 stop=574 strand=+ frame=2 features=[]
+ATGTCACCGCACAAGGCAATGTGA
+>seq id=10 start=569 stop=574 strand=+ frame=2 features=[]
+ATGTGA
+>seq id=11 start=581 stop=601 strand=+ frame=2 features=[]
+ATGTGTCCAACGGCAGCCTGA
+>seq id=12 start=695 stop=706 strand=+ frame=2 features=[]
+ATGCAACCCTGA

This could also be done to writting a FASTA file with the nucleotide sequences of the ORFIs using the write_orfs_fna function. Similarly for the BED and GFF files using the write_orfs_bed and write_orfs_gff functions respectively.

`,11)]))}const C=i(n,[["render",k]]);export{F as __pageData,C as default}; diff --git a/dev/assets/iodocs.md.CF-VJtx2.lean.js b/dev/assets/iodocs.md.CF-VJtx2.lean.js new file mode 100644 index 0000000..d009aa9 --- /dev/null +++ b/dev/assets/iodocs.md.CF-VJtx2.lean.js @@ -0,0 +1,33 @@ +import{_ as i,c as a,a5 as t,o as h}from"./chunks/framework.NQCVMzj3.js";const F=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"iodocs.md","filePath":"iodocs.md","lastUpdated":null}'),n={name:"iodocs.md"};function k(l,s,p,e,r,d){return h(),a("div",null,s[0]||(s[0]=[t(`

Writting ORFIs into bioinformatic formats ​

This package facilitates the creation of FASTA, BED, and GFF files, specifically extracting Open Reading Frame (ORF) information from BioSequence instances, particularly those of type NucleicSeqOrView{A} where A, and then writing the information into the desired format.

Functionality:

The package provides four distinct functions for writing files in different formats:

FunctionDescription
write_orfs_fnaWrites nucleotide sequences in FASTA format.
write_orfs_faaWrites amino acid sequences in FASTA format.
write_orfs_bedOutputs information in BED format.
write_orfs_gffGenerates files in GFF format.

All these functions support processing both BioSequence instances and external FASTA files. In the case of a BioSequence instace into external files, simply provide the path to the FASTA file using a String to the path. To demonstrate the use of the write_* methods with a BioSequence, consider the following example:

julia
using BioSequences, GeneFinder
+
+# > 180195.SAMN03785337.LFLS01000089 -> finds only 1 gene in Prodigal (from Pyrodigal tests)
+seq = dna"AACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAACAGCACTGGCAATCTGACTGTGGGCGGTGTTACCAACGGCACTGCTACTACTGGCAACATCGCACTGACCGGTAACAATGCGCTGAGCGGTCCGGTCAATCTGAATGCGTCGAATGGCACGGTGACCTTGAACACGACCGGCAATACCACGCTCGGTAACGTGACGGCACAAGGCAATGTGACGACCAATGTGTCCAACGGCAGTCTGACGGTTACCGGCAATACGACAGGTGCCAACACCAACCTCAGTGCCAGCGGCAACCTGACCGTGGGTAACCAGGGCAATATCAGTACCGCAGGCAATGCAACCCTGACGGCCGGCGACAACCTGACGAGCACTGGCAATCTGACTGTGGGCGGCGTCACCAACGGCACGGCCACCACCGGCAACATCGCGCTGACCGGTAACAATGCACTGGCTGGTCCTGTCAATCTGAACGCGCCGAACGGCACCGTGACCCTGAACACAACCGGCAATACCACGCTGGGTAATGTCACCGCACAAGGCAATGTGACGACTAATGTGTCCAACGGCAGCCTGACAGTCGCTGGCAATACCACAGGTGCCAACACCAACCTGAGTGCCAGCGGCAATCTGACCGTGGGCAACCAGGGCAATATCAGTACCGCGGGCAATGCAACCCTGACTGCCGGCGGTAACCTGAGC"

Once a BioSequence object has been instantiated, the write_orfs_fna function proves useful for generating a FASTA file containing the nucleotide sequences of the ORFIs. Notably, the write_orfs* methods support either an IOStream or an IOBuffer as an output argument, allowing flexibility in directing the output either to a file or a buffer. In the following example, we demonstrate writing the output directly to a file.

julia
outfile = "LFLS01000089.fna"
+
+open(outfile, "w") do io
+    write_orfs_fna(seq, io, NaiveFinder())
+end
bash
cat LFLS01000089.fna
+
+>seq id=01 start=29 stop=40 strand=+ frame=2 features=[]
+ATGCAACCCTGA
+>seq id=02 start=137 stop=145 strand=+ frame=2 features=[]
+ATGCGCTGA
+>seq id=03 start=164 stop=184 strand=+ frame=2 features=[]
+ATGCGTCGAATGGCACGGTGA
+>seq id=04 start=173 stop=184 strand=+ frame=2 features=[]
+ATGGCACGGTGA
+>seq id=05 start=236 stop=241 strand=+ frame=2 features=[]
+ATGTGA
+>seq id=06 start=248 stop=268 strand=+ frame=2 features=[]
+ATGTGTCCAACGGCAGTCTGA
+>seq id=07 start=362 stop=373 strand=+ frame=2 features=[]
+ATGCAACCCTGA
+>seq id=08 start=470 stop=496 strand=+ frame=2 features=[]
+ATGCACTGGCTGGTCCTGTCAATCTGA
+>seq id=09 start=551 stop=574 strand=+ frame=2 features=[]
+ATGTCACCGCACAAGGCAATGTGA
+>seq id=10 start=569 stop=574 strand=+ frame=2 features=[]
+ATGTGA
+>seq id=11 start=581 stop=601 strand=+ frame=2 features=[]
+ATGTGTCCAACGGCAGCCTGA
+>seq id=12 start=695 stop=706 strand=+ frame=2 features=[]
+ATGCAACCCTGA

This could also be done to writting a FASTA file with the nucleotide sequences of the ORFIs using the write_orfs_fna function. Similarly for the BED and GFF files using the write_orfs_bed and write_orfs_gff functions respectively.

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Writting ORFIs into bioinformatic formats ​