Software package for signal-level analysis of Oxford Nanopore sequencing data. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels with respect to a reference genome and more (see Nanopolish modules, below).
A compiler that supports C++11 is needed to build nanopolish. Development of the code is performed using gcc-4.8.
By default, nanopolish will download and compile all of its required dependencies. Some users however may want to use system-wide versions of the libraries. To turn off the automatic installation of dependencies set HDF5=noinstall, EIGEN=noinstall or HTS=noinstall parameters when running make as appropriate. The current versions and compile options for the dependencies are:
- libhdf5-1.8.14 compiled with multi-threading support
--enable-threadsafe - eigen-3.2.5
- htslib-1.4
Additionally the helper scripts require biopython and pysam.
You can download and compile the latest code from github as follows:
git clone --recursive https://github.com/jts/nanopolish.git
cd nanopolish
make
When major features have been added or bugs fixed, we will tag and release a new version of nanopolish. If you wish to use a particular version, you can checkout the tagged version before compiling:
git clone --recursive https://github.com/jts/nanopolish.git
cd nanopolish
git checkout v0.7.1
make
The main subprograms of nanopolish are:
nanopolish extract: extract reads in FASTA or FASTQ format from a directory of FAST5 files
nanopolish call-methylation: predict genomic bases that may be methylated
nanopolish variants: detect SNPs and indels with respect to a reference genome
nanopolish variants --consensus: calculate an improved consensus sequence for a draft genome assembly
nanopolish eventalign: align signal-level events to k-mers of a reference genome
Nanopolish needs access to the signal-level data measured by the nanopore sequencer. The first step of any nanopolish workflow is to prepare the input data by telling nanopolish where to find the signal files. If you ran Albacore 2.0 on your data you should run nanopolish index on your input reads (-d can be specified more than once if using multiple runs):
# Index the output of the albacore basecaller
nanopolish index -d /path/to/raw_fast5s/ -s sequencing_summary.txt albacore_output.fastq
The -s option tells nanopolish to read the sequencing_summary.txt file from Albacore to speed up indexing. Without this option nanopolish index is extremely slow as it needs to read every fast5 file individually. If you basecalled your run in parallel, so you have multiple sequencing_summary.txt files, you can use the -f option to pass in a file containing the paths to the sequencing summary files (one per line).
The original purpose of nanopolish was to compute an improved consensus sequence for a draft genome assembly produced by a long-read assembly like canu. This section describes how to do this, starting with your draft assembly which should have megabase-sized contigs. We've also posted a tutorial including example data here.
# Index the draft genome
bwa index draft.fa
# Align the basecalled reads to the draft sequence
bwa mem -x ont2d -t 8 draft.fa reads.fa | samtools sort -o reads.sorted.bam -T reads.tmp -
samtools index reads.sorted.bam
Now, we use nanopolish to compute the consensus sequence (the genome is polished in 50kb blocks and there will be one output file per block). We'll run this in parallel:
python nanopolish_makerange.py draft.fa | parallel --results nanopolish.results -P 8 \
nanopolish variants --consensus polished.{1}.fa -w {1} -r reads.fa -b reads.sorted.bam -g draft.fa -t 4 --min-candidate-frequency 0.1
This command will run the consensus algorithm on eight 50kbp segments of the genome at a time, using 4 threads each. Change the -P and --threads options as appropriate for the machines you have available.
After all polishing jobs are complete, you can merge the individual 50kb segments together back into the final assembly:
python nanopolish_merge.py polished.*.fa > polished_genome.fa
nanopolish can use the signal-level information measured by the sequencer to detect 5-mC as described here. We've posted a tutorial on how to call methylation here.
First build the image from the dockerfile:
docker build .
Note the uuid given upon successful build. Then you can run nanopolish from the image:
docker run -v /path/to/local/data/data/:/data/ -it :image_id ./nanopolish eventalign -r /data/reads.fa -b /data/alignments.sorted.bam -g /data/ref.fa
The fast table-driven logsum implementation was provided by Sean Eddy as public domain code. This code was originally part of hmmer3. Nanopolish also includes code from Oxford Nanopore's scrappie basecaller. This code is licensed under the MPL.