README.md

Annotation Pipeline for BT&S

This repository will make up the evidence for the Bioinformatics Software and Tools module of the ARU/Sanger BSc Bioinformatics Year 2 Course.

Installation of pipeline

This can be achieved through the use of:

conda create --file environment.yml

However, this can take quite some time and personally was faster installing it all separately.

Usage

Local Machine Usage 1 - Clone repo

2 - cd annotation-pipeline

3 - Download data into the raw_data folders (links below).

4 - python3 config-writer.py REFERENCE SAMPLE_1 SAMPLE_2 CHR_OF_INTEREST

5 - snakemake --configfile config.yaml --cores 10

This pipeline will require the igv.sh file to have been set to 15gb. (-Xmx15g compared to -Xmx4g) this is in order to produce the .genome file.

If this can't be used then please use the built-in database for Human (hg38).

snpEff is set to -Xmx4g in order to annotate the vcf.

After completion:

6 - Run IGV

In my case this was bash {location of installation}/igv.sh

7 - OPTION A - build .genome file

Genomes > Create .genome

The fasta file = the reference genome

The Gene file is the .gff file downloaded earlier

7 - OPTION B - use pre-installed Human (hg38)

8 - Load annotated file

File > load from file

Navigate to folder s13 witch should contain something akin to: chr10.filt.annotated.vcf.gz

9 - Navigate to location chr10:94,760,000-94,860,000 which will centre on the gene CYP2C19.

Usage - Cluster implementation

snakemake --configfile config.yaml --cores 10 --cluster-config
./cluster.yaml --cluster "bsub -q {cluster.queue} -oo {cluster.output} 
-eo {cluster.error} -M {cluster.memory} -R {cluster.resources} -J {cluster.jobname}" 
-j 10 --use-conda

Currently in Repo

environment.yml - a list of packages used in this project

Data

Files should be downloaded into a {project dir}/raw_data/{ reference | sample_data }/{Downloaded file}.

Sample Data comes from the Utah family platinum read set.

ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR622/SRR622461/SRR622461_1.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR622/SRR622461/SRR622461_2.fastq.gz

Mapped against GRCH38.p15:

https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_full_analysis_set.fna.gz

We also used:

https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_full_analysis_set.refseq_annotation.gff.gz

This was used in conjunction with the reference genome to build a .genome file to better compare the results of this pipeline when visualising the end product with IGV.