Quick Start

Assuming you want to generate core snps for more than a few hundred samples and run the analysis in parallel on cluster(Time and memory efficient). The default pbs resources used for parallel jobs are:

nodes=1:ppn=4,pmem=4000mb,walltime=24:00:00

See option resources in scheduler section of config file. Detailed information in section Customizing Config file

• Run variant calling step (All) on a set of PE reads with default parameters

python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps All -cluster parallel-cluster

The above command will run variant calling (step 1) pipeline on a set of PE reads residing in test_readsdir. The results will be saved in output directory test_output_core. The config file contains options for some frequently used reference genome. To know which reference genomes are included in config file, look up the config file or check the help menu of the pipeline.

The results of variant calling will be placed in an individual folder generated for each sample in output directory. A log file for each sample will be generated and can be found in each sample folder inside the out directory. A single log file of this step will be generated in main output directory. For more information on log file prefix and convention, please refer log section below.

• Run core_prep step to generate files for core SNP calling.

Run this steps to generate various intermediate files that will be used for generating core SNPs.

python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps core_prep -cluster cluster

• Run core step to generate final core SNP consensus fasta files.

Since this step compares multiple files simultaneously and involves multiple I/O operations, It is recommended to provide higher memory compute resources.

example:

nodes=1:ppn=4,mem=47000mb,walltime=24:00:00

Replace the resources option in scheduler section of config file with the above line before running the command.

python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps core -cluster cluster

• Run report step to aggregate results under prefix_core_results folder

python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps report -cluster cluster

• Run tree step to generate FastTree/RAxML phylogenetic tree and recombination filterating using Gubbins

python /nfs/esnitkin/bin_group/pipeline/Github/variant_calling_pipeline/variant_call.py -type PE -readsdir /Path-To-Your/test_readsdir/ -outdir /Path/test_output_core/ -analysis output_prefix -index MRSA_USA_300 -steps tree -cluster cluster -gubbins yes