THIS VERSION IS DEPRECATED GO TO https://github.com/SchwartzLabURI/SISRS/

SISRS

SISRS: Site Identification from Short Read Sequences
Version 1.6.2
Copyright (c) 2013-2016 Rachel Schwartz [email protected]
https://github.com/rachelss/SISRS
More information: Schwartz, R.S., K.M Harkins, A.C. Stone, and R.A. Cartwright. 2015. A composite genome approach to identify phylogenetically informative data from next-generation sequencing. BMC Bioinformatics. 16:193. (http://www.biomedcentral.com/1471-2105/16/193/)

Talk from Evolution 2014 describing SISRS and its application:
https://www.youtube.com/watch?v=0OMPuWc-J2E&list=UUq2cZF2DnfvIUVg4tyRH5Ng

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details.

Requirements

• Built-In Genome Assemblers (Required if SISRS is building your composite genome)
  • Velvet (tested with v.1.2.10) (http://www.ebi.ac.uk/~zerbino/velvet/)
  • Minia (tested with v.2.0.7) (http://minia.genouest.org/)
  • AbySS (tested with v.2.0.2) (http://www.bcgsc.ca/platform/bioinfo/software/abyss)
• Bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)
• Python 2.7, Biopython, and PySAM
• Samtools v1.3.1 (http://www.htslib.org/)
• GNU Parallel (http://www.gnu.org/software/parallel/)
• MAFFT (http://mafft.cbrc.jp/alignment/software/)
• BBMap [requires Java] (https://sourceforge.net/projects/bbmap/)

Input

Next-gen sequence data such as Illumina HiSeq reads. Data must be sorted into folders by taxon (e.g. species or genus). Paired reads in fastq format must be specified by _R1 and _R2 in the (otherwise identical) filenames. Paired and unpaired reads must have a fastq file extension.

Running SISRS

Usage:

sisrs command options

By default, SISRS assumes that

• A reference genome is not available and a composite assembly will be assembled using Velvet
• The K-mer size to be used by Velvet in contig assembly is 21.
• Only one processor is available.
• Files are in fastq format.
• Paired read filenames end with _R1 and _R2
• A site is only required to have data for two species to be included in the final alignment.
• Folders containing reads are in the present working directory
• SISRS data will be output into the present working directory
• A minimum of three reads are required to call the base at a site for a taxon.

Commands:

sites: produce an alignment of sites from raw reads

loci: produce a set of aligned loci based on the most variable regions of the composite genome

Subcommands of sites:

subSample: run sisrs subsampling scheme, subsampling reads from all taxa to ~10X coverage across species, relative to user-specified genome size

buildContigs: given subsampled reads, run sisrs composite genome assembly with user-specified assembler

alignContigs: align reads to composite genome as single-ended, uniquely mapped

mapContigs: align composite genome reads to a reference genome (optional)

identifyFixedSites: find sites with no within-taxa variation

outputAlignment: output alignment file of sisrs sites

changeMissing: given alignment of sites (alignment.nex), output a file with only sites missing fewer than a specified number of samples per site

Option Flags:

• -g : the approximate genome size (mandatory if sisrs will be assembling a composite genome)
• -p : use this number of processors [Default: 1]
• -r : the path to the reference genome in fasta format [Optional]
• -k : k-mer size (for assembly) [Default: 21]
• -f : path to the directory containing the folders of reads [Default: Current Directory]
• -z : path to either an empty or non-existent directory where SISRS will output data [Default: Current Directory]
• -n : the number of reads required to call a base at a site [Default: 3]
• -t : the threshold for calling a site; e.g. 0.99 means that >99% of bases for that taxon must be one allele; only recommended for low ploidy with <3 individuals [Default: 1 (100%)]
• -m : the number of species that are allowed to have missing data at a site
• -o : the length of the final loci dataset for dating
• -l : the number of alleles
• -a : assembler [velvet, minia, abyss, or premade; Default: velvet]
  • If using a premade composite genome, it must be in a folder named 'premadeoutput' in the same directory as the folders of read data, and must be called 'contigs.fa'
• -s : Sites to analyze when running 'loci' [0,1,2]
  • 0 [Default], all variable sites, including singletons
  • 1, variable sites excluding singletons
  • 2, only biallelic variable sites
• -c : continuous command mode for calling subcommands [1,0]
  • 1 [Default]: calling a subcommand runs that subcommand and all subsequent steps in the pipeline
  • 0: calling a subcommand runs only that subcommand

Output

Nexus file with variable sites in a single alignment. Usable in most major phylogenetics software as a concatenated alignment with a setting for variable-sites-only.

Test Data

The folder test_data (https://github.com/rachelss/SISRS_test_data) contains simulated data for 10 species on the tree found in simtree.tre . Using 40 processors this run took 9 minutes. Analysis of the alignment output by sisrs using raxml produced the correct tree.

Sample commands

1. Basic sisrs run: start with fastq files and produce an alignment of variable sites

sisrs sites -g 1745690

2. Basic sisrs run with modifications

sisrs sites -g 1745690 -p 40 -m 4 -f /usr/test_data -z /usr/output_data -t .99 -a minia

3. Run only sisrs read subsampling step

sisrs subSample -g 1745690 -f /usr/test_data -c 0

4. Produce an alignment of loci based on the most variable loci in your basic sisrs run. Note - this command will run sisrs sites if (and only if) it was not run previously.

sisrs loci -g 1745690 -p 40 -l 2 -f /usr/test_data           # Will run sites first, then loci
sisrs loci -g 1745690 -p 40 -l 2 -f /usr/SISRS_sites_ouput   # Will run loci from previous sites data

5. Get loci from your fastq files given known loci.

   first name your reference loci ref_genes.fa and put in your main folder

sisrs loci -p 40 -f /usr/test_data