SSR_pipeline is a flexible set of programs designed to efficiently identify simple sequence repeats (SSRs; for example, microsatellites) from paired-end high-throughput Illumina DNA sequencing data. The program suite contains three analysis modules along with a fourth control module that can be used to automate analyses of large volumes of data. The modules are used to (1) identify the subset of paired-end sequences that pass quality standards, (2) align paired-end reads into a single composite DNA sequence, and (3) identify sequences that possess microsatellites conforming to user specified parameters. Each of the three separate analysis modules also can be used independently to provide greater flexibility or to work with FASTQ or FASTA files generated from other sequencing platforms (Roche 454, Ion Torrent, etc).
All modules are implemented in the Python programming language and can therefore be used from nearly any computer operating system (Linux, Macintosh, Windows). The program suite relies on a compiled Python extension module to perform paired-end alignments. Instructions for compiling the extension from source code are provided in the documentation. Users who do not have Python installed on their computers or who do not have the ability to compile software also may choose to download packaged executable files. These files include all Python scripts, a copy of the compiled extension module, and a minimal installation of Python in a single binary executable. See program documentation for more information.
module spider ssr_pipeline to find out what environment modules are available for this application.
- HPC_SSR_PIPELINE_DIR - installation directory
- HPC_SSR_PIPELINE_BIN - executable directory
If you publish research that uses ssr_pipeline you have to cite it as follows:
Miller, M.P., Knaus, B.J., Mullins, T.D., and Haig, S.M., 2013, SSR_pipeline—Computer software for the identification of microsatellite sequences from paired-end Illumina High-Throughput DNA sequence data (ver. 1.1, February 2014): U.S. Geological Survey Data Series 778.