Strainer: Software for analysis of population variation in community genomic datasets

Eppley, John M.; Tyson, Gene W.; Getz, Wayne M.; Banfield, Jillian F.

doi:10.1186/1471-2105-8-398

Strainer: Software for analysis of population variation in community genomic datasets

Journal Article · Wed Oct 17 00:00:00 EDT 2007 · BMC Bioinformatics

DOI:https://doi.org/10.1186/1471-2105-8-398· OSTI ID:1626345

Eppley, John M. ^[1]; Tyson, Gene W. ^[2]; Getz, Wayne M. ^[3]; Banfield, Jillian F. ^[3]

Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering; Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Civil and Environmental Engineering; DOE/OSTI
Univ. of California, Berkeley, CA (United States). Dept. of Environmental Science, Policy and Management; Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Civil and Environmental Engineering
Univ. of California, Berkeley, CA (United States). Dept. of Environmental Science, Policy and Management

Background: Metagenomic analyses of microbial communities that are comprehensive enough to provide multiple samples of most loci in the genomes of the dominant organism types will also reveal patterns of genetic variation within natural populations. New bioinformatic tools will enable visualization and comprehensive analysis of this sequence variation and inference of recent evolutionary and ecological processes. Results: We have developed a software package for analysis and visualization of genetic variation in populations and reconstruction of strain variants from otherwise co-assembled sequences. Sequencing reads can be clustered by matching patterns of single nucleotide polymorphisms to generate predicted gene and protein variant sequences, identify conserved intergenic regulatory sequences, and determine the quantity and distribution of recombination events. Conclusion: The Strainer software, a first generation metagenomic bioinformatics tool, facilitates comprehension and analysis of heterogeneity intrinsic in natural communities. The program reveals the degree of clustering among closely related sequence variants and provides a rapid means to generate gene and protein sequences for functional, ecological, and evolutionary analyses.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of California, Berkeley, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Grant/Contract Number:: FG02-05ER64134

OSTI ID:: 1626345

Journal Information:: BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: 1 Vol. 8; ISSN 1471-2105

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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