Ontology highlight
ABSTRACT: Background
Terabyte-scale collections of string-encoded data are expected from consortia efforts such as the Human Microbiome Project http://nihroadmap.nih.gov/hmp. Intra- and inter-project data similarity searches are enabled by rapid k-mer matching strategies. Software applications for sequence database partitioning, guide tree estimation, molecular classification and alignment acceleration have benefited from embedded k-mer searches as sub-routines. However, a rapid, general-purpose, open-source, flexible, stand-alone k-mer tool has not been available.Results
Here we present a stand-alone utility, Simrank, which allows users to rapidly identify database strings the most similar to query strings. Performance testing of Simrank and related tools against DNA, RNA, protein and human-languages found Simrank 10X to 928X faster depending on the dataset.Conclusions
Simrank provides molecular ecologists with a high-throughput, open source choice for comparing large sequence sets to find similarity.
SUBMITTER: DeSantis TZ
PROVIDER: S-EPMC3097142 | biostudies-literature | 2011 Apr
REPOSITORIES: biostudies-literature
DeSantis Todd Z TZ Keller Keith K Karaoz Ulas U Alekseyenko Alexander V AV Singh Navjeet N S NN Brodie Eoin L EL Pei Zhiheng Z Andersen Gary L GL Larsen Niels N
BMC ecology 20110427
<h4>Background</h4>Terabyte-scale collections of string-encoded data are expected from consortia efforts such as the Human Microbiome Project http://nihroadmap.nih.gov/hmp. Intra- and inter-project data similarity searches are enabled by rapid k-mer matching strategies. Software applications for sequence database partitioning, guide tree estimation, molecular classification and alignment acceleration have benefited from embedded k-mer searches as sub-routines. However, a rapid, general-purpose, ...[more]