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micRocounter: Microsatellite Characterization in Genome Assemblies
Author(s) -
Johnathan Lo,
Michelle M. Jonika,
Heath Blackmon
Publication year - 2019
Publication title -
g3 genes genomes genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.468
H-Index - 66
ISSN - 2160-1836
DOI - 10.1534/g3.119.400335
Subject(s) - software portability , genome , software , computer science , benchmark (surveying) , coding (social sciences) , microsatellite , computational biology , population , flexibility (engineering) , sequence assembly , data mining , biology , genetics , programming language , gene , mathematics , geography , allele , statistics , demography , transcriptome , gene expression , sociology , geodesy
Microsatellites are repetitive DNA sequences usually found in non-coding regions of the genome. Their quantification and analysis have applications in fields from population genetics to evolutionary biology. As genome assemblies become commonplace, the need for software that can facilitate analyses has never been greater. In particular, R packages that can analyze genomic data are particularly important since this is one of the most popular software environments for biologists. We created an R package, micRocounter, to quantify microsatellites. We have optimized our package for speed, accessibility, and portability, making the automated analysis of large genomic data sets feasible. Computationally intensive algorithms were built in C++ to increase speed. Tests using benchmark datasets show a 200-fold improvement in speed over existing software. A moderately sized genome of 500 Mb can be processed in under 50 sec. Results are output as an object in R increasing accessibility and flexibility for practitioners.

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