AI Article Synopsis

  • Microsatellites are valuable genetic markers for studying populations, and new high-throughput sequencing methods make finding these markers faster but require advanced algorithms to manage various challenges, like sequencing errors.
  • A new framework called HighSSR has been developed for microsatellite genotyping that outperforms existing tools and effectively maps loci against reference genomes while managing pooled libraries and designing PCR primers.
  • HighSSR is available for use and aims to streamline microsatellite analysis as sequencing costs decrease, making this technology more accessible for research.

Article Abstract

Motivation: Microsatellites are among the most useful genetic markers in population biology. High-throughput sequencing of microsatellite-enriched libraries dramatically expedites the traditional process of screening recombinant libraries for microsatellite markers. However, sorting through millions of reads to distill high-quality polymorphic markers requires special algorithms tailored to tolerate sequencing errors in locus reconstruction, distinguish paralogous loci, rarify raw reads originating from the same amplicon and sort out various artificial fragments resulting from recombination or concatenation of auxiliary adapters. Existing programs warrant improvement.

Results: We describe a microsatellite prediction framework named HighSSR for microsatellite genotyping based on high-throughput sequencing. We demonstrate the utility of HighSSR in comparison to Roche gsAssembler on two Roche 454 GS FLX runs. The majority of the HighSSR-assembled loci were reliably mapped against model organism reference genomes. HighSSR demultiplexes pooled libraries, assesses locus polymorphism and implements Primer3 for the design of PCR primers flanking polymorphic microsatellite loci. As sequencing costs drop and permit the analysis of all project samples on next-generation platforms, this framework can also be used for direct simple sequence repeats genotyping.

Availability: http://code.google.com/p/highssr/

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4439519PMC
http://dx.doi.org/10.1093/bioinformatics/bts524DOI Listing

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