MicNeSs: genotyping microsatellite loci from a collection of (NGS) reads

• Published in: Molecular ecology resources 2016-03, Vol.16 (2), p.524-533
• Main Authors: Suez, Marie, Behdenna, Abdelkader, Brouillet, Sophie, Graça, Paula, Higuet, Dominique, Achaz, Guillaume
• Format: Article
• Language: English
• Subjects: Algorithms; Biodiversity; Computational Biology - methods; Drosophila; genotyping; Genotyping Techniques - methods; High-Throughput Nucleotide Sequencing; Life Sciences; microsatellite loci; Microsatellite Repeats; next-generation sequencing (NGS); Python
• ISSN: 1755-098X; 1755-0998
• DOI: 10.1111/1755-0998.12467

Microsatellites are widely used in population genetics to uncover recent evolutionary events. They are typically genotyped using capillary sequencer, which capacity is usually limited to 9, at most 12 loci for each run, and which analysis is a tedious task that is performed by hand. With the rise of next‐generation sequencing (NGS), a much larger number of loci and individuals are available from sequencing: for example, on a single run of a GS Junior, 28 loci from 96 individuals are sequenced with a 30X cover. We have developed an algorithm to automatically and efficiently genotype microsatellites from a collection of reads sorted by individual (e.g. specific PCR amplifications of a locus or a collection of reads that encompass a locus of interest). As the sequencing and the PCR amplification introduce artefactual insertions or deletions, the set of reads from a single microsatellite allele shows several length variants. The algorithm infers, without alignment, the true unknown allele(s) of each individual from the observed distributions of microsatellites length of all individuals. MicNeSs, a python implementation of the algorithm, can be used to genotype any microsatellite locus from any organism and has been tested on 454 pyrosequencing data of several loci from fruit flies (a model species) and red deers (a nonmodel species). Without any parallelization, it automatically genotypes 22 loci from 441 individuals in 11 hours on a standard computer. The comparison of MicNeSs inferences to the standard method shows an excellent agreement, with some differences illustrating the pros and cons of both methods.