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

Abstract

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.

High‐throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low‐quantity and degraded DNA

“…Alternative approaches can also be used, for example Suez et al . (). Whatever the pipeline, appropriate loci selection is fundamental to favour complete bioinformatic automation; for example, loci producing high levels of artefactual sequences may require significant manual verifications and should be avoided.…”

“…Suez et al . ; but see Zhan et al . for a comparison between this distribution based approach and a sequence string based approach).…”

Digital fragment analysis of short tandem repeats by high‐throughput amplicon sequencing

“…Many of these programs make an explicit assumption that alleles are separated by the length of their longest repeat unit (e.g., Suez et al. ), but this is not necessarily the case (e.g., see alleles 234A and 234B for locus Oz43b, Table B). At the moment there is no sequence‐aware allele‐calling program that we are aware of, so visual allele calling is a current limitation of this approach.…”

“…; Suez et al. ), and Suez et al. () showed that sequencing data are quantitatively comparable to capillary electrophoresis data, but the question remains, “is it worth adopting the amplicon sequencing approach instead of capillary electrophoresis to genotype microsatellites?”…”

De novo assembly and characterization of the Hucho taimen transcriptome

“…The data analysis pipeline contained two main self‐developed programs: The first one demultiplexed the reads to each locus and sample with the sequences of the primer loci and index bases of samples added by PCR (Figure S1), and the second one identified alleles via a modified algorithm of MEGASAT (Zhan et al., 2016). The program is distinguished from previous software (Suez et al., 2015; Zhan et al., 2016) because it adopts the Sputnik algorithm (La Rota et al., 2005) to search the microsatellite repeat array. This characteristic enabled the program to function without background information, such as microsatellite sequences and repeat array flank sequences, and only primer sequences are needed to classify reads to loci and index bases for demultiplexing the reads to samples.…”