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 done 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 fruitflies (a model species) and red deers (a non-model 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. This article is protected by copyright. All rights reserved.
@article{suez_micness:_2015,
Author = {Suez, Marie and Behdenna, Abdelkader and Brouillet,
Sophie and Graça, Paula and Higuet, Dominique and
Achaz, Guillaume},
Title = {{MicNeSs}: genotyping microsatellite loci from a
collection of ({NGS}) reads},
Journal = {Molecular Ecology Resources},
Pages = {n/a--n/a},
Keywords = {genotyping, microsatellite loci, Next Generation
Sequencing (NGS)},
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
done 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 fruitflies (a
model species) and red deers (a non-model 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. This article is protected by copyright. All
rights reserved.},
copyright = {This article is protected by copyright. All rights
reserved.},
doi = {10.1111/1755-0998.12467},
issn = {1755-0998},
language = {en},
month = sep,
shorttitle = {{MicNeSs}},
url = {http://onlinelibrary.wiley.com/doi/10.1111/1755-0998.12467/abstract},
urldate = {2015-10-07},
year = 2015
}
