ngsTools: methods for population genetics analyses from next-generation sequencing data.
CONCLUSIONS
Next-generation sequencing technologies produce short reads that are either de novo assembled or mapped to a reference genome. Genotypes and/or single-nucleotide polymorphisms are then determined from the read composition at each site, which become the basis for many downstream analyses. However, for low sequencing depths, e.g. , there is considerable statistical uncertainty in the assignment of genotypes because of random sampling of homologous base pairs in heterozygotes and sequencing or alignment errors. Recently, several probabilistic methods have been proposed to account for this uncertainty and make accurate inferences from low quality and/or coverage sequencing data. We present ngsTools, a collection of programs to perform population genetics analyses from next-generation sequencing data. The methods implemented in these programs do not rely on single-nucleotide polymorphism or genotype calling and are particularly suitable for low sequencing depth data.


BACKGROUND
Programs included in ngsTools are implemented in C/C++ and are freely available for noncommercial use at https://github.com/mfumagalli/ngsTools.


BACKGROUND
mfumagalli82@gmail.com


BACKGROUND
Supplementary materials are available at Bioinformatics online.
Department of Integrative Biology, Department of Statistics, University of California, Berkeley, CA 94720, USA and Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark.
Matteo
M
Fumagalli
Filipe G
FG
Vieira
Tyler
T
Linderoth
Rasmus
R
Nielsen
D000465:Algorithms; D005828:Genetics, Population; D016678:Genome; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D020641:Polymorphism, Single Nucleotide; D012984:Software