Summary: Next-Generation Sequencing (NGS) technologies produce short reads that are either de novo assembled or mapped to a reference genome. Genotypes and or SNPs 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. < 10×, 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 NGS data. The methods implemented in these programs do not rely on SNP nor genotype calling, and are particularly suitable for low sequencing depth data. Availability: Programs included in ngsTools are implemented in C/C++ and are freely available for non-commercial use at https: //github.com/mfumagalli/ngsTools.
['Matteo Fumagalli', 'Filipe G. Vieira', 'Tyler Linderoth', 'Rasmus Nielsen']
ngsTools: methods for population genetics analyses from next-generation sequencing data