NGS (Next-Generation Sequencing) technologies have revolutionised population genetic research by enabling unparalleled data collection from the genomes or subsets of genomes from many individuals. Current technologies produce short fragments of sequenced DNA called reads that are either de novo assembled or mapped to a pre-existing reference genome. This leads to chromosomal positions being sequenced a variable number of times across the genome. This parameter is usually referred to as the sequencing depth. Individual genotypes are then inferred from the proportion of nucleotide bases covering each site after the reads have been aligned.

Low sequencing depth and high error rates stemming from base calling and mapping errors can cause SNP (Single Nucleotide Polymorphism) and genotype calling from NGS data to be associated with considerable statistical uncertainty. Probabilistic models, which take these errors into account, have been proposed to accurately assign genotypes and estimate allele frequencies (e.g. Nielsen et al., 2012; for a review Nielsen et al., 2011).

ngsTools is a collection of programs for population genetics analyses from NGS data, taking into account data statistical uncertainty. The methods implemented in these programs do not rely on SNP or genotype calling, and are particularly suitable for low sequencing depth data. An application note illustrating its application has published (Fumagalli et al., 2014).

Usage

Version	module name
2019-06-24	ngstools/2019-06-24

Links

For more information visit https://github.com/mfumagalli/ngsTools

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