Fumagalli, M;
Vieira, FG;
Linderoth, T;
Nielsen, R;
(2014)
ngsTools: methods for population genetics analyses from next-generation sequencing data.
Bioinformatics
, 30
(10)
1486 - 1487.
10.1093/bioinformatics/btu041.
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Abstract
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.
Type: | Article |
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Title: | ngsTools: methods for population genetics analyses from next-generation sequencing data |
Location: | England |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1093/bioinformatics/btu041 |
Publisher version: | http://dx.doi.org/10.1093/bioinformatics/btu041 |
Language: | English |
Additional information: | © The Author 2014. Published by Oxford University Press. All rights reserved. PMCID: PMC4016704 |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences |
URI: | https://discovery.ucl.ac.uk/id/eprint/1433139 |
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