The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ngsTools: methods for population genetics analyses from next-generation sequencing data
Open accessAbstract
Summary: 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. Availability: Programs included in ngsTools are implemented in C/C++ and are freely available for noncommercial use at https://github.com/mfumagalli/ngsTools. Contact: mfumagalli82@gmail.com Supplementary Information: Supplementary materials are available at Bioinformatics online.
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Nielsen, R., Korneliussen, T., Albrechtsen, A., Li, Y., & Wang, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037558 · Cited by 424 · cited 2×
“Recently, probabilistic models that take these errors into account have been proposed to accurately assign genotypes and estimate allele frequencies (e.g. Nielsen et al., 2012).”
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Cited by
ANGSD: Analysis of Next Generation Sequencing Data
Korneliussen, T. S., Albrechtsen, A., & Nielsen, R. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0356-4 · Cited by 2,982
Self-citationInferring Population Structure and Admixture Proportions in Low-Depth NGS Data
Meisner, J., & Albrechtsen, A. (2018) · Genetics · doi:10.1534/genetics.118.301336 · Cited by 673
Genomic basis of geographical adaptation to soil nitrogen in rice
Liu, Y., Wang, H., Jiang, Z., Wang, W., Xu, R., Wang, Q., Zhang, Z., Li, A., Liang, Y., Ou, S., et al. (2021) · Nature · doi:10.1038/s41586-020-03091-w · Cited by 468
Population genomics reveal recent speciation and rapid evolutionary adaptation in polar bears
Liu, S., Lorenzen, E. D., Fumagalli, M., Li, B., Harris, K., Xiong, Z., Zhou, L., Korneliussen, T. S., Somel, M., Babbitt, C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.03.054 · Cited by 411
Self-citationAncient genomes revisit the ancestry of domestic and Przewalski’s horses
Gaunitz, C., Fages, A., Hanghøj, K., Albrechtsen, A., Khan, N., Schubert, M., Seguin-Orlando, A., Owens, I. J., Felkel, S., Bignon-Lau, O., et al. (2018) · Science · doi:10.1126/science.aao3297 · Cited by 298
Metadata
- Authors
- Fumagalli, M., Vieira, F. G., Linderoth, T., & Nielsen, R.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-01-23
- Volume
- 30
- Issue
- 10
- Pages
- 1486-1487
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2142138981
- Cited by
- 251
- References
- 1
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- bronze
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