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ngstools

Description

ngstools website

ngsTools is a collection of programs for population genetics analyses from NGS data, taking into account its 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.

Environment Modules

Run module spider ngstools to find out what environment modules are available for this application.

Environment Variables

  • HPC_NGSTOOLS_DIR - installation directory
  • HPC_NGSTOOLS_BIN - executable directory

Citation

If you publish research that uses ngstools you have to cite it as follows:

ngsTools package can be cited as:

  • Fumagalli, M., Vieira, F. G., Linderoth, T., & Nielsen, R. (2014). ngsTools: methods for population genetics analyses from next-generation sequencing data. Bioinformatics, 30(10), 1486–1487. https://doi.org/10.1093/bioinformatics/btu041

ANGSD can be cited as:

  • Korneliussen, T., Albrechtsen, A., & Nielsen, R. (2014). ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics, 15(1), 356. https://doi.org/10.1186/s12859-014-0356-4
  • Nielsen, R., Korneliussen, T., Albrechtsen, A., Li, Y., & Wang, J. (2012). SNP calling, genotype calling, and sample allele frequency estimation from New-Generation Sequencing data. PLoS One, 7(7), e37558. https://doi.org/10.1371/journal.pone.0037558

FST and PCA methods can be cited as:

  • Fumagalli, M., Vieira, F. G., Korneliussen, T. S., Linderoth, T., Huerta-Sánchez, E., Albrechtsen, A., & Nielsen, R. (2013). Quantifying Population Genetic Differentiation from Next-Generation Sequencing Data. Genetics, 195(3), 979–992. https://doi.org/10.1534/genetics.113.154740

Inbreeding estimation can be cited as:

  • Vieira, F. G., Fumagalli, M., Albrechtsen, A., & Nielsen, R. (2013). Estimating inbreeding coefficients from NGS data: impact on genotype calling and allele frequency estimation. Genome Research, 23(11), 1852–1861. https://doi.org/10.1101/gr.157388.113
  • Vieira, F. G., Albrechtsen, A., & Nielsen, R. (2016). Estimating IBD tracts from low coverage NGS data. Bioinformatics, 32(13), 2096–2102. https://doi.org/10.1093/bioinformatics/btw142

Nucleotide diversity estimates from NGS data implemented here have been proposed in:

  • Yi, X., Liang, Y., Huerta-Sanchez, E., Jin, X., Cuo, Z. X., Pool, J. E., Xu, X., Jiang, H., Vinckenbosch, N., Korneliussen, T. S., Zheng, H., Liu, T., He, W., Li, K., Luo, R., Nie, X., Wu, H., Zhao, M., Cao, H., ... Wang, J. (2010). Sequencing of 50 human exomes reveals adaptation to high altitude. Science, 329(5987), 75–78. https://doi.org/10.1126/science.1190371
  • Korneliussen, T. S., Moltke, I., Albrechtsen, A., & Nielsen, R. (2013). Calculation of Tajima's D and other neutrality test statistics from low depth next-generation sequencing data. BMC Bioinformatics, 14(289). https://doi.org/10.1186/1471-2105-14-289
  • Fumagalli, M. (2013). Assessing the effect of sequencing depth and sample size in population genetics inferences. PLoS ONE, 8(11), e79667. https://doi.org/10.1371/journal.pone.0079667

Estimation of genetic distances have been described here:

Vieira, F. G., Lassalle, F., Korneliussen, T. S., & Fumagalli, M. (2016). Improving the estimation of genetic distances from next-generation sequencing data. Biological Journal of the Linnean Society, 117(1), 139–149. https://doi.org/10.1111/bij.12511

Categories

biology, ngs