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ngstools website  

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.

Environment Modules

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

System Variables

  • HPC_NGSTOOLS_DIR - installation directory
  • HPC_NGSTOOLS_BIN - executable directory


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

Expand this section to view citation details.

ngsTools package can be cited as:

  • ngsTools: methods for population genetics analyses from next-generation sequencing data.Fumagalli M, Vieira FG, Linderoth T, Nielsen R. Bioinformatics. 2014 May 15;30(10):1486-7

ANGSD can be cited as:

  • ANGSD: Analysis of Next Generation Sequencing Data. Korneliussen T, Albrechtsen A, Nielsen R BMC Bioinformatics. 2014 Nov 25;15(1):356
  • SNP calling, genotype calling, and sample allele frequency estimation from New-Generation Sequencing data. Nielsen R, Korneliussen T, Albrechtsen A, Li Y, Wang J PLoS One. 2012;7(7):e37558

FST and PCA methods can be cited as:

  • Quantifying Population Genetic Differentiation from Next-Generation Sequencing Data. Fumagalli M, Vieira FG, Korneliussen TS, Linderoth T, Huerta-Sánchez E, Albrechtsen A, Nielsen R Genetics. 2013 Nov;195(3):979-92

Inbreeding estimation can be cited as:

  • Estimating inbreeding coefficients from NGS data: impact on genotype calling and allele frequency estimation. Vieira FG, Fumagalli M, Albrechtsen A, Nielsen RGenome Res. 2013 Nov;23(11):1852-61
  • Estimating IBD tracts from low coverage NGS data Vieira FG, Albrechtsen A and Nielsen R Bioinformatics. 2016; 32:2096-2102

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

  • Sequencing of 50 human exomes reveals adaptation to high altitude. Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZX, Pool JE, Xu X, Jiang H, Vinckenbosch N, Korneliussen TS, Zheng H, Liu T, He W, Li K, Luo R, Nie X, Wu H, Zhao M, Cao H, Zou J, Shan Y, Li S, Yang Q, Asan, Ni P, Tian G, Xu J, Liu X, Jiang T, Wu R, Zhou G, Tang M, Qin J, Wang T, Feng S, Li G, Huasang, Luosang J, Wang W, Chen F, Wang Y, Zheng X, Li Z, Bianba Z, Yang G, Wang X, Tang S, Gao G, Chen Y, Luo Z, Gusang L, Cao Z, Zhang Q, Ouyang W, Ren X, Liang H, Zheng H, Huang Y, Li J, Bolund L, Kristiansen K, Li Y, Zhang Y, Zhang X, Li R, Li S, Yang H, Nielsen R, Wang J, Wang J Science. 2010 Jul 2;329(5987):75-8
  • Calculation of Tajima's D and other neutrality test statistics from low depth next-generation sequencing data. Korneliussen TS, Moltke I, Albrechtsen A, Nielsen R BMC Bioinformatics. 2013 Oct 2;14(1):289
  • Assessing the effect of sequencing depth and sample size in population genetics inferences. Fumagalli M PLoS One. 2013 Nov 18;8(11):e79667

Estimation of genetic distances have been described here:

  • Improving the estimation of genetic distances from Next-Generation Sequencing data. Vieira FG, Lassalle F, Korneliussen TS, Fumagalli M Biological Journal of the Linnean Society. Special Issue: Collections-Based Research in the Genomic Era. 117(1):139–149