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).
NOTE: this repository is intended for general use as it groups together the latest stable version of each tool. Developers (and only them) may want to check each tool's main repository.
ANGSD - Software for analyzing next generation sequencing data taking genotype uncertainty into account by working with genotype probabilities (instead of called genotypes). This is especially useful for low and medium depth data (Korneliussen et al., 2014). NOTE: this program is NOT developed by
ngsToolsso, if you have any questions about it or encounter any errors/bugs, please check its wiki or contact its authors.
ngsSim - Simple sequencing read simulator that can generate data for multiple populations with variable levels of depth, error rates, genetic variability, and individual inbreeding (Kim et al., 2011). It generates mapped reads and the corresponding genotype likelihoods, avoiding mapping uncertainty and being extremely fast.
ngsF - This program provides a method to estimate individual inbreeding coefficients using an EM algorithm (Vieira et al., 2013). These can provide insight into a population's mating system or demographic history and, more importantly, they can be used as a prior in ANGSD.
- ngsFst - Quantifying population genetic differentiation
- ngsCovar - Population structure via PCA (principal components analysis)
- ngs2dSFS - Estimate 2D-SFS from posterior probabilities of sample allele frequencies
- ngsStat - Estimate number of segregating sites, expected average heterozygosity and other nucleotide diversity indexes
ngsUtils - General tools to manipulate data produced by ngsTools.
- GetMergedGeno - Merge genotype posterior probabilities files
- GetSubGeno - Select a subset of genotype posterior probabilities files
- GetSubSim - Select a subset of simulated data files
- GetSwitchedGeno - Switch major/minor in genotype posterior probabilities files
ngsF-HMM is a program developed and written by F.G. Vieira to estimate per-individual inbreeding tracts using a two-state Hidden Markov Model (Vieira et al. 2016). It uses a probabilistic framework that takes the uncertainty of genotype's assignation into account; making it specially suited for low-quality or low-coverage datasets. It is not officially part of ngsTools so it must be installed separately.
ngsTools can be easily installed but some packages have some external dependencies:
gcc: >= 4.9.2 tested on Debian 7.8 (wheezy)
zlib: v1.2.7 tested on Debian 7.8 (wheezy)
gsl: v1.15 tested on Debian 7.8 (wheezy)
libbz2.so, required by htslib
liblzma.so, required by htslib
libcurl.so, required by angsd
- Optional (only needed for testing or auxilliary scripts):
To download ngsTools and its submodules use:
% git clone --recursive https://github.com/mfumagalli/ngsTools.git
If you prefer, although it is not recomended, you can download a zipped folder on the right side of this page ("Download ZIP").
To install these tools just run:
% cd ngsTools % make
To run the tests:
% make test
Executables are built into each tool directory in the repository. If you wish to clean all binaries and intermediate files:
% make clean
To get the latest version of ngsTools package:
% git pull % git submodule update
NOTE: for developers only: if you wish to make changes and update the whole package:
# in the modified repo # be sure to be on the master branch: git checkout master % git commit -a -m 'Local changes...' % git push # in ngsTools main repo % git commit -a -m 'Submodules updated' % git push # check that everything went well: % git status
All programs receive as input files produced by ANGSD. In general, these files can contain genotype likelihoods, genotype posterior probabilities, sample allele frequency posterior probabilities or an estimate of the SFS (Site Frequency Spectrum). Please refer to each tool's repository or the comprehensive Tutorial for more explanations and examples on how these tools work.
A tutorial on some analyses using ngsTools/ANGSD from BAM files can be found here. In this Tutorial, you will find how to filter your data, assess population structure and estimate summary statistics using these tools for low-depth data. For most cases, you will find all the information you need here.
Matteo Fumagalli & Filipe G. Vieira. Other programmers and developers: Tyler Lynderoth, Rasmus Nielsen. Some lines of code have been 'taken' (and adapted) from: Thorfinn Korneliussen, Anders Albrechtsen, Jacob Crawford, Dean Ousby, Martin Sykora, Leo Diaz.
If you want to be updated about new releases and fixed bugs please follow the github repository. For specific queries on the code, please use github features to raise issues. For informal questions (better on ngsTools and not ANGSD) feel free to contact Matteo Fumagalli, at mfumagalli82 [at] g mail [dot] com.
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 R Genome 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