#ngsTools

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, 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, along with high error rates stemming from base calling and mapping errors, cause SNP (Single Nucleotide Polymorphism) and genotype calling from NGS data to be associated with considerable statistical uncertainty. Recently, 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 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. 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 may want to check each tool's main repository.

##Packages

• 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.

• ngsPopGen - Several tools to perform population genetic analyses from NGS data (Fumagalli et al., 2013, Fumagalli, 2013).

• 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 number of fixed differences (if data from 2 populations are provided).

• ngsUtils - General tools to manipulate data.

• GetMergedGeno - Merge genotype posterior probabilities files

• GetSubGeno - Select a subset of genotype posterior probabilities files

• GetSubSfs - Select a subset of sample allele frequency posterior probabilities files

• GetSubSim - Select a subset of simulated data files

• GetSwitchedGeno - Switch major/minor in genotype posterior probabilities files

• GetSwitchedSfs - Switch major/minor in sample allele frequency posterior probabilities files

• ngsDist is a program that estimates genetic distances for phylogenetic analyses from genotype posterior probabilities. It is not yet officially included in ngsTools, and therefore must be installed separately.

ngsTools can be easily installed but some packages have some external dependencies:

• zlib: v1.2.7 tested on Debian 7.8 (wheezy)
• gsl : v1.15 tested on Debian 7.8 (wheezy)
• md5sum: only needed for make test

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

Executables are built into each tool directory in the repository. If you wish to clean all binaries and intermediate files:

% make clean

NOTE: We now provide a direct link to ANGSD's repository on github. We anyway recommend users to refer to its official website here to ensure that the latest version is in use.

To get the latest version of ngsTools package:

% git pull
% git submodule update

NOTE for developers: 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 for more explanations and examples on how these tools work.

A short tutorial on how some basic analyses using ngsTools from BAM files can be found here. For most cases, here you will find all the information you need.

Matteo Fumagalli & Filipe G. Vieira. Other programmers and developers: Tyler Lynderoth, Rasmus Nielsen. Some lines of code have been 'taken' from: Thorfinn Korneliussen, Anders Albrechtsen, Jacob Crawford.

If you want to be updated about new releases and fixed bugs please consider joining the ngsTools official google group at https://groups.google.com/forum/#!forum/ngstools-user. For informal questions (not about ANGSD, only for ngsTools) feel free to contact Matteo Fumagalli, at mfumagalli82 [at] gmail [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.
2014 May 15;30(10):1486-7. doi: 10.1093/bioinformatics/btu041. Epub 2014 Jan 23.
http://www.ncbi.nlm.nih.gov/pubmed/24458950

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. [Epub ahead of print]
http://www.ncbi.nlm.nih.gov/pubmed/25420514

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. doi: 10.1371/journal.pone.0037558. Epub 2012 Jul 24.

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. doi: 10.1534/genetics.113.154740. Epub 2013 Aug 26.

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. doi: 10.1101/gr.157388.113. Epub 2013 Aug 15.

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. doi: 10.1126/science.1190371.

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. [Epub ahead of print]

Assessing the effect of sequencing depth and sample size in population genetics inferences.
Fumagalli M.
PLoS One. 2013 Nov 18;8(11):e79667. doi: 10.1371/journal.pone.0079667.