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ngsPopGen

Several tools to perform population genetic analyses from NGS data:

  • ngsFst - Quantificate population genetic differentiation
  • ngsCovar - Population structure via PCA (principal components analysis)
  • ngs2dSFS - Estimate 2D-SFS from posterior probabilities of sample allele frequencies
  • ngsStat - Estimates number of segregating sites, expected average heterozygosity, and number of fixed differences and Dxy (if 2 populations provided).

IMPORTANT NOTE i):

In all analises involving 2 populations, input data must refer to the exact same sites. If they differ (e.g. because of different filtering), you must first get the overlapping subset of sites for both populations. To achieve this, you can follow instructions given in the tutorial (here).

IMPORTANT NOTE ii):

The use of folded data (spectrum or sample allele frequencies probabilities) is no longer supported. In case you do not have a reliable ancestral information, please use your reference sequence to polarise your data and follow all the steps as documented here. However, do not attempt to make any inference based on the resulting unfolded reference/non-reference based - site frequency spectrum.

IMPORTANT NOTE iii):

It may be practical to perform a non-stringent SNP calling before running the following analyses, in order to reduce the computational time and data dimensions. Moreover, this will reduce noise due to monomorphic sites, especially when the species' polymorphic rate is very low.

Installation

To install the entire package just download the source code:

% git clone https://github.com/mfumagalli/ngsPopGen.git

and run:

% cd ngsPopGen
% make

To run the tests (only if installed through ngsTools):

% make test

Executables are built into the main directory. If you wish to clean all binaries and intermediate files:

% make clean

However, we recommend to download and install the whole ngsTools package.

ngsFST

Program to estimate FST from NGS data. It computes expected genetic variance components and estimate per-site FST from those using methods-of-moments estimator. See Fumagalli et al. Genetics 2013 for more details. In input it receives sample allele frequencies likelihoods for each population and a 2D-SFS as a prior.

The output is a tab-separated text file. Each row represents a site. Columns are ordered as: A, AB, f, FST, Pvar; where A is the expectation of genetic variance between populations, AB is the expectation of the total genetic variance, f is the correcting factor for the ratio of expectations, FST is the per-site FST value, Pvar is the probability for the site of being variable.

Usage

Examples:

  • using a 2D-SFS as a prior, estimated using ngs2dSFS:

% ./ngsFST -postfiles pop1.saf pop2.saf -priorfile spectrum2D.txt -nind 20 20 -nsites 100000 -outfile pops.fst -verbose 0

Parameters:

  • -postfiles FILE_1 FILE_2: files with sample allele frequencies likelihoods for each population
  • -priorfile FILE: 2D-SFS to be used as a prior; you can use ngs2dSfs with parameter -relative set to 1
  • -nind INT: number of individuals for each population
  • -nsites INT: total number of sites; in case of a site subset this is the upper limit
  • -firstbase INT: in case of a site subset, this is the lower limit
  • -outfile FILE: name of the output file
  • -block_size INT: number of sites in each chunk (for memory reasons, increase it if you can use more RAM)
  • -verbose INT: level of verbosity

ngsCovar

Program to compute the expected correlation matrix between individuals from genotype posterior probabilities. It receives as input genotype posterior probabilities. It can receive in input also posterior probabilities of sample allele frequencies for computing the probability of each site to be variant.

Usage

Examples:

  • not calling genotypes but with SNP calling (preferred way under most circumstances):

% ./ngsCovar -probfile pop.geno -outfile pop.covar -nind 40 -nsites 100000 -block_size 20000 -call 0 -minmaf 0.05
  • not calling genotypes and no SNP calling (weighting by each site's probability of being variable; recommended if depth is extremely low):

% gunzip -f pop.saf.gz
% ./ngsCovar -probfile pop.geno -outfile pop.covar -nind 40 -nsites 100000 -block_size 20000 -call 0 -norm 0 -sfsfile pop.saf
  • calling genotypes (this is kept for compatibility but should not be used unless you have high-depth data, > 20X):

% ./ngsCovar -probfile pop.geno -outfile pop.covar -nind 40 -nsites 100000 -block_size 20000 -call 1

Parameters:

  • -probfile FILE: file with genotype posterior probabilities
  • -sfsfile FILE: file with per site allele frequency posterior probabilities
  • -nind INT: number of individuals
  • -nsites INT: total number of sites; in case of a site subset this is the upper limit
  • -offset INT: in case of a site subset, this is the lower limit
  • -genoquality FILE: text file with 'nsites' lines stating whether to use (1) or ignore (0) the site
  • -norm INT: normalization procedure; either "0" for no normalization (recommended if no SNP calling was performed), "1" for normalization by p(1-p) (Patterson et al, 2006) or "2" for normalization by 2p(1-p)
  • -minmaf FLOAT: ignore sites below this threshold of minor allele frequency
  • -call: call genotypes based on the maximum posterior probability
  • -outfile FILE: name of output file
  • -block_size INT: number of sites in each chunk (for memory reasons)
  • -verbose INT: level of verbosity

ngs2dSFS

Program to estimate 2D-SFS from posterior probabilities of sample allele frequencies. Output file reports the occurrence of sites at distinct joint allele frequencies. This spectrum is a (2N1+1)x(2N2+2) matrix with N1 and N2 number of individuals at the two populations. Please note that cells are zero-based ordered. As an example, value reported in the cell [4,3] represents the frequency of sites with allele frequency 3 and 2 at population 1 and 2 respectively.

Example:

% ./ngs2dSFS -postfiles pop1.saf pop2.saf -outfile spectrum.txt -relative 1 -nind 20 20 -nsites 100000

Parameters:

  • -postfiles FILE: file with sample allele frequency posterior probabilities (or likelihoods) for each population
  • -nind INT: number of individuals
  • -nsites INT: total number of sites; in case of a site subset this is the upper limit
  • -offset INT: in case of a site subset, this is the lower limit
  • -outfile FILE: name of output file
  • -maxlike INT: how to compute the MLE; either, "1" (preferred) to sum across sites' joint allele frequency, or "0" to sum of the products of likelihoods
  • -relative INT: whether input are absolute counts of sites with a specific joint allele frequency (0) or relative frequencies (1)
  • -block_size INT: number of sites for each chunk (for memory efficiency only)

ANGSD can compute a ML estimate of the 2D-SFS which should be preferred when many sites are available. However, ANGSD output file should be transformed (from log to un-log and from space-separated to tab-separated) before being used in ngsFST.

ngsStat

Program to compute estimates of the number of segregating sites, the expected average heterozygosity, and the number of fixed differences (if 2 populations data is provided). It receives as input sample allele frequency posterior probabilities (from ANGSD) from 1 or 2 populations. Output is a text file with columns: start, end, segregating sites (pop 1), heterozygosity (pop 1), segregating sites (pop 2), heterozygosity (pop 2), fixed differences, dxy.

Example:

  • 2 populations, sliding windows of 100 sites (latter recommended only if no missing data is present, however in most cases you will have some missing sites so this command should not be used):

./ngsStat -npop 2 -postfiles pop1.saf pop2.saf -nsites 1000 -iswin 1 -nind 10 10 -outfile pops.stat -verbose 0 -block_size 100
  • 1 populations, sliding windows of 100 sites (latter recommended only if no missing data is present, so again in many cases this should not be used):

./ngsStat -npop 1 -postfiles pop1.saf -nsites 1000 -iswin 1 -nind 10 -outfile pops.stat -block_size 100
  • 1 population, values estimated at each site (recommended in case of missing data, and then the computation of values in sliding windows will be performed using the R script provided):

./ngsStat -npop 1 -postfiles pop1.saf -nsites 1000 -iswin 0 -nind 10 -outfile pops.stat

Parameters:

  • -npop INT: number of populations (should be the first one to be specified)
  • -postfiles: file with sample allele frequency posterior probabilities for each population
  • -nind INT: number of individuals
  • -nsites INT: total number of sites; in case of a site subset this is the upper limit
  • -firstbase INT: in case of a site subset, this is the lower limit
  • -iswin INT: if set to 1, chuncks are considered non-overlapping sliding-windows
  • -outfile FILE: name of output file
  • -block_size INT: number of sites in each chunk (for memory reasons)
  • -verbose INT: level of verbosity

Further examples can be found in the Tutorial.