python script.py -hThere is no system installation required. Just download this entire repository using the green "Code" button at the top of this page, or with the bash command git clone https://github.com/simonhmartin/genomics_general.git. If you prefer to move scripts to the directory where you run them, bear in mind that many of these scripts require the script genomics.py to be present in the same directory (or on your PYTHONPATH). The easiest is just to leave them in the main directory and specify the full path to the script when running it from elsewhere.
The only dependency is numpy.
Most of the scripts now run in python 3. Some are still written for puthon 2, but those will be updated soon.
Most of my scripts use a processed .vcf format that I call .geno. This looks something like this:
#CHROM POS ind1 ind2 ind3
scaffold1 1 A/A A/G G|A
scaffold1 1 N/N T/T T|CMissing data is denoted as N, and phased and unphased genotypes are shown conventionally with | and /.
The script parseVCF.py in the VCF_processing directory, will convert vcf to this format. It has various options for filtering based on read depth, genotype quality or any other flag in the FORMAT column of the vcf.
python VCF_processing/parseVCF.py -i input.vcf.gz --skipIndels --minQual 30 --gtf flag=DP min=5 max=50 -o output.geno.gzYou can read more about this script in the VCF_processing directory.
If you vcf file was not already filtered, or you would like to filter further. The script filtergenotypes.py has many options for filtering. Some examples include:
N/N genotypes at a site (--minCalls)--minAlleles and --maxAlleles)--minVarCount)--thinDist)It requires the script genomics.py to be present in the same directory, or in your Python path.
python filterGenotypes.py --threads 4 -i input.geno.gz -o output.geno.gz --minAlleles 2 --minCalls 10 --thinDist 1000python filterGenotypes.py -h will print a full list of command options.
By default, this script assumes that the .geno input file is is encoded as diploid genotypes with a phase operator (/ or |):
scaffold1 1 A/A G/G G|AYou can specify a different input formats using the -if, but this is not recommended.
You can also specify various putput formats using -of.
| Output format | Description | Example |
|---|---|---|
phased (default) |
Alleles separates by a phase operator. This doesn't mean the phase is known, just that it is indicated | A/A G/G G\|A |
diplo |
For diploids only. Genotypes are single bases denoting the diploid genotype, using ambiguity codes for heterozygotes | A G R |
alleles |
as above but without the phase operator | AA GG GA |
randomAllele |
Randomly pick one allele per individual | A G A |
coded |
Coded numerically as in the VCF | 0/0 1/1 1\|0 |
bases |
Separate the alleles for each individual into different columns and also give different headers for each | A A G G G A |
counts |
Count of the minor allele in each individual | 0 2 1 |
The script popgenWindows.py computes some standard population genomic statistics in sliding windows: pi, FST and DXY. It requires the script genomics.py to be present in the same directory, or in your Python path.
python popgenWindows.py -w 50000 -m 5000 -g input.geno.gz -o output.csv.gz -f phased -T 5 -p popA A1,A2,A3,A4 -p popB B1,B2,B3,B4,B6,B6 -p popC -p popD --popsFile pops.txt python popgenWindows.py -h Will print a full list of command arguments.
Input is a .geno file as shown above. This can be gzipped (.geno.gz).
Output is a .csv. If you add .gz it will be gzipped.
Genotype encoding is indicated by the -f flag. -f phased is normally used, see the table above. Other options are -f haplo for haploid data (although phased will also interpret haploid data correctly), -f diplo and f pairs which is like the alleles output in the table above, but assumes diplid data.
There are three options for defining the windows to be analysed, using the --windType argument.
| Wndow Type | Description |
|---|---|
coordinate |
Windows will cover a fixed range in the genome, which is defined as the window size. If there is missing data, this can lead to variable numbers of sites used for each window. |
sites |
Each window will have the same number of sites. If there is missing data, this can lead to different absolute sizes for windows in terms of genome coordinates. |
predefined |
This will analyse predefined windows provided using the --windCoords flag. |
--popsFile, which has two columns: the first gives sample names and teh second gives population name:C1 popC
C2 popC
C3 popC
C4 popC
D1 popD
D2 popD
D3 popD
D4 popDThe most common source of errors here involve the -m (--minSites) flag. If you are useing coordinate windows and have any sites with missing data, then -m must be set to a value smaller than the window size. If you have reduced representation data such as RADseq, you will need a much lower -m value (more like 1% of the window size or even less).
If some samples are haploid and others are diploid, you can use one of the diploid formats, but indicate that certain samples are haploid by listing them after the --haploid flag. The script will force them to have haploid genotyps, and any apparently heterozygous genotype will be converted to N.
The script can run on multiple cores (-T flag). Try different numbers, as using too many can slow the script down (due to the difficulty in sorting the outputs coming from the different cores).
The script freq.py computes allele frequencies at each site, either for each base, the minor allele, or the derived allele (if an outgroup is provided). It requires the script genomics.py to be present in the same directory, or in your Python path.
python freq.py -g input.geno.gz -p pop1 -p pop2 --popsFile populations.txt --target derived --threads 10Input is a .geno file as shown above. This can be gzipped (.geno.gz).
Output is a .csv. If you add .gz it will be gzipped.
If you do not include the --target option, the script will export the count (i.e. an integer) of each base (A, C, G, T) for ech population.
If you set --target, you have two options
| Wndow Type | Description |
|---|---|
--target minor |
Export the frequency of the minor (rarer) allele at each site (based on all included individuals). This can be at most 0.5 for the whole dataset, but can exceed 0.5 in certain populations |
--target derived |
Export the frequency of the derived allele. NOTE: this assumes that the last population listed with -p is the outgroup. |
The script sfs.py computes site frequency spectrum (SFS, also called the allele frequency spectrum) from input variants. It requires the script genomics.py to be present in the same directory, or in your Python path.
The input for sfs.py is produced by freq.py (see above). These two scripts can be combined with a pipe.
#1D folded (derived allele) sfs
python freq.py -g input.geno.gz -f phased --threads 10 -p pop1 -p pop2 -p outgroup | \
python sfs.py --inputType baseCounts -p pop1 -p pop2 -p outgroup \
--FSpops pop1 pop2 --polarized --subsample 10 10 --pref mydata. --suff .subsample10.sfs
#2D unfolded (minor allele) SFS
python freq.py -g input.geno.gz -f phased --threads 10 -p pop1 -p pop2 | \
python sfs.py --inputType baseCounts -p pop1 -p pop2 \
--FSpops pop1 pop2 --doPairs --subsample 10 10 --pref mydata. --suff .subsample10.sfsInput is a .geno file as shown above. This can be gzipped (.geno.gz).
Output is a table giving the number of sites with each allele count.
--polarized will produce an unfolded SFS assuming the final population named is the outgroup
--doPairs, --doTrios and doQuartets will also export the 2D, 3D and 4D SFS for all pairs, trios or quartets of populations.
The script distMat.py computes a distance matrix among all pairs of individuals. This can be computed either for the entire input file or in windows, as in the popgenWindows script above. This works for samples of any ploidy or mix of ploidies. For ploidy > 1, the pairwise diatance will be the average diatance among all haplotypes in the two individuals.
python distMat.py -g input.geno.gz -f phased --windType cat -o output.distpython distMat.py -h will print a list of command options.
This script shares several command arguments with popgenWindows.py. And input formats are the same. Please see the notes for that script above.
Output format has three otptions: raw, phylip and nexus.
To make a single matrix for the entire input file (i.e. not making windows and ignoring scaffold bounaries), use --windType cat
If using --windType cat, the entire input file will be read into memory. This often leads to RAM issues for large files. One option is to filter your data hard before doing this analysis using filterGenotypes.py.
To make separate matrices for windows, use one of the window type options described above. There will still be a single output file, but with separate matrices separated by blank lines.
The script ABBABABAwindows.py performs analyses described in Martin et al. 2015, MBE, compurting the D statistic and f estimators in windows across the genome. Like the script above, it requires genomics.py.
python ABBABABAwindows.py -g /zoo/disk1/shm45/vcf/set62/set62.chr21.DP5GQ30.AN100MAC1.diplo.gz -f phased -o output.csv -w 100000 -m 100 -s 100000 -P1 A -P2 B -P3 C -O D -T 10 --minData 0.5 --popsFile pops.txt --writeFailedWindows --polarize &python ABBABABAwindows.py -h Will print a full list of command arguments.
This script shares several command arguments with popgenWindows.py. And input formats are the same. Please see the notes for that script above.
As above, you can either include sample names after the population name, separated by commas, or provide a populations file, which has two columns: the first gives sample names and teh second gives population name.
fd gives meaningless values (<0 or >1) if D is negative. If there is no excess of shared derived alleles between P2 and P3 (indicated by a positive D), then the excess cannot be quantified. fd values for windows with negative D should therefore either be discarded or converted to zero, depending on your hypothesis.
If you are interested in shared variation between P3 and P2 (positive D) or between P3 and P1 (negative D), then fd might not be the best approach. fdM is an alternatve statistic, devised by Milan Malinsky that is better suited to this scenario. It gives positive values for introgression between P3 and P2 and negative values for introgression between P3 and P1. However, my simulation tests show that fdM tends to underestimate the admixture proportion. Also, note that if introgression occurred between both P3 and P2 and P3 and P1 at the same locus, then it cannot be accurately quantified, because the signal of shared variation depends on either P1 or P2 being uninvolved.
If a small number of SNPs is used per window, stochastic errors can cause fd to have meaningless values even when D is positive. Therefore, try to use a window size that allows at least 100 biallelic SNPs per window (see the sitesUsed column to see the number of biallelic SNPs available).
| Column Header | Description |
|---|---|
scaffold |
The scaffold the window is on (all windows are on a single scaffold) |
start |
window start position (inclusive) |
end |
window end position (NOTE, this can exceed the length of the scaffold) |
sites |
Number of genotypes sites in the input file in each window |
sitesUsed |
number of sites used to compute statistics (biallelic SNPs) |
ABBA |
Pseudo count of ABBA sites (including polymorphic sites) (See Martin et al. 2015 Equation 2) |
BABA |
Pseudo count of BABA sites (including polymorphic sites) (See Martin et al. 2015 Equation 3) |
D |
D statistic (see Durand et al. 2011 Equation 2) |
fd |
fd admixture estimation (See Martin et al. 2015 Equation 6) |
fdM |
Malinsky's modified statistic, fdM to accomodate admixture between either P1 and P3 or P2 and P3 (See Malinsky et al. 2015 Supplementart Material Page 8) |
Two scripts in the phylo/ directory will make trees in sliding windows: phymlWindows.py and raxmlWindows.py. As the names suggest they use Phyml and RAxML, respectively.
python phyml_sliding_windows.py -T 10 -g input.phased.geno.gz --prefix output.phyml_bionj.w50 -w 50 --windType sites --model GTR python phymlWindows.py -h Will print a full list of command arguments.
You need to have Phyml (or RAxML) installed on your machine. You can direct the script to the location of the executable. I recommend using an unthreaded version, since each window tree will run very quickly.
The window can be defined based on genomic coordinates (--windType coord) or the number of sites (--windType sites). Windows will not cross contig/scaffold boundaries.
Genotypes need to be in either the phased, haplo or diplo formats shown above (diplo format is not recommended, as heterozygous genotypes will be treated as single genotypes with ambiguous bases, which are ignored by Phyml.
For the raxml script, you could also use diplo format, although I'm not sure whether the ambiguity codes will be used at all by RAxML. It is certainly better to use phased sequences if you can.
If diploid genotypes are in the phased format, they will be split into haplotypes, and the suffixes '_A' and '_B' will be added to the sample names to distinguish the haplotypes.
To make neighbour-joining trees, use the Phyml script, and set --optimise n, which tells it not to do any ML optimisation.
Given a genome annotation file, the script codingSiteTypes.py classifies each site within a codon according to its codon position, synonymous/non-synonymopus (if it's a variant) and degeneracy.
python codingSiteTypes.py -a annotation.gff3.gz -f gff -r reference.fasta.gz -v variants.vcf.gz --ignoreConflicts | bgzip > output.coding_site_types.tsv.gzpython distMat.py -h will print a list of command options.
-f gff or -f gtfscaffold, position, codon_position, substitution_type and degeneracy.substitution_type can be synonymous (syn) or nonsynonymous (non), or simply NA, if the site is not variable in the dataset, or if no VCF file is provided.gunzip -c output.coding_site_types.tsv.gz | awk 'BEGIN {OFS="\t"}; $5=="4" {print($1,$2-1,$2)}' > output.4Dsites.bed