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• Parsing VCF files
• Diversity and divergence analyses in sliding windows
• Distance matrix
• ABBA-BABA statistics in sliding windows
• Trees for sliding windows

Most of my scripts use a processed .vcf format that I call .geno. This looks something like this:

#CHOM      POS      ind1      ind2      ind3
scaffold1  1        A/A       A/G       G|A
scaffold1  1        N/N       T/T       T|C

Missing 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 parseVCF.py -i input.vcf.gz --skipIndels --minQual 30 --gtf flag=DP min=5 | gzip > output.geno.gz

• You can filter on any flag associated with the genotype, such as read depth (DP) and genotype likelihood (PL). Check the FORMAT column of your vcf file to see which flags are present. To add a genotype filter, add the argument --gtf followed by the flag title, minimum and/or maximum value, as in the example above.

• By default the script outputs the genotype for each sample at each site. You can also output a different field, such as DP, as long as it is present in the FORMAT column of the vcf. e.g. --field DP

• minQual refers to the QUAL column in the vcf, and not individual genotype qualities, for which you should use the --gtf argument.

The script popgenWindows.py computes some standard population genomic statistics in sliding windows: pi, F_ST and D_XY. 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.

• Genotypes can be incoded in various ways, as indicated by the -f flag.

-f phased means that genotypes include phase information (but they don't necessarily need to actually be phased):

scaffold1  1        A/A       G/G       G|A`

-f pairs is the same, but without the phase indicator:

scaffold1  1        AA       GG       AG

-f haplo means that genotypes are haploid bases:

scaffold1  1        A       G       G

-f diplo means that genotypes are single bases denoting the diploid genotype, using ambiguity codes for heterozygotes:

scaffold1  1        A       G       R

• There are three options for defining the windows to be analysed, using the --windType argument.

• You can either include sample names after the population name, separated by commas, or provide only the population name, along with a populations file, with the 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  popD

• The 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 distMat.py computes a distance matrix among all pairs of individuals. This can be computed either for the entire infput 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.dist

python 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, use --windType cat

• 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.

• f_d 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. f_d 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 f_d might not be the best approach. f_dM 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 f_dM 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 f_d 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).

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.