Genetic diversity metrics from popoulation genomic datasets.
Pypgen provides various utilities for estimating standard genetic diversity measures including Gst, G’st, G’’st, and Jost’s D from large genomic datasets (Hedrick, 2005; Jost, 2008; Masatoshi Nei, 1973; Nei & Chesser, 1983). Pypgen operates both on individual SNPs as well as on user defined regions (e.g., five kilobase windows tiled across each chromosome). For the windowed analyses pypgen estimates the multi-locus versions of each estimator.
Handles multiallelic SNP calls
Allows a single VCF file to contain multiple populations
Operates on standard VCF (Variant Call Format) formatted SNP calls
Uses bgziped input for fast random access
Takes advantage of multiple processor cores
Calculates additional metrics:
snp count per window
mean read depth (+/- STDEV) per window
populations with fixed alleles per SNP
more as I think of them
PYPGEN IS STILL IN ACTIVE DEVELOPMENT AND ALMOST CERTAINLY CONTAINS BUGS. If you find a bug please file a report in the issues section of the github repository and I’ll address it as soon as I can.
Sliding window analysis (vcf_sliding_window.py)
Per SNP analysis (vcf_snpwise_fstats.py)
First install samtools. On OS X I recommend using homebrew to do this. Once you have samtools installed and available in terminal you can use either pip or setuptools to install the current release of pypgen:
pip install pypgen
Alternately, if you like to live on the edge, you can clone and install the current development version from github.
pip install -e git+https://github.com/ngcrawford/pypgen.git
More detailed documentation will be forthcoming, but in the meantime information about each script can be obtained by running:
python [script name].py --help
Note: this will probably change.
chrm = Name of chromosome
start = Starting position of window
stop = Ending position of window
snp_count = Total Number of SNPs in window
total_depth_mean = Mean read depth across window
total_depth_stdev = Standard deviation of read depth across window
Pop1.sample_count.mean = Mean number of samples per snp for ‘Pop1’
Pop1.sample_count.stdev = Standard deviation of samples per snp for - ‘Pop1’
Pop2.sample_count.mean = Mean number of samples per snp for ‘Pop2’
Pop2.sample_count.stdev = Standard deviation of samples per snp for ‘Pop2’
Pop2.Pop1.D_est = Multilocus Dest (Jost 2008)
Pop2.Pop1.G_double_prime_st_est = (Meirmans & Hedrick 2011)
Pop2.Pop1.G_prime_st_est = Standardized Gst (Hedrick 2005)
Pop2.Pop1.Gst_est = Fst corrected for sample size and allowing for multiallelic loci (Nei & Chesser 1983)
chrm = Name of chromosome
pos = Position of SNP
outgroups = Number of samples
Pop1 = Population ID
Pop1.Pop2.D_est= Multilocus Dest (Jost 2008)
Pop1.Pop2.G_double_prime_st_est = (Meirmans & Hedrick 2011)
Pop1.Pop2.G_prime_st_est = Standardized Gst (Hedrick 2005)
Pop1.Pop2.Gst_est = Fst corrected for sample size and allowing for multiallelic loci (Nei & Chesser 1983)
Pop1_fixed = If a sample is fixed at a particular allele this flag is set to 1 (= “True” in binary).
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