pypgen 0.2.1

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.

Features:

• 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

Important Note:

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.

Enclosed Scripts:

• Sliding window analysis (vcf_sliding_window.py)

• Per SNP analysis (vcf_snpwise_fstats.py)

Dependancies:

• OSX or Linux

• Python 2.7

• Numpy

• pysam and samtools

Installation:

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

or,

easy_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

Documentation:

More detailed documentation will be forthcoming, but in the meantime information about each script can be obtained by running:

python [script name].py --help

Output:

Note: this will probably change.

vcf_sliding_window.py:

• 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)

• cont…

vcf_snpwise_fstats.py:

• 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.Pop2.Hs_est

• Pop1.Pop2.Ht_est

• cont…,

• Pop1_fixed = If a sample is fixed at a particular allele this flag is set to 1 (= “True” in binary).

• cont…