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Effective selection of population size projection for construction of the site frequency spectrum. Convert VCF to dadi/fastsimcoal style SFS for demographic analysis
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README.md

easySFS

Effective selection of population size projection for construction of the site frequency spectrum. Convert VCF to dadi/fastsimcoal style SFS for demographic analysis

This is a relatively simple script. It was created for use with VCF files from RAD-style datasets. VCF file formats differ pretty dramatically so ymmv. Right now it's been tested and seems to run fine for VCF as output by both pyrad/ipyrad and tassel.

Dependencies

The script assumes you have matplotlib and dadi installed. The easiest way to install matplotlib: pip install matplotlib. There is no easy way to install dadi, so you have to download and install from source:

Install & Run

  • Clone this repo
  • git clone https://github.com/isaacovercast/easySFS.git
  • cd easySFS
  • chmod 777 easySFS.py
  • ./easySFS.py

General workflow

Converting VCF to SFS is a 2 step process. The first step is to run a preview to identify the values for projecting down each population. The second step is to actually do the conversion specifying the projection values. It looks like this:

./easySFS -i input.vcf -p pops_file.txt --preview

Which will output something like this:

Pop1
(2, 45.0)   (3, 59.0)   (4, 58.0)   (5, 49.0)   (6, 41.0)   (7, 35.0)   (8, 27.0)   (9, 20.0)   (10, 13.0)  (11, 8.0)   (12, 8.0)   (13, 5.0)   (14, 2.0)   (15, 2.0)   (16, 1.0)   

pop2
(2, 68.0)   (3, 96.0)   (4, 106.0)  (5, 110.0)  (6, 108.0)  (7, 89.0)   (8, 76.0)   (9, 66.0)   (10, 56.0)  (11, 49.0)  (12, 42.0)  (13, 39.0)  (14, 34.0)  (15, 29.0)  (16, 27.0)  (17, 26.0)  (18, 24.0)  (19, 23.0)  (20, 21.0)  (21, 22.0)  (22, 20.0)  (23, 19.0)  (24, 16.0)  (25, 16.0)  (26, 15.0)  (27, 15.0)  (28, 13.0)  (29, 13.0)  (30, 14.0)  (31, 14.0)  (32, 14.0)  (33, 13.0)  (34, 12.0)  (35, 9.0)   (36, 9.0)   (37, 8.0)   (38, 8.0)   (39, 8.0)   (40, 6.0)   (41, 6.0)   (42, 6.0)   (43, 5.0)   (44, 5.0)   (45, 5.0)   (46, 4.0)   (47, 4.0)   (48, 4.0)   (49, 3.0)   (50, 3.0)   (51, 3.0)   (52, 3.0)   (53, 3.0)   (54, 3.0)   (55, 2.0)   (56, 2.0)   (57, 2.0)   (58, 2.0)   (59, 2.0)   (60, 2.0)   (61, 2.0)   (62, 0.0)   (63, 0.0)   (64, 0.0)   (65, 0.0)   (66, 0.0)   (67, 0.0)   (68, 0.0)   (69, 0.0)   (70, 0.0)   (71, 0.0)   (72, 0.0)   (73, 0.0)   (74, 0.0)   (75, 0.0)   (76, 0.0)

Each column is the number of samples in the projection and the number of segregating sites at that projection value. The dadi manual recommends maximizing the number of segregating sites, but at the same time if you have lots of missing data then you might have to balance # of segregating sites against # of samples to avoid downsampling too far.

Next run the script with the values for projecting for each population, like this:

./easySFS -i input.vcf -p pops_file.txt --proj 12,20

Input files

Two input files are required, the VCF and the population specification file. VCF in the format as written out by pyrad/ipyrad is known to work well, other vcf formats may work too, but aren't guaranteed. The population assignment file is a plain text file with two columns, one for sample names and one for the population the sample belongs to, like this:

sample1 pop1
sample2 pop1
sample3 pop2
sample4 pop2

Only samples that are in both the pop file and the vcf file will be included in the final sfs.

Outputs

By default the script generates all 1D sfs per population, all pairwise joint sfs per population pair and one multiSFS for all populations. If you specify the -o flag you can pass in an output directory which will be created, otherwise output files are written to the default directory output. There will be two directories created here dadi and fastsimcoal2.

dadi

1D SFS will be named like this: Pop1-<sample_size>.sfs
Joint SFS will be named like this: Pop1-Pop2.sfs Pop2-Pop3.sfs
multiSFS will will be named like this: Pop1-Pop2-Pop3.sfs

Fastsimcoal2

Fastsimcoal2 is pickier about file naming conventions and file format.

1D SFS will be named like this: Pop1_MAFpop0.obs Pop2_MAFpop0.obs
Joint SFS will be named like this: prefix_jointMAFpop0_1.obs prefix_jointMAFpop0_2.obs prefix_jointMAFpop1_2.obs
multiSFS will will be named like this: prefix_MSFS.obs

Running example files

The example files are different enough where they will give you an idea of what most of the command line options do.

Diploid example

  • Diploid data
  • 2 populations
  • SFS includes all snps from w/in each locus

Preview: ./easySFS.py -i example_files/wcs_1200.vcf -p example_files/wcs_pops.txt --preview -a
Convert: ./easySFS.py -i example_files/wcs_1200.vcf -p example_files/wcs_pops.txt -a --proj=7,7

The -a flag specifies that all snps in the vcf should be used. Also, notice the --ploidy flag is not required since diploid is the default.

Haploid example

  • Haploid data
  • 3 populations
  • SFS only includes one snp per RAD locus

Preview: ./easySFS.py -i example_files/leuco_1200.vcf -p example_files/leuco_pops.txt --ploidy 1
Convert: ./easySFS.py -i example_files/leuco_1200.vcf -p example_files/leuco_pops.txt --ploidy 1 --proj=8,10

Here the --polidy flag is required. In this example only one snp per locus will be randomly sampled for inclusion in the output sfs.

Usage

You can get usage info any time by: ./easySFS.py

usage: easySFS.py [-h] [-a] -i VCF_NAME -p POPULATIONS [--proj PROJECTIONS]
                  [--preview] [-o OUTDIR] [--ploidy PLOIDY] [--prefix PREFIX]
                  [--unfolded] [--dtype DTYPE] [--GQ GQUAL] [-f] [-v]

optional arguments:
  -h, --help          show this help message and exit
  -a                  Keep all snps within each RAD locus (ie. do _not_
                      randomly sample 1 snp per locus).
  -i VCF_NAME         name of the VCF input file being converted
  -p POPULATIONS      Input file containing population assignments per
                      individual
  --proj PROJECTIONS  List of values for projecting populations down to
                      different sample sizes
  --preview           Preview the number of segragating sites per population
                      for different projection values.
  -o OUTDIR           Directory to write output SFS to
  --ploidy PLOIDY     Specify ploidy. Default is 2. Only other option is 1 for
                      haploid.
  --prefix PREFIX     Prefix for all output SFS files names.
  --unfolded          Generate unfolded SFS. This assumes that your vcf file
                      is accurately polarized.
  --dtype DTYPE       Data type for use in output sfs. Options are `int` and
                      `float`. Default is `float`.
  --GQ GQUAL          minimum genotype quality tolerated
  -f                  Force overwriting directories and existing files.
  -v                  Set verbosity. Dump tons of info to the screen

Credits

easySFS is kind of a loose wrapper around dadi, which I use for internal sfs representation, and much of the grunt work.

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