pywgsim is a modified version of the wgsim short read simulator.
The code for
wgsim has been modified to allow visualizing the simulated mutations as a GFF file.
The package provides both a python wrapper and standalone compiled executables for Linux and MacOS.
pip install pywgsim
PyPI page: https://pypi.org/project/pywgsim/
$ pywgsim -h
usage: pywgsim [-h] [-e 0.02] [-D 500] [-s 50] [-N 1000] [-1 70] [-2 70] [-r 0.001] [-R 0.15] [-X 0.25] [-S 0] [-A 0.05] [-f] genome [read1] [read2] positional arguments: genome FASTA reference sequence read1 FASTQ file for first in pair (read1.fq) read2 FASTQ file for second in pair (read2.fq) optional arguments: -h, --help show this help message and exit -e 0.02, --err 0.02 the base error rate -D 500, --dist 500 outer distance between the two ends -s 50, --stdev 50 standard deviation -N 1000, --num 1000 number of read pairs -1 70, --L1 70 length of the first read -2 70, --L2 70 length of the second read -r 0.001, --mut 0.001 rate of mutations -R 0.15, --frac 0.15 fraction of indels -X 0.25, --ext 0.25 probability an indel is extended -S 0, --seed 0 seed for the random generator -A 0.05, --amb 0.05 disregard if the fraction of ambiguous bases higher than FLOAT -f, --fixed each chromosome gets N sequences
Changes compared to wgsim
The original code for wgsim has been modified as follows:
- The output describing the mutations introduced by
wgsimare generated in GFF format.
- The separator character in the read name has been changed from
- There is a new flag called
--fixedthat generates the same
Nnumber of reads for each chromosome.
The read naming now follows a more widely accepted convention (i.e. NCBI) and allows for contigs with underscores in them. In addition the visual inspection of the read names is easier:
In the default operation of wgsim the
N reads are distributed such to create a uniform coverage across all chromosomes (longer chromosomes get a larger fraction of
--fixed mode is enabled
N reads will be generated for each chromosome. The
--fixed mode was introduced to simplify the evaluation of classifiers. Since the same number of reads is generated from each input sequence it becomes simpler the assess the quality of classifications (i.e. how many out of
N were classified correctly)
The tool simulates mutations assuming a diploid genome. The output generated by
pywgsim will look like this:
##gff-version 3 # # N=10000 err_rate=0 mut_rate=0.001 indel_frac=0.15000001 indel_ext=0.25 size=500 std=50 len1=70 len2=70 seed=1607013056 # NC_001416.1 wgsim snp 89 89 . + . Name=A/R;Ref=A;Alt=R;Type=het NC_001416.1 wgsim snp 2825 2825 . + . Name=-/A;Ref=-;Alt=A;Type=het NC_001416.1 wgsim snp 3712 3712 . + . Name=G/A;Ref=G;Alt=A;Type=hom NC_001416.1 wgsim snp 4622 4622 . + . Name=G/-;Ref=G;Alt=-;Type=hom
A/Rmeans heterozygous mutations with
-/Ameans an insertion of a
Arelative to the reference, the type field indicates heterozygous mutation.
G/Ameans homozygous mutations with
G/Aalleles in both copies.
G/-means a deletion of a
Gfrom the reference, the type field indicates homozygous mutation.
A A T C C G G G C T/U T A M A or C K R A or G Y W A or T W S C or G S Y C or T R K G or T M V A or C or G B H A or C or T D D A or G or T H B C or G or T V N G or A or T or C N
Read name conventions
The read names are now of the form:
NC_002945.4is the contig name that the fragment was generated from.
1768156is the left-most position of the fragment.
1768694is the right-most position of the fragment.
0:0:0are the number of errors, substitutions and indels in the left-most read of the pair.
4:0:0are the number of errors, substitutions and indels in the right-most read of the pair.
4is the read pair number, unique, per contig.
The C interface to
wgsim is accessible as a single function call
from pywgsim import wgsim wgsim.core(r1="read1.fq", r2="read2.fq", ref="genome.fa", err_rate=0.02, mut_rate=0.001, indel_frac=0.15, indel_ext=0.25, max_n=0.05, is_hap=0, N=100000, dist=500, stdev=50, size_l=100, size_r=100, is_fixed=0, seed=0)
The function creates the files
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