Some quick 'n dirty variant "estimation" tools. The intent of this project is not to create accurate genotypes, per se, as there are a great many good softwares for doing this, but to create the fastest possible genotypes that are passable to assess sample integrity, match samples from the same person, and approximate relatedness among individuals.
Dependencies:
[HTSLib v1.10+] (https://github.com/samtools/htslib/releases/tag/1.13) must be [installed] (https://github.com/samtools/htslib/blob/develop/INSTALL) globally on your machine, or if you don't have root access to your machine, tell GCC where a local htslib is.
For example, if you have built htslib in /home/user/htslib:
export LIBRARY_PATH=/home/usr/htslib:$LIBRARY_PATH
export CPATH=/home/usr/htslib/htslib:$CPATH
Install:
git clone http://github.com/txje/fastvar
cd fastvar
# get klib headers
mkdir incl
cd incl
git clone http://github.com/attractivechaos/klib
cd ..
cd ..
make
./fasttype <BAM> <BED> <REF FASTA>
BAM: reads aligned to <REF FASTA> or '-' to read SAM format from the input stream
BED: list of variant positions to capture in BED format (like dbSNP's [discontinued] batch query results)
<chrom> <st> <en> <snp> <score> <strand>
chr7 6026829 6026830 rs2639 0 +
We actually only care about the first two fields - chrom and start position.
IMPORTANT: in this format, for whatever reason, the canonical reference position of the SNP is the end, and the start is (pos - 1). In practice, we just use the "start" as an index into the 0-indexed reference, so if you're off by one, most of the time all of your loci will appear homozygous and there will be little or no differences between samples. If you see this, double-check your BED file. See data/exomeChip_fingerprint_snps.bed as an example.
REF FASTA: fasta file used for alignments
Or, directly from one or more unaligned read files, using a k-mer approach:
./ktype <BED> <REF FASTA> <READ FASTA/Q> [<READ FASTA/Q> ...]
READ FASTA/Q: may be gzipped
A tab-separated file in the following format:
chrom start end ref_allele A C G T N Unknown
Where the first three fields match the input BED file, followed by the allele from the reference fasta, and counts for each read allele observed at that position, which - in rare cases - may include an "Unknown" that accounts for all manner of sins, including deletions.
For both fastttype and ktype, memory usage is typically dominated the size of the reference (~3GB for humans). If you have very many SNPs, the variant bookkeeping will begin to contribute.
Runtime is typically dominated by the BAM or read FASTQ size. In my hands, for high-depth (>100x) exome sequencing, fasttype takes ~2 min per sample and ktype takes ~7 min per sample from gzipped reads (on one thread).
Alignment-based fasttype results correspond very closely to the ktype results in most cases. For the limited trials, with relatively few SNPs, described below, I've tried k = 32 and k = 64, and there's little difference. There are still some undiagnosed differences where, in some cases, ktype finds anywhere from ~1/2 - 2x as many of one or both nucleotides as fasttype. For simple sample matching, this isn't a huge problem because at least it's consistent across samples, but is certainly not the expected behavior.
For the cleanest and fastest rapid genotyping, here's what I recommend:
git clone https://github.com/lh3/minimap2
cd minimap2
make
cd ..
ref=h38.fa # or something
r1=SAMPLE_A_R1.fastq.gz # or something
r2=SAMPLE_A_R2.fastq.gz # or something
snp_bed=fastvar/data/exomeChip_fingerprint_snps.bed # or something
./minimap2/minimap2 -t $(nproc) -ax sr $ref $r1 $r2 | ./fastvar/fasttype - $snp_bed $ref > SAMPLE_A.fast_variants
# -- OR --
./fastvar/ktype $snp_bed $ref $r1 $r2 > SAMPLE_A.k_variants
I've included a small set of common exome genotyping SNPs (data/exomeChip_fingerprint_snps.bed) (see https://genome.sph.umich.edu/wiki/Exome_Chip_Design) that are typically sufficient to match and distinguish samples by individual for fast sanity checks (read barcode mixups).
There is also another, much bigger set of exome SNPs with AF 0.45 - 0.55 from 1000 Genomes data: data/1kgenomes_0.5af_exome_snps.bed
Feel free to submit issues and pull requests.