The variant calling command in its simplest form is
bcftools mpileup -f reference.fa alignments.bam | bcftools call -mv -Ob -o calls.bcf
mpileup part generates genotype likelihoods at each genomic
position with coverage. The second
call part makes the actual calls.
-m switch tells the program to use the default calling method,
-v option asks to output only variant sites, finally the
selects the output format. In this example we chosen binary compressed BCF,
which is the optimal starting format for further processing, such as filtering.
However, there are a few more options one should know about.
Do not waste computer’s time by making
mpileupconvert from the internal binary representation (BCF) to text (VCF), only to be immediately converted back to binary representation by
call. Instead, use
-Outo work with uncompressed BCF output:
bcftools mpileup -Ou -f reference.fa alignments.bam | bcftools call -mv -Ob -o calls.bcf
bcftools mpileup --max-depthoption, most likely it should be increased. Note that by default only 250 reads per-file are considered at a position!
The calling can be made more sensitive or restrictive by using a different prior. Stricter calls are obtained by using smaller value, more benevolent calls are obtained by using bigger value. The default is
bcftools call -P 1.1e-3.
Run in parallel..
Variant filtering is not easy. The variant callers provide a quality score (the QUAL) column, which gives an estimate of how likely it is to observe a call purely by chance. An easy way to filter low quality calls is
bcftools view -i '%QUAL>=20' calls.bcf
Unfortunately, the quality score does not include the effects of systematic biases. For example, if the sequenced genome had two copies of a region which appears only once in the reference genome, reads from both copies would end up aligned to the single reference copy. If one of the copies acquired a mutation, this would appear as a genuine high-quality SNP. One might argue whether the callers should or should not report such calls: on one hand, the call certainly highlights a difference between the genomes; the other hand, it certainly is not a simple SNV. There are other types of artefacts one can observe in short reads and which we will not go into here, but the message is already clear: it is difficult to tell the variants and artefacts apart.
The annotations produced by variant callers provide only indirect hints about
which is which and an approach which worked for one dataset may not work for
another. Nowadays most powerful seem machine learning approaches such as SVM
(not implemented in
bcftools), see an example of SVM filtering pipeline
The least one can do is to apply a fixed-threshold filtering. One can get a feel
for typical distributions of annotations from a set of known good calls vs all
calls. If you don’t already have a pipeline for this, use
bcftools query to
extract the annotations and plot manually, for example like this:
bcftools query -f '%MyAnnotation\n' calls.bcf | my-plotting-program
A good measure of the callset quality is often ts/tv, the ratio of transitions and transversions. Try the following command with different quality thresholds. The stricter the calls, the bigger ts/tv value one should get:
bcftools filter -i'%QUAL>20' calls.vcf.gz | bcftools stats | grep TSTV
Other useful metrics are:
sequencing depth (DP bigger than twice the average depth indicates problematic regions and is often enriched for artefacts)
the minimum number of high-quality non-reference reads
proximity to indels (
bcftools filter -g)
To give a concrete example, the following filter seemed to work quite well for one particular dataset (human data, exomes):
bcftools filter -sLowQual -g3 -G10 \ -e'%QUAL<10 || (RPB<0.1 && %QUAL<15) || (AC<2 && %QUAL<15) || %MAX(DV)<=3 || %MAX(DV)/%MAX(DP)<=0.3' \ calls.vcf.gz
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