A sensitive Mitochondrial variant detection pipeline from WGS data
mity is a bioinformatic analysis pipeline designed to call mitochondrial SNV and INDEL variants from Whole Genome Sequencing (WGS) data.
- identify very low-heteroplasmy variants, even <1% heteroplasmy when there is sufficient read-depth (eg >1000x)
- filter out common artefacts that arise from high-depth sequencing
- easily integrate with existing nuclear DNA analysis pipelines (mity merge)
- provide an annotated report, designed for clinicians and researchers to interrogate
- python3 (tested on 3.7.4)
- freebayes >= 1.2.0
- bgzip + tabix
- gsort (https://github.com/brentp/gsort)
Installation instructions via Docker, pip, or manually are available in INSTALL.md
This is an example of calling variants in the Ashkenazim Trio.
mity call on three MT BAMs provided in mity/test_in. CRAM files are supported.
We recommend always using
mity report won't work:
mity call \ --prefix ashkenazim \ --out-folder-path test_out \ --region MT:1-500 \ --normalise \ test_in/HG002.hs37d5.2x250.small.MT.RG.bam \ test_in/HG003.hs37d5.2x250.small.MT.RG.bam \ test_in/HG004.hs37d5.2x250.small.MT.RG.bam
This will create
test_out/normalised/ashkenazim.mity.vcf.gz (and tbi file).
or, if using Docker:
docker run -w "$PWD" -v "$PWD":"$PWD" drmjc/mity call \ --prefix ashkenazim \ --out-folder-path test_out \ --region MT:1-500 \ --normalise \ test_in/HG002.hs37d5.2x250.small.MT.RG.bam \ test_in/HG003.hs37d5.2x250.small.MT.RG.bam \ test_in/HG004.hs37d5.2x250.small.MT.RG.bam
We can create a
mity report on the normalised VCF:
mity report \ --prefix ashkenazim \ --min_vaf 0.01 \ --out-folder-path test_out \ test_out/ashkenazim.mity.vcf.gz
This will create:
High-depth sequencing and sensitive variant calling can create many variants with more than 2 alleles, and in some
cases, joins two nearby variants separated by shared
REF sequence into a multi-nucleotide polymorphism
as discussed in the manuscript. Here, variant normalisation relates to decomposing the multi-allelic variants and
where possible, splitting multi-nucleotide polymorphisms into their cognate smaller variants. At the time of writing,
all variant decomposition tools we used failed to propagate the metadata in a multi-allelic variant to the split
variants which caused problems when reporting the quality scores associated with each variant.
Technically you can run
mity call and
mity normalise separately, but since
mity report requires a normalised
vcf file, we recommend running
mity call --normalise.
You can merge a nuclear vcf.gz file and a mity.vcf.gz file thereby replacing the MT calls from the nuclear VCF (
presumably from a caller like HaplotypeCaller which is not able to sensitively call mitochondrial variants) with
the calls from
mity merge \ --prefix ashkenazim \ --mity_vcf test_out/ashkenazim.mity.vcf.gz \ --nuclear_vcf todo-create-example-nuclear.vcf.gz
Recommendations for interpreting the report
Assuming that you are looking for a pathogenic variant underlying a patient with a rare genetic disorder potentially caused by a Mitochondrial mutation, then we recommend the following strategy:
- tier 1 or 2 variants included in the 'commercial_panels' column
- tier 1 or 2 variants that match the clinical presentation and the phenotype in 'disease_mitomap', preferably those that are annotated with Confirmed evidence in the 'status_mitomap' column
- exclude common variants: anything linked to 'phylotree_haplotype', high 'phylotree_haplotype', high 'MGRB_frequency', high 'GenBank_frequency_mitomap'.
- consider any remaining tier 1 or 2 variants that may have a predicted impact on tRNA
- consider any remaining variants with high numbers of 'variant_references_mitomap'
- if you have analysed multiple family members, consider variants who's level of 'variant_heteroplasmy' match the disease burden
- tier 3 variants have low numbers of supporting reads, and should be considered with caution. However we have observed numerous tier 3 variants, especially in WGS from blood, that match the pathogenic allele known to be at much higher heteroplasmy in the affected tissue (this phenomenon is well established in the literature). Thus, if there are any tier 3 variants identified that match the patient's clinical presentation, then we recommend considering these as candidate variants and validating using an orthogonal clinically validated assay, preferably on the disease affected tissue.
mity natively supports the analysis of the revised Cambridge Reference Sequence (rCRS, RefSeq ID NC_012920.1). The
rCRS used in most human reference genomes from NCBI (GRCh37, hs37d5, GRCh38) and hg38 from UCSC, where it is either
MT. The main exception in common use is the
hg19 reference genome from UCSC, which used a different
sequence (RefSeq NC_001807) which differs in length by 2bp, and sharing 99% sequence homology (16530/16572 identities)
and 4 gaps. For now,
mity call supports the hg19 reference, but
mity report will not annotate variants properly, so
you should not use this part of the pipeline. We strongly recommend that for mitochondrial analysis, to use a reference
genome that uses the rCRS sequence.
- the mitochondrial genome: since the release of the UCSC hg19 assembly, the Homo sapiens mitochondrion sequence (represented as "chrM" in the Genome Browser) has been replaced in GenBank with the record NC_012920, the revised Cambridge Reference Sequence (rCRS). We have not replaced the original sequence, NC_001807, as chrM in the hg19 Genome Browser. However, files in the subdirectory p13.plusMT include NC_012920 as "chrMT", in addition to the original "chrM".
|Reference||contig name||RefSeq ID||length||rCRS|
|hg19 (UCSC)||chrM||NC_001807.4||16571 bp||no|
normalise support the analysis of the mouse genome (
mity call --reference mm10 ...).
currently only supports variant annotation to the human rCRS sequence.
Commonly asked Questions
Base quality score recalibration (BQSR)
Most of the development of
mity was tested on BAM files that had undergone GATK's BQSR method, which improves the
base qualities of each read.
In our experience, this reduced the quality score of most bases by ~10 points, indicating that the base qualities
straight out of the sequencer are generally inflated. As the GATK best practices guide no longer recommends BQSR, it's
reasonable to ask whether
mity can be run on BAM files straight out of the aligner.
mity has a custom QUAL score, which depends on the base qualities of only the reads that support the alternative
For tier 1 or 2 variants, there will be so many supporting reads, that any miscalibration of base quality scores will have no material effect. Tier 3 variants with very few supporting reads may be impacted, where a variant with only 3 or 4 supporting reads may end up having a stronger mity QUAL score than after BQSR. The comment above regarding how you should interpret and validate tier 3 variant still holds. We would appreciate any feedback you may have on this.
CRAM support was added to
mity call in v0.4.0.
We would like to thank:
- The Kinghorn Centre for Clinical Genomics and collaborators, who helped with feedback for running
- The Genome in a Bottle consortium for providing the test data used here
- Eric Talevich who's CNVkit helped us structure
mityas a package
- Erik Garrison for developing
FreeBayesand his early feedback in optimising
FreeBayesfor sensitive variant detection.
- Brent Pederson for developing
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