You should use https://github.com/KCCG/mity/
mity
is a bioinformatic analysis pipeline designed to call mitochondrial SNV and INDEL variants from Whole Genome Sequencing (WGS) data. mity
can:
- 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
mity -h
- python3 (tested on 3.7.4)
- freebayes >= 1.2.0
- bgzip + tabix
- gsort (https://github.com/brentp/gsort)
- pyvcf
- xlsxwriter
- pandas
Installation instructions via Docker, pip, or manually are available in INSTALL.md
This is an example of calling variants in the Ashkenazim Trio.
First run mity call
on three MT BAMs provided in mity/test_in.
We recommend always using --normalise
, or 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).
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: test_out/ashkenazim.annotated_variants.csv
and test_out/ashkenazim.annotated_variants.xlsx
.
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
.
mity merge \
--prefix ashkenazim \
--mity_vcf test_out/ashkenazim.mity.vcf.gz \
--nuclear_vcf todo-create-example-nuclear.vcf.gz
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
We would like to thank:
- The Kinghorn Centre for Clinical Genomics and collaborators, who helped with feedback for running
mity
. - The Genome in a Bottle consortium for providing the test data used here
- Eric Talevich who's CNVkit helped us structure
mity
as a package - Erik Garrison for developing
FreeBayes
and his early feedback in optimisingFreeBayes
for sensitive variant detection. - Brent Pederson for developing
gsort