Package: mitywgs
mitymityis a bioinformatic analysis pipeline designed to call mitochondrial SNV and INDEL variants from Whole Genome Sequencing (WGS) data.mitycan: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 sequencingeasily integrate with existing nuclear DNA analysis pipelines (mity merge)provide an annotated report, designed for clinicians and researchers to interrogateUsagemity -hMore detailed usage can be founddocs/commands.mdDependenciespython3 (tested on 3.7.4)freebayes >= 1.2.0bgzip + tabixgsort (https://github.com/brentp/gsort)pyvcfxlsxwriterpandasInstallationInstallation instructions via Docker, pip, or manually are available inINSTALL.mdExample UsageThis is an example of calling variants in the Ashkenazim Trio.mity callFirst runmity callon three MT BAMs provided indocs/test-files.md. CRAM files are supported.We recommend always using--normalise, ormity reportwon't work:mitycall\--prefixashkenazim\--output-dirtest_out\--regionMT: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.bamThis will createtest_out/normalised/ashkenazim.mity.vcf.gz(and tbi file).or, if using Docker:dockerrun-w"$PWD"-v"$PWD":"$PWD"drmjc/mitycall\--prefixashkenazim\--output-dirtest_out\--regionMT: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.bammity reportWe can create amity reporton the normalised VCF:mityreport\--prefixashkenazim\--min_vaf0.01\--output-dirtest_out\test_out/ashkenazim.mity.vcf.gzThis will create:test_out/ashkenazim.annotated_variants.csvandtest_out/ashkenazim.annotated_variants.xlsx.mity normaliseHigh-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 sharedREFsequence 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 runmity callandmity normaliseseparately, but sincemity reportrequires a normalised
vcf file, we recommend runningmity call --normalise.mity mergeYou 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 frommity.mitymerge\--prefixashkenazim\--mity_vcftest_out/ashkenazim.mity.vcf.gz\--nuclear_vcftodo-create-example-nuclear.vcf.gzRecommendations for interpreting the reportAssuming 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' columntier 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' columnexclude 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 tRNAconsider 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 burdentier 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.Reference genomesHumanmitynatively 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
namedchrM, orMT. The main exception in common use is thehg19reference 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 callsupports the hg19 reference, butmity reportwill 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".Referencecontig nameRefSeq IDlengthrCRSGRCh37chrMNC_012920.116569 bprCRShs37d5MTNC_012920.116569 bprCRShg19 (UCSC)chrMNC_001807.416571 bpnoGRCh38chrMNC_012920.116569 bprCRSMousemitycallandnormalisesupport the analysis of the mouse genome (mity call --reference mm10 ...).mity reportcurrently only supports variant annotation to the human rCRS sequence.Commonly asked QuestionsBase quality score recalibration (BQSR)Most of the development ofmitywas 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 whethermitycan be run on BAM files straight out of the aligner.mityhas a custom QUAL score, which depends on the base qualities of only the reads that support the alternative
allele.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 supportCRAM support was added tomity callin v0.4.0.AcknowledgementsWe would like to thank:The Kinghorn Centre for Clinical Genomics and collaborators, who helped with feedback for runningmity.The Genome in a Bottle consortium for providing the test data used hereEric Talevich who's CNVkit helped us structuremityas a packageErik Garrison for developingFreeBayesand his early feedback in optimisingFreeBayesfor sensitive variant detection.Brent Pederson for developinggsort