bioRxivGenomicsPreprint
Correcting Methylation Calls in Clinically Relevant Low-Mappability Regions
Caiden M. Kumar, Devon Patrick RyaniD, Bradley W. LanghorstiD
Abstract
DNA methylation is an important component in vital biological functions such as embryonic development, carcinogenesis, and heritable regulation. Accurate methods to assess genomic methylation status are crucial to its effective use in many scenarios, especially in the detection and diagnosis of disease. Methylation aligners, such as Bismark and bwa-meth, frequently assign significantly higher MapQ values than can be supported by the uniqueness of the region reads are mapped to. These incorrectly high MapQs result in inappropriate methylation calling in repetitive regions. We observe reads that should map to separate locations (possibly having different methylation states) actually end up mapping to the same locus, causing apparent mixed methylation at such loci. Methylation calling can be improved by using Bismap mappability data to filter out insufficiently unique reads. However, simply filtering out Cs in insufficiently unique regions is not adequate as it is prone to over-filtering Cs in small mappability dips. These Cs can in fact often be called using reads anchored in a nearby mappable region. We have created a new feature for the MethylDackel methylation caller to perform read-based filtering. This new methylation calling method resolves some of the apparent mixed methylation to either 0% or 100% methylation and removes many unsupportable methylation calls. We examined methylation calls with and without read-based filtering in or near the 7830 genes containing ClinVar variants in a methylation sequencing data set from the NA12878 cell line. Use of this improved method corrected 41,143 mixed methylation Cs to 0% methylation, and 22,345 to 100% methylation throughout the genome.
- organism: human
- assay: DNA methylation
- research focus: variant calling
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 2
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