BOREALIS: an R/Bioconductor package to detect outlier methylation from bisulfite sequencing data

Background: Rare genetic disease studies have benefited from the era of high throughput sequencing. DNA sequencing results in genetic diagnosis of 18-40% of previously unsolved cases, while the incorporation of RNA-Seq analysis has more recently been shown to generate significant numbers of previously unattainable diagnoses. While DNA methylation remains less explored, multiple inborn diseases resulting from disorders of genomic imprinting are well characterized and a growing body of literature suggests the causative or correlative role of aberrant methylation in diverse rare inherited conditions. Complex pictures of methylation patterning are also emerging, including the association of regional, multiple specific-site or even single-site methylation, with disease. The systematic application of genomic-wide methylation-based sequencing for undiagnosed cases of rare diseases is a logical progression from current testing paradigms. Similar to the rationale previously exploited in RNA-based rare disease studies, we can assume that disease-associated or causative methylation aberrations in an individual will demonstrate significant differences from other individuals with unrelated phenotypes. Thus, aberrantly methylated sites will be outliers from a heterogeneous cohort of individuals. Methods: Based on this rationale, we present BOREALIS: Bisulfite-seq OutlieR MEthylation At SingLeSIte ReSolution. BOREALIS uses a beta binomial model to identify outlier methylation at single CpG site resolution from bisulfite sequencing data. Results: Utilizing power analyses, we demonstrate that BOREALIS can identify outlier CpG methylation within a cohort of samples. Furthermore, we show that BOREALIS is tolerant to the inclusion of multiple identical outliers with sufficient cohort size and sequencing depth. Conclusions: The method demonstrates improved performance versus standard statistical testing and is suited for single or multi-site downstream analysis.