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Project 2: A reproducible template workflow for single-cell DNA methylation data #2

@abaghela

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@abaghela

A reproducible template workflow for single-cell DNA methylation data

DNA methylation is a heritable epigenetic mark that shows a strong correlation with transcriptional activity, and may be detected by whole genome bisulfite sequencing (WGBS). Recently, WGBS has been performed successfully on single cells (SC-WGBS). The resulting data represents a fundamental shift in the capacity to measure and interpret DNA methylation, especially in rare cell types and contexts where subtle cell-to-cell heterogeneity is crucial, such as in stem cells or cancer. However, although some software tools have been published, and several existing studies have tended to use similar methods, no standardized pipeline for the analysis of SC-WGBS yet exists. Simultaneously, there has been a drive within bioinformatics towards improved reproducibility. Recreating the exact results of a study requires not only the exact code, but also the exact software. Common Workflow Language (CWL) provides a framework for specifying complete workflows, while Docker allows for bundling of the exact software and auxiliary data used in an analysis within a container that can be executed anywhere. Together, these have the potential to enable completely reproducible bioinformatics research. At a previous Hackathon, the first steps were taken towards developing Screw, a collection of standard tools and workflows for analysing SC-WGBS data, wrapped in CWL and Docker. https://github.com/Epigenomics-Screw/Screw Screw will include quality control visualization, clustering and visualisation of cells by pairwise dissimilarity measures, construction of recapitulated-bulk methylomes from single cells of the same lineage, generation of bigWig methylation tracks for downstream visualization, and wrappers around published tools such as DeepCpG and LOLA. This project will focus on completing Screw, while also building standardised workflows to analyse a series of public SC-WGBS data sets. This will both provide a complete resource for reproducible SC-WGBS analysis, as well as a first metanalysis of SC-WGBS data.

Team Lead: Kieran O'Neill | koneill@bcgsc.ca | @oneillkza | Postdoctoral Fellow | BC Genome Sciences Centre

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