snakePipes enable flexible, scalable and integrative epigenomic analysis

Abstract

The scale and diversity of epigenomics data has been rapidly increasing and ever more studies now present analyses of data from multiple epigenomic techniques. Performing such integrative analysis is time-consuming, especially for exploratory research, since there are currently no pipelines available that allow fast processing of datasets from multiple epigenomic assays while also allow for flexibility in running or upgrading the workflows. Here we present a solution to this problem : snakePipes, which can process and perform downstream analysis of data from all common epigenomic techniques (ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq) in a single package. We demonstrate how snakePipes can simplify integrative analysis by reproducing and extending the results from a recently published large-scale epigenomics study with a few simple commands. snakePipes are available under an open-source license at https://github.com/maxplanck-ie/snakepipes . MainEpigenomics is a fast growing field, and due to the consistent fall in the price of sequencing, increase in multiplexing abilities, and multiple innovations in laboratory protocols, it has become increasingly convenient to perform multiple epigenomic assays within a project. However, a major bottleneck on the way to process and analyse this data in a reproducible way, particularly for novice analysts, is the availability of analysis pipelines. Next-generation sequencing (NGS) analysis pipelines are composed of a series of data processing steps, employ standardised processing parameters, and are usually scalable to large number of samples 1 . Due to such properties, most pipelines are currently developed and deployed for settings where standardized, large scale analysis is required. Examples are RNA-seq variant-calling pipelines deployed in clinical settings 2 , or processing pipelines developed for large-scale consortia 3,4 . Figure S1. Analysis of de-repressed genes upon Schmd1 knock-out. A. PCA output from snakepipes suggested that one knock-out sample (replicate1) behaves differently. This sample was later revealed to be the XO clone which lost it's inactive X chromosome. The sample was removed for DESeq2 analysis and the workflow was re-run. B. Volcano plot for DESeq2 output from snakePipes (knock-out replicate 1 excluded), shows an increase in up-regulated genes, indicating de-repression upon knock-out. C. Wild-type ATAC-seq signal on UP, DOWN and unchanged (NONE) genes, gene lists were extracted from DESeq2 output of RNA-seq workflow and depth-normalized bigwigs from ATAC-seq workflow was used for plotting. D. Wild-type methylation level reported by the WGBS workflow on UP, DOWN and unchanged (NONE) genes. (TSS = Transcription Start Site)

EpiMOLAS: an intuitive web-based framework for genome-wide DNA methylation analysis

“…The combination of DocMethyl and EpiMOLAS_web offers an integrated solution without tedious software installation and database management. Comparisons among several well-known platforms and tools for genome-wide DNA methylation analysis, such as BAT [29], ENCODE-WGBS [30], snakePipe [31], NGI-MethylSeq [32], Mint [33], MethylPipe [34], MethylSig [35], and Methylkit [36] with EpiMOLAS are presented in Table 1. BAT, NGI-MethylSeq and EpiMOLAS use Docker containerization technology to allow fast and simple environment deployment.…”