The ENCODE Uniform Analysis Pipelines

Abstract

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses.

Chromatin packing domains persist after RAD21 depletion in 3D

“…3a ). Utilizing publicly available data through the ENCODE Consortium( 32–34 ), we then analyzed the effect of RAD21 depletion on TADs, loop domains, compartments, and genome connectivity in Micro-C (accession numbers available in Table S1 ). As expected, RAD21 depletion results in the marked decrease of TADs and loops, with a visually apparent transformation in contacts genome wide ( Fig.…”

“…Likewise, if global alterations in connectivity resulted in genome-wide alterations in accessibility, we would observe throughout the genome an increase in accessible loci consistent with the loss of numerous space-filling structures. To test this hypothesis, we utilized publicly available ATAC-Seq data from the ENCODE Consortium (32)(33)(34) on RAD21 depleted AID2 cells (accession numbers in Table S2), finding no change in the frequency of accessible loci per chromosome with or without RAD21 depletion at 6 hours (Fig. 5b), suggesting that loss of loop domains does not result in global alterations in chromatin accessibility.…”

RNA polymerases reshape chromatin and coordinate transcription on individual fibers

“…We next sought to validate the positioning and enrichment of these putative PIC and PPP footprints using a combination of orthologous datasets and chemical perturbations. First, we compared the positioning of these footprints to short-read sequencing derived transcript initiation and pausing data from CAGE-seq and PRO-seq, respectively [29][30][31][32] . As expected, putative PIC footprints are selectively positioned directly over CAGE-seq peaks, and putative PPP footprints are selectively positioned directly over PRO-seq peaks (Fig.…”

Simulating cell-free chromatin using preclinical models for cancer-specific biomarker discovery

“…Binary classification of SU-DHL-6 gene expression from histone mark modifications was performed using an adapted version of ShallowChrome from Frasca et al 68 to predict gene expression. 56 cell types from Roadmap Epigenomics Consortium 23 , 9 cell types from ENCODE [69][70][71][72] , and a lab-generated cfChIP-Seq SU-DHL-6 media sample, each of which had paired histone modification and gene expression data, were used for this analysis and all were processed under ChIP-Seq uniform processing guidelines as previously described 23 (Supplementary Table S3). This approach used peak calling within each gene's TSS and gene body to predict gene expression for each given cell type.…”