Abstract
BackgroundNext-generation sequencing (NGS) approaches are commonly used to identify key regulatory networks that drive transcriptional programs. Although these technologies are frequently used in biological studies, NGS data analysis remains a challenging, time-consuming, and often irreproducible process. Therefore, there is a need for a comprehensive and flexible workflow platform that can accelerate data processing and analysis so more time can be spent on functional studies.ResultsWe have developed an integrative, stand-alone workflow platform, named CIPHER, for the systematic analysis of several commonly used NGS datasets including ChIP-seq, RNA-seq, MNase-seq, DNase-seq, GRO-seq, and ATAC-seq data. CIPHER implements various open source software packages, in-house scripts, and Docker containers to analyze and process single-ended and pair-ended datasets. CIPHER’s pipelines conduct extensive quality and contamination control checks, as well as comprehensive downstream analysis. A typical CIPHER workflow includes: (1) raw sequence evaluation, (2) read trimming and adapter removal, (3) read mapping and quality filtering, (4) visualization track generation, and (5) extensive quality control assessment. Furthermore, CIPHER conducts downstream analysis such as: narrow and broad peak calling, peak annotation, and motif identification for ChIP-seq, differential gene expression analysis for RNA-seq, nucleosome positioning for MNase-seq, DNase hypersensitive site mapping, site annotation and motif identification for DNase-seq, analysis of nascent transcription from Global-Run On (GRO-seq) data, and characterization of chromatin accessibility from ATAC-seq datasets. In addition, CIPHER contains an “analysis” mode that completes complex bioinformatics tasks such as enhancer discovery and provides functions to integrate various datasets together.ConclusionsUsing public and simulated data, we demonstrate that CIPHER is an efficient and comprehensive workflow platform that can analyze several NGS datasets commonly used in genome biology studies. Additionally, CIPHER’s integrative “analysis” mode allows researchers to elicit important biological information from the combined dataset analysis.
Highlights
Next-generation sequencing (NGS) approaches are commonly used to identify key regulatory networks that drive transcriptional programs
Next-generation sequencing (NGS) technologies are powerful, and widely applied tools to map the in vivo genome-wide location of transcription factors (TFs), histone modifications, nascent transcription, nucleosome positioning, and chromatin accessibility features that make up these regulatory networks
We demonstrate that CIPHER is a fast, reproducible, and flexible tool that accurately processes and integrates NGS datasets by recreating the results of two published studies, and comparing CIPHER’s speed and ease of use to two other chromatin immunoprecipitation (ChIP)-seq and RNA sequencing (RNA-seq) pipelines
Summary
To validate CIPHER’s potential in NGS data analysis, we used data from the Gene Expression Omnibus repository (GEO) to re-create two previously published studies: a ChIP-seq study from McNamara et al [42] and a GRO-seq study from Liu et al [43]. This analysis revealed accessible chromatin at the center of all predicted enhancers as shown by DNase-seq, and chromatin signatures surrounding the nucleosome free region (NFR) in a ‘peak-valley-peak’ pattern that is consistent with traditional enhancer signatures [55] (Fig. 6d) While both active and primed enhancers contained comparable levels of H3K4me, active enhancers contained larger H3K27ac levels (average coverage: 0.72 versus 0.099), and stronger eRNA sense (6 versus 1) and anti-sense (5 versus 1) read coverage compared with primed enhancers, consistent with increased enhancer activity (Fig. 7a and b). Using CIPHER in combination with our previous stringent cut-off, we predicted enhancers in other cell lines: 38,045 active and 10,600 primed enhancers in HeLa (Fig. 6e and f), and 38,551 active and 2292 primed enhancers in K562 cells (data not shown) These results demonstrate that our enhancer-recognition model can reliably detect enhancer elements using ChIPseq and DNase-seq datasets in a broad range of cell lines
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