Dataset Information

RACS: rapid analysis of ChIP-Seq data for contig based genomes.

ABSTRACT: BACKGROUND:Chromatin immunoprecipitation coupled to next generation sequencing (ChIP-Seq) is a widely-used molecular method to investigate the function of chromatin-related proteins by identifying their associated DNA sequences on a genomic scale. ChIP-Seq generates large quantities of data that is difficult to process and analyze, particularly for organisms with a contig-based sequenced genomes that typically have minimal annotation on their associated set of genes other than their associated coordinates primarily predicted by gene finding programs. Poorly annotated genome sequence makes comprehensive analysis of ChIP-Seq data difficult and as such standardized analysis pipelines are lacking. RESULTS:We present a one-stop computational pipeline, "Rapid Analysis of ChIP-Seq data" (RACS), that utilizes traditional High-Performance Computing (HPC) techniques in association with open source tools for processing and analyzing raw ChIP-Seq data. RACS is an open source computational pipeline available from any of the following repositories https://bitbucket.org/mjponce/RACS or https://gitrepos.scinet.utoronto.ca/public/?a=summary&p=RACS . RACS is particularly useful for ChIP-Seq in organisms with contig-based genomes that have poor gene annotation to aid protein function discovery.To test the performance and efficiency of RACS, we analyzed ChIP-Seq data previously published in a model organism Tetrahymena thermophila which has a contig-based genome. We assessed the generality of RACS by analyzing a previously published data set generated using the model organism Oxytricha trifallax, whose genome sequence is also contig-based with poor annotation. CONCLUSIONS:The RACS computational pipeline presented in this report is an efficient and reliable tool to analyze genome-wide raw ChIP-Seq data generated in model organisms with poorly annotated contig-based genome sequence. Because RACS segregates the found read accumulations between genic and intergenic regions, it is particularly efficient for rapid downstream analyses of proteins involved in gene expression.

SUBMITTER: Saettone A

PROVIDER: S-EPMC6819487 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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RACS: rapid analysis of ChIP-Seq data for contig based genomes.

Saettone Alejandro A Ponce Marcelo M Nabeel-Shah Syed S Fillingham Jeffrey J

BMC bioinformatics 20191029 1

<h4>Background</h4>Chromatin immunoprecipitation coupled to next generation sequencing (ChIP-Seq) is a widely-used molecular method to investigate the function of chromatin-related proteins by identifying their associated DNA sequences on a genomic scale. ChIP-Seq generates large quantities of data that is difficult to process and analyze, particularly for organisms with a contig-based sequenced genomes that typically have minimal annotation on their associated set of genes other than their asso ...[more]

PMID: 31664892

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Project description:ChIP-seq has become the primary method for identifying in vivo protein-DNA interactions on a genome-wide scale, with nearly 800 publications involving the technique appearing in PubMed as of December 2012. Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ?25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets (i.e., no immunoprecipitation and mock immunoprecipitation samples) display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.

Dataset Information

RACS: rapid analysis of ChIP-Seq data for contig based genomes.

Publications

RACS: rapid analysis of ChIP-Seq data for contig based genomes.

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