Dataset Information

Widespread misinterpretable ChIP-seq bias in yeast.

ABSTRACT: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to detect genome-wide interactions between a protein of interest and DNA in vivo. Loci showing strong enrichment over adjacent background regions are typically considered to be sites of binding. Insufficient attention has been given to systematic artifacts inherent to the ChIP-seq procedure that might generate a misleading picture of protein binding to certain loci. We show here that unrelated transcription factors appear to consistently bind to the gene bodies of highly transcribed genes in yeast. Strikingly, several types of negative control experiments, including a protein that is not expected to bind chromatin, also showed similar patterns of strong binding within gene bodies. These false positive signals were evident across sequencing platforms and immunoprecipitation protocols, as well as in previously published datasets from other labs. We show that these false positive signals derive from high rates of transcription, and are inherent to the ChIP procedure, although they are exacerbated by sequencing library construction procedures. This expression bias is strong enough that a known transcriptional repressor like Tup1 can erroneously appear to be an activator. Another type of background bias stems from the inherent nucleosomal structure of chromatin, and can potentially make it seem like certain factors bind nucleosomes even when they don't. Our analysis suggests that a mock ChIP sample offers a better normalization control for the expression bias, whereas the ChIP input is more appropriate for the nucleosomal periodicity bias. While these controls alleviate the effect of the biases to some extent, they are unable to eliminate it completely. Caution is therefore warranted regarding the interpretation of data that seemingly show the association of various transcription and chromatin factors with highly transcribed genes in yeast.

SUBMITTER: Park D

PROVIDER: S-EPMC3857294 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Widespread misinterpretable ChIP-seq bias in yeast.

Park Daechan D Lee Yaelim Y Bhupindersingh Gurvani G Iyer Vishwanath R VR

PloS one 20131209 12

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to detect genome-wide interactions between a protein of interest and DNA in vivo. Loci showing strong enrichment over adjacent background regions are typically considered to be sites of binding. Insufficient attention has been given to systematic artifacts inherent to the ChIP-seq procedure that might generate a misleading picture of protein binding to certain loci. We show here that unrelated transcription factors ap ...[more]

PMID: 24349523

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Project description:BackgroundShort-read high-throughput DNA sequencing technologies provide new tools to answer biological questions. However, high cost and low throughput limit their widespread use, particularly in organisms with smaller genomes such as S. cerevisiae. Although ChIP-Seq in mammalian cell lines is replacing array-based ChIP-chip as the standard for transcription factor binding studies, ChIP-Seq in yeast is still underutilized compared to ChIP-chip. We developed a multiplex barcoding system that allows simultaneous sequencing and analysis of multiple samples using Illumina's platform. We applied this method to analyze the chromosomal distributions of three yeast DNA binding proteins (Ste12, Cse4 and RNA PolII) and a reference sample (input DNA) in a single experiment and demonstrate its utility for rapid and accurate results at reduced costs.ResultsWe developed a barcoding ChIP-Seq method for the concurrent analysis of transcription factor binding sites in yeast. Our multiplex strategy generated high quality data that was indistinguishable from data obtained with non-barcoded libraries. None of the barcoded adapters induced differences relative to a non-barcoded adapter when applied to the same DNA sample. We used this method to map the binding sites for Cse4, Ste12 and Pol II throughout the yeast genome and we found 148 binding targets for Cse4, 823 targets for Ste12 and 2508 targets for PolII. Cse4 was strongly bound to all yeast centromeres as expected and the remaining non-centromeric targets correspond to highly expressed genes in rich media. The presence of Cse4 non-centromeric binding sites was not reported previously.ConclusionWe designed a multiplex short-read DNA sequencing method to perform efficient ChIP-Seq in yeast and other small genome model organisms. This method produces accurate results with higher throughput and reduced cost. Given constant improvements in high-throughput sequencing technologies, increasing multiplexing will be possible to further decrease costs per sample and to accelerate the completion of large consortium projects such as modENCODE.

Project description:BACKGROUND: Unraveling transcriptional regulatory networks is a central problem in molecular biology and, in this quest, chromatin immunoprecipitation and sequencing (ChIP-seq) technology has given us the unprecedented ability to identify sites of protein-DNA binding and histone modification genome wide. However, multiple systemic and procedural biases hinder harnessing the full potential of this technology. Previous studies have addressed this problem, but a thorough characterization of different, interacting biases on ChIP-seq signals is still lacking. RESULTS: Here, we present a novel framework where the genome-wide ChIP-seq signal is viewed as being quantifiably influenced by different, measurable sources of bias, which can then be computationally subtracted away. We use a compendium of 123 human ENCODE ChIP-seq datasets to build regression models that tell us how much of a ChIP-seq signal can be attributed to mappability, GC-content, chromatin accessibility, and factors represented in input DNA and IgG controls. When we use the model to separate out these non-binding influences from the ChIP-seq signal, we obtain a purified signal that associates better to TF-DNA-binding motifs than do other measures of peak significance. We also carry out a multiscale analysis that reveals how ChIP-seq signal biases differ across different scales. Finally, we investigate previously reported associations between gene expression and ChIP-seq signals at transcription start sites. We show that our model can be used to discriminate ChIP-seq signals that are truly related to gene expression from those that are merely correlated by virtue of bias-in particular, chromatin accessibility bias, which shows up in ChIP-seq signals and also relates to gene expression. CONCLUSIONS: Our study provides new insights into the behavior of ChIP-seq signal biases and proposes a novel mitigation framework that improves results compared to existing techniques. With ChIP-seq now being the central technology for studying transcriptional regulation, it is most crucial to accurately characterize, quantify, and adjust for the genome-wide effects of biases affecting ChIP-seq. Our study also emphasizes that properly accounting for confounders in ChIP-seq data is of paramount importance for obtaining biologically accurate insights into the workings of the complex regulatory mechanisms in living organisms. R and MATLAB packages implementing the framework can be obtained from http://www.perkinslab.ca/Software.html.

Project description:Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome.We have developed ChIPpeakAnno as a Bioconductor package within the statistical programming environment R to facilitate batch annotation of enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis of gene expression (CAGE) or any experiments resulting in a large number of enriched genomic regions. The binding sites annotated with ChIPpeakAnno can be viewed easily as a table, a pie chart or plotted in histogram form, i.e., the distribution of distances to the nearest genes for each set of peaks. In addition, we have implemented functionalities for determining the significance of overlap between replicates or binding sites among transcription factors within a complex, and for drawing Venn diagrams to visualize the extent of the overlap between replicates. Furthermore, the package includes functionalities to retrieve sequences flanking putative binding sites for PCR amplification, cloning, or motif discovery, and to identify Gene Ontology (GO) terms associated with adjacent genes.ChIPpeakAnno enables batch annotation of the binding sites identified from ChIP-seq, ChIP-chip, CAGE or any technology that results in a large number of enriched genomic regions within the statistical programming environment R. Allowing users to pass their own annotation data such as a different Chromatin immunoprecipitation (ChIP) preparation and a dataset from literature, or existing annotation packages, such as GenomicFeatures and BSgenome, provides flexibility. Tight integration to the biomaRt package enables up-to-date annotation retrieval from the BioMart database.

Dataset Information

Widespread misinterpretable ChIP-seq bias in yeast.

Publications

Widespread misinterpretable ChIP-seq bias in yeast.

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