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Integrative analysis of C. elegans modENCODE ChIP-seq data sets to infer gene regulatory interactions.

ABSTRACT: The C. elegans modENCODE Consortium has defined in vivo binding sites for a large array of transcription factors by ChIP-seq. In this article, we present examples that illustrate how this compendium of ChIP-seq data can drive biological insights not possible with analysis of individual factors. First, we analyze the number of independent factors bound to the same locus, termed transcription factor complexity, and find that low-complexity sites are more likely to respond to altered expression of a single bound transcription factor. Next, we show that comparison of binding sites for the same factor across developmental stages can reveal insight into the regulatory network of that factor, as we find that the transcription factor UNC-62 has distinct binding profiles at different stages due to distinct cofactor co-association as well as tissue-specific alternative splicing. Finally, we describe an approach to infer potential regulators of gene expression changes found in profiling experiments (such as DNA microarrays) by screening these altered genes to identify significant enrichment for targets of a transcription factor identified in ChIP-seq data sets. After confirming that this approach can correctly identify the upstream regulator on expression data sets for which the regulator was previously known, we applied this approach to identify novel candidate regulators of transcriptional changes with age. The analysis revealed nine candidate aging regulators, of which three were previously known to have a role in longevity. We experimentally showed that two of the new candidate aging regulators can extend lifespan when overexpressed, indicating that this approach can identify novel functional regulators of complex processes.

SUBMITTER: Van Nostrand EL

PROVIDER: S-EPMC3668362 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Integrative analysis of C. elegans modENCODE ChIP-seq data sets to infer gene regulatory interactions.

Van Nostrand Eric L EL Kim Stuart K SK

Genome research 20130326 6

The C. elegans modENCODE Consortium has defined in vivo binding sites for a large array of transcription factors by ChIP-seq. In this article, we present examples that illustrate how this compendium of ChIP-seq data can drive biological insights not possible with analysis of individual factors. First, we analyze the number of independent factors bound to the same locus, termed transcription factor complexity, and find that low-complexity sites are more likely to respond to altered expression of ...[more]

PMID: 23531767

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Project description:BACKGROUND:Cell-specific gene expression is controlled by epigenetic modifications and transcription factor binding. While genome-wide maps for these protein-DNA interactions have become widely available, quantitative comparison of the resulting ChIP-Seq data sets remains challenging. Current approaches to detect differentially bound or modified regions are mainly borrowed from RNA-Seq data analysis, thus focusing on total counts of fragments mapped to a region, ignoring any information encoded in the shape of the peaks. RESULTS:Here, we present MMDiff, a robust, broadly applicable method for detecting differences between sequence count data sets. Based on quantifying shape changes in signal profiles, it overcomes challenges imposed by the highly structured nature of the data and the paucity of replicates.We first use a simulated data set to compare the performance of MMDiff with results obtained by four alternative methods. We demonstrate that MMDiff excels when peak profiles change between samples. We next use MMDiff to re-analyse a recent data set of the histone modification H3K4me3 elucidating the establishment of this prominent epigenomic marker. Our empirical analysis shows that the method yields reproducible results across experiments, and is able to detect functional important changes in histone modifications. To further explore the broader applicability of MMDiff, we apply it to two ENCODE data sets: one investigating the histone modification H3K27ac and one measuring the genome-wide binding of the transcription factor CTCF. In both cases, MMDiff proves to be complementary to count-based methods. In addition, we can show that MMDiff is capable of directly detecting changes of homotypic binding events at neighbouring binding sites. MMDiff is readily available as a Bioconductor package. CONCLUSIONS:Our results demonstrate that higher order features of ChIP-Seq peaks carry relevant and often complementary information to total counts, and hence are important in assessing differential histone modifications and transcription factor binding. We have developed a new computational method, MMDiff, that is capable of exploring these features and therefore closes an existing gap in the analysis of ChIP-Seq data sets.

Dataset Information

Integrative analysis of C. elegans modENCODE ChIP-seq data sets to infer gene regulatory interactions.

Publications

Integrative analysis of C. elegans modENCODE ChIP-seq data sets to infer gene regulatory interactions.

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