Characterizing protein-DNA binding event subtypes in ChIP-exo data

Yamada, Norimasa; Lai, William; Farrell, Nina; Pugh, B. Franklin; Mahony, Shaun

doi:10.1101/266536

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…These limitations denote that our approach is stringent for the prediction of direct TF-DNA interactions, favoring specificity over sensitivity. The ChIP-seq peaks that our method did not predict to contain direct TF-DNA binding events could be further analyzed to discriminate other mechanisms for protein-DNA interactions from background noise, as proposed in the ChExMix tool established for ChIP-exo data (47) .…”

Section: Discussionmentioning

confidence: 99%

“…ChIP-eat was applied with DiMO-optimized PFMs to the ChIP-exo data sets from (47) , which were lifted over to hg38 using the liftOver tool (17) . As for ChIP-seq peaks, we considered 1,001 bp regions centered around the peak summits.…”

Section: Chip-exo Datamentioning

confidence: 99%

“…We aimed at assessing the performance of ChIP-eat on predicting direct TF-DNA interactions using ChIP-exo data. The ChExMix tool has recently been introduced to characterize protein-DNA binding event subtypes from ChIP-exo peaks (47) . ChExMix predicted different binding event subtypes for ChIP-exo data obtained for the TFs ESR1 and FOXA1, one of these subtypes corresponding to direct TF-DNA interactions (47) .…”

Section: Predictions Of Direct Tf-dna Interactions In Chip-exo Datamentioning

confidence: 99%

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A map of direct TF-DNA interactions in the human genome

Gheorghe

Sandve

Khan

et al. 2018

Preprint

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Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most popular assay to identify genomic regions, called ChIP-seq peaks, that are bound in vivo by transcription factors (TFs). These regions are derived from direct TF-DNA interactions, indirect binding of the TF to the DNA (through a co-binding partner), nonspecific binding to the DNA, and noise/bias/artifacts. Delineating the bona fide direct TF-DNA interactions within the ChIP-seq peaks remains challenging. We developed a dedicated software, ChIP-eat, that combines computational TF binding models and ChIP-seq peaks to automatically predict direct TF-DNA interactions. Our work culminated with predicted interactions covering >4% of the human genome, obtained by uniformly processing 1,983 ChIP-seq peak data sets from the ReMap database for 232 unique TFs. The predictions were a posteriori assessed using protein binding microarray and ChIP-exo data, and were predominantly found in high quality ChIP-seq peaks. The set of predicted direct TF-DNA interactions suggested that high-occupancy target regions are likely not derived from direct binding of the TFs to the DNA. Our predictions derived co-binding TFs supported by protein-protein interaction data and defined cis -regulatory modules enriched for disease-and trait-associated SNPs. Finally, we provide this collection of direct TF-DNA interactions and cis -regulatory modules in the human genome through the UniBind web-interface ( http://unibind.uio.no ).

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Section: Discussionmentioning

confidence: 99%

Section: Chip-exo Datamentioning

confidence: 99%

Section: Predictions Of Direct Tf-dna Interactions In Chip-exo Datamentioning

confidence: 99%

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A map of direct TF-DNA interactions in the human genome

Gheorghe

Sandve

Khan

et al. 2018

Preprint

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ChIPulate : A comprehensive ChIP-seq simulation pipeline

Gopalan

Hannenhalli

Siddharthan

2018

Preprint

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9ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) is a high-throughput technique 10 to identify genomic regions that are bound in vivo by a particular protein, e.g., a transcription fac-11 tor (TF). Biological factors, such as chromatin state, indirect and cooperative binding, as well as 12 experimental factors, such as antibody quality, cross-linking, and PCR biases, are known to affect 13 the outcome of ChIP-seq experiments. However, the relative impact of these factors on inferences 14 made from ChIP-seq data is not entirely clear. Here, via a detailed ChIP-seq simulation pipeline, 15 ChIPulate, we assess the impact of various biological and experimental sources of variation on sev-16 eral outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy 17 of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of 18 replicates needed to confidently infer binding strength. We find that the TF motif can be recovered 19 despite poor and non-uniform extraction and PCR amplification efficiencies. The recovery of the 20 motif is however affected to a larger extent by the fraction of sites that are either cooperatively 21 or indirectly bound. Importantly, our simulations reveal that the number of ChIP-seq replicates 22 needed to accurately measure in vivo occupancy at high-affinity sites is larger than the recom-23 mended community standards. Our results establish statistical limits on the accuracy of inferences 24 of protein-DNA binding from ChIP-seq and suggest that increasing the mean extraction efficiency, 25 rather than amplification efficiency, would better improve sensitivity. The source code and instruc-26 tions for running ChIPulate can be found at https://github.com/vishakad/chipulate. . The author list is alphabetical.[1]. Upon mapping of the DNA fragments bound by the TF to the reference genome, the genomic 31 loci bound by the TF are identified as high density mapped regions or peaks, where each peak is 32 associated with an intensity based on the number of sequenced fragments arising from it. The 33 intensity reflects the in vivo occupancy of the TF at that locus. 34Several studies of ChIP-seq data have focussed on the biological factors distinguishing the loci 35 bound by the TF. It has been shown that in addition to the affinities of binding sites present 36 at a locus, nucleosome positioning is a strong determinant of TF binding in vivo [2, 3, 4, 5]. 37Other studies have shown that the concentration of the target TF [6, 7], short-range cooperative 38 interactions between the target TF and other TFs [8], and variation in chromatin accessibility [5, 7] 39 explain the variation in intensities across peaks. Some of the variation can arise due to indirect 40 binding, where the target TF binds DNA indirectly via a second 10, 11]. The 41 intensity of such peaks is then no longer directly dependent on the affinity of the target TF to 42 sequence at the bound locus. 43Since the distribution of ChIP-seq peaks and the...

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ChIPulate: A comprehensive ChIP-seq simulation pipeline

2019

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ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) is a high-throughput technique to identify genomic regions that are bound in vivo by a particular protein, e.g., a transcription factor (TF). Biological factors, such as chromatin state, indirect and cooperative binding, as well as experimental factors, such as antibody quality, cross-linking, and PCR biases, are known to affect the outcome of ChIP-seq experiments. However, the relative impact of these factors on inferences made from ChIP-seq data is not entirely clear. Here, via a detailed ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength. We find that the TF motif can be recovered despite poor and non-uniform extraction and PCR amplification efficiencies. The recovery of the motif is, however, affected to a larger extent by the fraction of sites that are either cooperatively or indirectly bound. Importantly, our simulations reveal that the number of ChIP-seq replicates needed to accurately measure in vivo occupancy at high-affinity sites is larger than the recommended community standards. Our results establish statistical limits on the accuracy of inferences of protein-DNA binding from ChIP-seq and suggest that increasing the mean extraction efficiency, rather than amplification efficiency, would better improve sensitivity. The source code and instructions for running ChIPulate can be found at https://github.com/vishakad/chipulate .

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Characterizing protein-DNA binding event subtypes in ChIP-exo data

Cited by 3 publications

References 36 publications

A map of direct TF-DNA interactions in the human genome

A map of direct TF-DNA interactions in the human genome

ChIPulate : A comprehensive ChIP-seq simulation pipeline

ChIPulate: A comprehensive ChIP-seq simulation pipeline

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