CexoR: an R/Bioconductor package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates

Madrigal, Pedro

doi:10.14806/ej.21.0.837

Cited by 9 publications

(12 citation statements)

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“…Regions bound by the protein of interest are surrounded by pile-ups of 5’ ends reads mapped to the forward and reverse strand and therefore will be emphasized in the qfrag depth profile. This approach differs from previous published peak pairing methods for ChIP-exo [13, 14], in which peaks are detected separately for the forward and reverse strand and subsequently combined into pairs. The saturation-based method we presented for the evaluation ChIP-seq analysis involved a statistical analysis of the number of positions within candidate ChIP-seq peaks to which one or more 5’ read ends mapped.…”

Section: Discussionmentioning

confidence: 99%

“…Although there are software packages specifically developed for ChIP-exo data [13, 14], they do not provide solutions for the extensive preprocessing of the data before peak calling, which comprises quality trimming, adapter clipping and mapping. For ChIP-nexus an additional processing of the mapping has to be performed in order to benefit from the random barcodes.…”

Section: Introductionmentioning

confidence: 99%

“…We will refer to such regions as protected regions, because they are protected from 5’-3’ ( λ ) exonuclease digestion. A number of methods have been developed to estimate the size of the protected region and to call peaks in ChIP-exo data [10, 11, 13, 14]. …”

Section: Introductionmentioning

confidence: 99%

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Q-nexus: a comprehensive and efficient analysis pipeline designed for ChIP-nexus

et al. 2016

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BackgroundChIP-nexus, an extension of the ChIP-exo protocol, can be used to map the borders of protein-bound DNA sequences at nucleotide resolution, requires less input DNA and enables selective PCR duplicate removal using random barcodes. However, the use of random barcodes requires additional preprocessing of the mapping data, which complicates the computational analysis. To date, only a very limited number of software packages are available for the analysis of ChIP-exo data, which have not yet been systematically tested and compared on ChIP-nexus data.ResultsHere, we present a comprehensive software package for ChIP-nexus data that exploits the random barcodes for selective removal of PCR duplicates and for quality control. Furthermore, we developed bespoke methods to estimate the width of the protected region resulting from protein-DNA binding and to infer binding positions from ChIP-nexus data. Finally, we applied our peak calling method as well as the two other methods MACE and MACS2 to the available ChIP-nexus data.ConclusionsThe Q-nexus software is efficient and easy to use. Novel statistics about duplication rates in consideration of random barcodes are calculated. Our method for the estimation of the width of the protected region yields unbiased signatures that are highly reproducible for biological replicates and at the same time very specific for the respective factors analyzed. As judged by the irreproducible discovery rate (IDR), our peak calling algorithm shows a substantially better reproducibility. An implementation of Q-nexus is available at http://charite.github.io/Q/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3164-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Q-nexus: a comprehensive and efficient analysis pipeline designed for ChIP-nexus

et al. 2016

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“…A list of key features of all the tools used in this study is enlisted in Table 1. We have omitted CexoR from this analysis because of its limitations of analyzing samples with low sequencing read depth and coverage (23).…”

Section: Difference Between Chip-seq and Chip-exomentioning

confidence: 99%

Comparative analysis of ChIP-exo peak-callers: impact of data quality, read duplication and binding subtypes

Majumdar

Sharma

2019

Preprint

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Background: ChIP (Chromatin immunoprecipitation)-exo has emerged as an important and versatile improvement over conventional ChIP-seq as it reduces the level of noise, maps the transcription factor (TF) binding location in a very precise manner, upto single base-pair resolution, and enables binding mode prediction. Availability of numerous peak-callers for analyzing ChIP-exo reads has motivated the need to assess their performance and report which tool executes reasonably well for the task.Results: This study has focussed on comparing peak-callers that report direct binding events with those that report indirect binding events. The effect of strandedness of reads and duplication of data on the performance of peak-callers has been investigated. The number of peaks reported by each peak-caller is compared followed by a comparison of the annotated motifs present in the reported peaks. The significance of peaks is assessed based on the presence of a motif in top peaks. Indirect binding tools have been compared on the basis of their ability to identify annotated motifs and predict mode of protein-DNA interaction.Conclusion: By studying the output of the peak-callers investigated in this study, it is concluded that the tools that use self-learning algorithms, i.e. the tools that estimate all the essential parameters from the aligned reads, perform better than the algorithms which require formation of peak-pairs. The latest tools that account for indirect binding of TFs appear to be an upgrade over the available tools, as they are able to reveal valuable information about the mode of binding in addition to direct binding. Furthermore, the quality of ChIP-exo reads have important consequences on the output of data analysis.

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“…We compared ChExMix performance in predicting human CTCF (Rhee and Pugh, 2011) and mouse FoxA2 (Iwafuchi-Doi et al, 2016) binding event locations to that of nine ChIP-exo analysis methods, including MultiGPS (Mahony et al, 2014), GEM (Guo et al, 2012), MACS2 (Zhang et al, 2008), MACE (Wang et al, 2014), PeakXus (Hartonen et al, 2016), Peakzilla (Bardet et al, 2013), Q-nexus (Hansen et al, 2016), DFilter (Kumar et al, 2013), and CexoR (Madrigal, 2015). We excluded ChIP-ePENS (Ye et al, 2016) from our evaluation because it requires paired-end ChIP-exo data.…”

Section: Chexmix Maintains High Accuracy In Predicting Binding Event mentioning

confidence: 99%

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

Yamada

Lai

Farrell

et al. 2018

Preprint

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Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each genomic recruitment mechanism may be associated with distinct motifs, and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5' à 3' exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo sequencing tag patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type, or rely on the presence of DNA motifs to cluster binding events into subtypes.To systematically detect multiple protein-DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype membership of protein-DNA binding events using both ChIP-exo tag enrichment patterns and DNA sequence information, thus offering a principled and robust approach to characterizing binding subtypes in ChIP-exo data.We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells.In these data, ChExMix detects cooperative binding interactions between FoxA1, ERa, and CTCF, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes.

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CexoR: an R/Bioconductor package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates

Cited by 9 publications

References 20 publications

Q-nexus: a comprehensive and efficient analysis pipeline designed for ChIP-nexus

Q-nexus: a comprehensive and efficient analysis pipeline designed for ChIP-nexus

Comparative analysis of ChIP-exo peak-callers: impact of data quality, read duplication and binding subtypes

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

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