ArticleBMC bioinformatics2011
Shape-based peak identification for ChIP-Seq.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
- Hypoxic regulation of chromatin and gene transcription.Communications biology · 2026Article
- Multi-level interaction between HIF and AHR transcriptional pathways in kidney carcinoma.Life science alliance · 2023Article
- Pan-cancer analysis of tissue and single-cell HIF-pathway activation using a conserved gene signature.Cell reports · 2022Article
- Quality control and evaluation of plant epigenomics data.The Plant cell · 2022Article
- Integration of Count Difference and Curve Similarity in Negative Regulatory Element Detection.Frontiers in genetics · 2022Article
- Self-organizing maps with variable neighborhoods facilitate learning of chromatin accessibility signal shapes associated with regulatory elements.BMC bioinformatics · 2021Article
- Co-incidence of RCC-susceptibility polymorphisms with HIF cis-acting sequences supports a pathway tuning model of cancer.Scientific reports · 2019Article
- Inherent DNA-binding specificities of the HIF-1α and HIF-2α transcription factors in chromatin.EMBO reports · 2019Article
- Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling.Nucleic acids research · 2017Article
- Retrotransposons are specified as DNA replication origins in the gene-poor regions of Arabidopsis heterochromatin.Nucleic acids research · 2017Article
- Features that define the best ChIP-seq peak calling algorithms.Briefings in bioinformatics · 2017Article
- The Krüppel-like factor 9 cistrome in mouse hippocampal neurons reveals predominant transcriptional repression via proximal promoter binding.BMC genomics · 2017Article
- Capture-C reveals preformed chromatin interactions between HIF-binding sites and distant promoters.EMBO reports · 2016Article
- Identifying peaks in *-seq data using shape information.BMC bioinformatics · 2016Article
- Peak shape clustering reveals biological insights.BMC bioinformatics · 2015Article
- Genome-wide study of correlations between genomic features and their relationship with the regulation of gene expression.DNA research : an international journal for rapid publication of reports on genes and genomes · 2015Article
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- A survey of motif finding Web tools for detecting binding site motifs in ChIP-Seq data.Biology direct · 2014Review
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3 authors.
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Abstract
backgroundThe identification of binding targets for proteins using ChIP-Seq has gained popularity as an alternative to ChIP-chip. Sequencing can, in principle, eliminate artifacts associated with microarrays, and cheap sequencing offers the ability to sequence deeply and obtain a comprehensive survey of binding. A number of algorithms have been developed to call "peaks" representing bound regions from mapped reads. Most current algorithms incorporate multiple heuristics, and despite much work it remains difficult to accurately determine individual peaks corresponding to distinct binding events.
resultsOur method for identifying statistically significant peaks from read coverage is inspired by the notion of persistence in topological data analysis and provides a non-parametric approach that is statistically sound and robust to noise in experiments. Specifically, our method reduces the peak calling problem to the study of tree-based statistics derived from the data. We validate our approach using previously published data and show that it can discover previously missed regions.
conclusionsThe difficulty in accurately calling peaks for ChIP-Seq data is partly due to the difficulty in defining peaks, and we demonstrate a novel method that improves on the accuracy of previous methods in resolving peaks. Our introduction of a robust statistical test based on ideas from topological data analysis is also novel. Our methods are implemented in a program called T-PIC (Tree shape Peak Identification for ChIP-Seq) is available at http://bio.math.berkeley.edu/tpic/.
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