ArticleBMC bioinformatics2016
Identifying peaks in *-seq data using shape information.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Accurate allocation of multimapped reads enables regulatory element analysis at repeats.Genome research · 2024Article
- Control of resistance against bacteriophage killing by a metabolic regulator in meningitis-associatedProceedings of the National Academy of Sciences of the United States of America · 2022Article
- NCommunications biology · 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
- From reads to insight: a hitchhiker's guide to ATAC-seq data analysis.Genome biology · 2020Review
- Elucidating the Regulon of a FurFrontiers in microbiology · 2020Article
- Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps.PLoS computational biology · 2019Article
- Feedforward regulation of Myc coordinates lineage-specific with housekeeping gene expression during B cell progenitor cell differentiation.PLoS biology · 2019Article
- Identification of new DNA-associated proteins from Waddlia chondrophila.Scientific reports · 2019Article
- Novel Discovery of LINE-1 in a Korean Individual by a Target Enrichment Method.Molecules and cells · 2019Article
- KSHV encoded ORF59 modulates histone arginine methylation of the viral genome to promote viral reactivation.PLoS pathogens · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPeak calling is a fundamental step in the analysis of data generated by ChIP-seq or similar techniques to acquire epigenetics information. Current peak callers are often hard to parameterise and may therefore be difficult to use for non-bioinformaticians. In this paper, we present the ChIP-seq analysis tool available in CLC Genomics Workbench and CLC Genomics Server (version 7.5 and up), a user-friendly peak-caller designed to be not specific to a particular *-seq protocol.
resultsWe illustrate the advantages of a shape-based approach and describe the algorithmic principles underlying the implementation. Thanks to the generality of the idea and the fact the algorithm is able to learn the peak shape from the data, the implementation requires only minimal user input, while still being applicable to a range of *-seq protocols. Using independently validated benchmark datasets, we compare our implementation to other state-of-the-art algorithms explicitly designed to analyse ChIP-seq data and provide an evaluation in terms of receiver-operator characteristic (ROC) plots. In order to show the applicability of the method to similar *-seq protocols, we also investigate algorithmic performances on DNase-seq data.
conclusionsThe results show that CLC shape-based peak caller ranks well among popular state-of-the-art peak callers while providing flexibility and ease-of-use.
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