ArticleBMC genomics2012
Combining multiple ChIP-seq peak detection systems using combinatorial fusion.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- fourSynergy: ensemble-based interaction calling on 4C-seq data using gradient-free optimization.BioData mining · 2026Article
- Improving SDG Classification Precision Using Combinatorial Fusion.Sensors (Basel, Switzerland) · 2022Article
- Human transposon insertion profiling: Analysis, visualization and identification of somatic LINE-1 insertions in ovarian cancer.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- The diversity rank-score function for combining human visual perception systems.Brain informatics · 2016Article
- On the combination of two visual cognition systems using combinatorial fusion.Brain informatics · 2015Article
- A survey of motif finding Web tools for detecting binding site motifs in ChIP-Seq data.Biology direct · 2014Review
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- Practical guidelines for the comprehensive analysis of ChIP-seq data.PLoS computational biology · 2013Article
- Genomics in 2012: challenges and opportunities in the next generation sequencing era.BMC genomics · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to the recent rapid development in ChIP-seq technologies, which uses high-throughput next-generation DNA sequencing to identify the targets of Chromatin Immunoprecipitation, there is an increasing amount of sequencing data being generated that provides us with greater opportunity to analyze genome-wide protein-DNA interactions. In particular, we are interested in evaluating and enhancing computational and statistical techniques for locating protein binding sites. Many peak detection systems have been developed; in this study, we utilize the following six: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator.
resultsWe define two methods to merge and rescore the regions of two peak detection systems and analyze the performance based on average precision and coverage of transcription start sites. The results indicate that ChIP-seq peak detection can be improved by fusion using score or rank combination.
conclusionOur method of combination and fusion analysis would provide a means for generic assessment of available technologies and systems and assist researchers in choosing an appropriate system (or fusion method) for analyzing ChIP-seq data. This analysis offers an alternate approach for increasing true positive rates, while decreasing false positive rates and hence improving the ChIP-seq peak identification process.
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