ArticleNucleic acids research2016
Differential peak calling of ChIP-seq signals with replicates with THOR.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
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- The endothelial-enriched lncRNA LINC00607 mediates angiogenic function.Basic research in cardiology · 2023Article
- DiffChIPL: a differential peak analysis method for high-throughput sequencing data with biological replicates based on limma.Bioinformatics (Oxford, England) · 2022Article
- Efficient detection and classification of epigenomic changes under multiple conditions.Biometrics · 2022Article
- Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection.Genome biology · 2022Article
- Cell type-specific mechanism of Setd1a heterozygosity in schizophrenia pathogenesis.Science advances · 2022Article
- Transcriptional changes and the role of ONECUT1 in hPSC pancreatic differentiation.Communications biology · 2021Article
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- Protocol for using heterologous spike-ins to normalize for technical variation in chromatin immunoprecipitation.STAR protocols · 2021Article
- Single-nuclei chromatin profiling of ventral midbrain reveals cell identity transcription factors and cell-type-specific gene regulatory variation.Epigenetics & chromatin · 2021Article
- Differential ATAC-seq and ChIP-seq peak detection using ROTS.NAR genomics and bioinformatics · 2021Article
- Long-lived macrophage reprogramming drives spike protein-mediated inflammasome activation in COVID-19.EMBO molecular medicine · 2021Article
- Niche stiffening compromises hair follicle stem cell potential during ageing by reducing bivalent promoter accessibility.Nature cell biology · 2021Article
- Genome-wide histone acetylation analysis reveals altered transcriptional regulation in the Parkinson's disease brain.Molecular neurodegeneration · 2021Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The study of changes in protein-DNA interactions measured by ChIP-seq on dynamic systems, such as cell differentiation, response to treatments or the comparison of healthy and diseased individuals, is still an open challenge. There are few computational methods comparing changes in ChIP-seq signals with replicates. Moreover, none of these previous approaches addresses ChIP-seq specific experimental artefacts arising from studies with biological replicates. We propose THOR, a Hidden Markov Model based approach, to detect differential peaks between pairs of biological conditions with replicates. THOR provides all pre- and post-processing steps required in ChIP-seq analyses. Moreover, we propose a novel normalization approach based on housekeeping genes to deal with cases where replicates have distinct signal-to-noise ratios. To evaluate differential peak calling methods, we delineate a methodology using both biological and simulated data. This includes an evaluation procedure that associates differential peaks with changes in gene expression as well as histone modifications close to these peaks. We evaluate THOR and seven competing methods on data sets with distinct characteristics from in vitro studies with technical replicates to clinical studies of cancer patients. Our evaluation analysis comprises of 13 comparisons between pairs of biological conditions. We show that THOR performs best in all scenarios.
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