ArticleBioinformatics (Oxford, England)2015
A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Deep5hmC: Predicting genome-wide 5-Hydroxymethylcytosine landscape via a multimodal deep learning model.bioRxiv : the preprint server for biology · 2024Article
- scaDA: A Novel Statistical Method for Differential Analysis of Single-Cell Chromatin Accessibility Sequencing Data.bioRxiv : the preprint server for biology · 2024Article
- Multi-level interaction between HIF and AHR transcriptional pathways in kidney carcinoma.Life science alliance · 2023Article
- CSSQ: a ChIP-seq signal quantifier pipeline.Frontiers in cell and developmental biology · 2023Article
- Motif elucidation in ChIP-seq datasets with a knockout control.Bioinformatics advances · 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
- ePeak: from replicated chromatin profiling data to epigenomic dynamics.NAR genomics and bioinformatics · 2022Article
- Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection.Genome biology · 2022Article
- Bioinformatics Methods for ChIP-seq Histone Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Integrative Ranking of Enhancer Networks Facilitates the Discovery of Epigenetic Markers in Cancer.Frontiers in genetics · 2021Article
- MAnorm2 for quantitatively comparing groups of ChIP-seq samples.Genome research · 2021Article
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- Genome Architecture Facilitates Phenotypic Plasticity in the Honeybee (Apis mellifera).Molecular biology and evolution · 2020Article
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- Chicdiff: a computational pipeline for detecting differential chromosomal interactions in Capture Hi-C data.Bioinformatics (Oxford, England) · 2019Article
- Colocalization analyses of genomic elements: approaches, recommendations and challenges.Bioinformatics (Oxford, England) · 2019Article
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4 authors.
Funding
Abstract
motivationChIP-seq is a powerful technology to measure the protein binding or histone modification strength in the whole genome scale. Although there are a number of methods available for single ChIP-seq data analysis (e.g. 'peak detection'), rigorous statistical method for quantitative comparison of multiple ChIP-seq datasets with the considerations of data from control experiment, signal to noise ratios, biological variations and multiple-factor experimental designs is under-developed.
resultsIn this work, we develop a statistical method to perform quantitative comparison of multiple ChIP-seq datasets and detect genomic regions showing differential protein binding or histone modification. We first detect peaks from all datasets and then union them to form a single set of candidate regions. The read counts from IP experiment at the candidate regions are assumed to follow Poisson distribution. The underlying Poisson rates are modeled as an experiment-specific function of artifacts and biological signals. We then obtain the estimated biological signals and compare them through the hypothesis testing procedure in a linear model framework. Simulations and real data analyses demonstrate that the proposed method provides more accurate and robust results compared with existing ones. AVAILABILITY AND IMPLEMENTATION: An R software package ChIPComp is freely available at http://web1.sph.emory.edu/users/hwu30/software/ChIPComp.html.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.