ArticleBMC genomics2012
iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets.
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 11 papers.
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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- Statistical framework for calling allelic imbalance in high-throughput sequencing data.Nature communications · 2025Article
- Article
- Landscape of allele-specific transcription factor binding in the human genome.Nature communications · 2021Article
- regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data.Frontiers in bioengineering and biotechnology · 2020Article
- Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases.Scientific reports · 2019Article
- Association analysis using somatic mutations.PLoS genetics · 2018Article
- An empirical Bayes test for allelic-imbalance detection in ChIP-seq.Biostatistics (Oxford, England) · 2018Article
- A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes.Genome biology · 2017Article
- A Hierarchical Framework for State-Space Matrix Inference and Clustering.The annals of applied statistics · 2016Article
- Integrative Analysis of "-Omics" Data Using Penalty Functions.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundChIP-seq provides new opportunities to study allele-specific protein-DNA binding (ASB). However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles.
resultsWe develop a new method iASeq to address this issue by jointly analyzing multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. Application of iASeq to 77 ChIP-seq samples from 40 ENCODE datasets and 1 genomic DNA sample in GM12878 cells reveals that allele-specificity of multiple proteins are highly correlated, and demonstrates the ability of iASeq to improve allelic inference compared to analyzing each individual dataset separately.
conclusionsiASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB.
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