ArticleNature communications2025
Statistical framework for calling allelic imbalance in high-throughput sequencing data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Article
- Multi-tissue analyses of allele-specific chromatin accessibility nominate likely functional variants for type 2 diabetes.medRxiv : the preprint server for health sciences · 2026Article
- DirectASRM: uncovering allele-specific post-transcriptional RNA modifications through direct RNA sequencing.Bioinformatics (Oxford, England) · 2026Article
- Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding.Plant communications · 2026Review
- Regulatory risk loci link disrupted androgen response to the pathophysiology of polycystic ovary syndrome.Journal of the Endocrine Society · 2026Article
- Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Nature communications · 2026Article
- Rare regulatory mutations disrupt mesenchymal molecular programs driving endocardial cushion formation in bicuspid aortic valve.Nature communications · 2026Article
- NEXT-scASV: a Nextflow pipeline for allele-specific variant calling from single-cell RNA-seq data.GigaScience · 2026Article
- Ultra-fast variant effect prediction using biophysical transcription factor binding models.Nucleic acids research · 2025Article
- Article
- Statistical framework for calling allelic imbalance in high-throughput sequencing data.Nature communications · 2025Article
- Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene regulation and expression. Compared to classic association studies, the assessment of an allelic imbalance at heterozygous variants captures functional variant effects with smaller sample sizes, higher sensitivity, and better resolution. Yet, identification of allele-specific variants from allelic read counts remains challenging due to data-dependent biases and overdispersion arising from technical and biological variability. We present MIXALIME, a novel computational framework for calling allele-specific variants in diverse omics data with a repertoire of statistical models accounting for read mapping bias and copy number variation. We benchmark MIXALIME with DNase-Seq, ATAC-Seq, and CAGE-Seq data, and we demonstrate that the allelic imbalance highlights causal variants in GWAS results. Finally, as a showcase of the large-scale practical application of MIXALIME, we present an atlas of variants exhibiting allele-specific chromatin accessibility, built from thousands of available datasets obtained from diverse cell types.
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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.