高通量测序数据中调用等位基因失衡的统计框架

IF 15.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Andrey Buyan, Georgy Meshcheryakov, Viacheslav Safronov, Sergey Abramov, Alexandr Boytsov, Vladimir Nozdrin, Eugene F. Baulin, Semyon Kolmykov, Jeff Vierstra, Fedor Kolpakov, Vsevolod J. Makeev, Ivan V. Kulakovskiy
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引用次数: 0

摘要

高通量测序促进了基因调控的大规模研究,并允许追踪个体基因组变异与基因调控和表达变化的关联。与经典的关联研究相比,杂合变异的等位基因失衡评估以更小的样本量、更高的灵敏度和更好的分辨率捕获功能性变异效应。然而,由于技术和生物变异引起的数据依赖偏差和过度分散,从等位基因读取计数中识别等位基因特异性变异仍然具有挑战性。我们提出MIXALIME,这是一个新的计算框架,用于调用不同组学数据中的等位基因特异性变异,并具有一系列统计模型,用于考虑读映射偏差和拷贝数变化。我们用dna - seq、ATAC-Seq和CAGE-Seq数据对MIXALIME进行基准测试,并证明等位基因失衡突出了GWAS结果中的因果变异。最后,作为MIXALIME大规模实际应用的展示,我们展示了一个展示等位基因特异性染色质可及性的变异图谱,该图谱基于从不同细胞类型获得的数千个可用数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Statistical framework for calling allelic imbalance in high-throughput sequencing data

Statistical framework for calling allelic imbalance in high-throughput sequencing data

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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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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