TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions.
TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions.
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Tivan-Indel：一个计算框架，用于注释和预测非编码调节性小插入和缺失。
DOI：
10.1093/bioinformatics/btad060
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发表时间：
2023-02-03
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人类基因组的小插入和小缺失(Sindel)在人类疾病中具有重要意义。非编码Sindel(NC-Sindel)对人类疾病和表型产生影响的一个重要机制是通过调节基因表达。然而，目前的测序实验可能缺乏统计能力和分辨率来精确定位功能Sindel，这是因为较低的次要等位基因频率或较小的效应大小。作为一种替代策略，有监督的机器学习方法可以通过直接预测它们的调节潜力来识别原本被屏蔽的功能SINDER。然而，用于注释和预测调节正弦信号的计算方法，特别是在非编码区，还不够发达。通过利用GTEx中44个组织的顺式表达定量性状座位分析识别的标记NC-SINDELs，以及通用功能注释和大规模表观基因组图谱的汇编，我们开发了非编码INDELL的组织特异性变体注释(Tivan-INDELL)，这是一个用于预测非编码调节性SINDELs的监督计算框架。结果表明，无论是组织内预测还是跨组织预测，Tivan-Indel都取得了最好的预测效果。作为一个独立的评估，我们从GTEx中的全血组织中训练Tivan-Indel，并使用来自独立研究数据库的15种免疫细胞来测试该模型。最后，我们对关键调控区域中的真实和预测的SINDELs进行了浓缩分析，如染色质相互作用、开放的染色质区域和组蛋白修饰位点，并找到了具有生物学意义的浓缩模式。Https://github.com/lichen-lab/TIVAN-indel补充数据可在生物信息学在线上获得。
Small insertion and deletion (sindel) of human genome has an important implication for human disease. One important mechanism for non-coding sindel (nc-sindel) to have an impact on human diseases and phenotypes is through the regulation of gene expression. Nevertheless, current sequencing experiments may lack statistical power and resolution to pinpoint the functional sindel due to lower minor allele frequency or small effect size. As an alternative strategy, a supervised machine learning method can identify the otherwise masked functional sindels by predicting their regulatory potential directly. However, computational methods for annotating and predicting the regulatory sindels, especially in the non-coding regions, are underdeveloped. By leveraging labeled nc-sindels identified by cis-expression quantitative trait loci analyses across 44 tissues in Genotype-Tissue Expression (GTEx), and a compilation of both generic functional annotations and large-scale epigenomic profiles, we develop TIssue-specific Variant Annotation for Non-coding indel (TIVAN-indel), which is a supervised computational framework for predicting non-coding regulatory sindels. As a result, we demonstrate that TIVAN-indel achieves the best prediction performance in both with-tissue prediction and cross-tissue prediction. As an independent evaluation, we train TIVAN-indel from the ‘Whole Blood’ tissue in GTEx and test the model using 15 immune cell types from an independent study named Database of Immune Cell Expression. Lastly, we perform an enrichment analysis for both true and predicted sindels in key regulatory regions such as chromatin interactions, open chromatin regions and histone modification sites, and find biologically meaningful enrichment patterns. https://github.com/lichen-lab/TIVAN-indel Supplementary data are available at Bioinformatics online.
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影响因子：
30.8
作者：
Kircher, Martin;Witten, Daniela M.;Jain, Preti;O'Roak, Brian J.;Cooper, Gregory M.;Shendure, Jay
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Shendure, Jay
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IANNUZZI, MC
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作者：
Karczewski, Konrad J;Francioli, Laurent C;MacArthur, Daniel G
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MacArthur, Daniel G