Integrative annotation scores of variants for impact on RNA binding protein activities

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Bioinformatics Pub Date : 2024-04-01 DOI:10.1093/bioinformatics/btae181

Jingqi Duan, A. Gasch, S. Keleş

{"title":"Integrative annotation scores of variants for impact on RNA binding protein activities","authors":"Jingqi Duan, A. Gasch, S. Keleş","doi":"10.1093/bioinformatics/btae181","DOIUrl":null,"url":null,"abstract":"Abstract Motivation The ENCODE project generated a large collection of eCLIP-seq RNA binding protein (RBP) profiling data with accompanying RNA-seq transcriptomes of shRNA knockdown of RBPs. These data could have utility in understanding the functional impact of genetic variants, however their potential has not been fully exploited. We implement INCA (Integrative annotation scores of variants for impact on RBP activities) as a multi-step genetic variant scoring approach that leverages the ENCODE RBP data together with ClinVar and integrates multiple computational approaches to aggregate evidence. Results INCA evaluates variant impacts on RBP activities by leveraging genotypic differences in cell lines used for eCLIP-seq. We show that INCA provides critical specificity, beyond generic scoring for RBP binding disruption, for candidate variants and their linkage-disequilibrium partners. As a result, it can, on average, augment scoring of 46.2% of the candidate variants beyond generic scoring for RBP binding disruption and aid in variant prioritization for follow-up analysis. Availability and implementation INCA is implemented in R and is available at https://github.com/keleslab/INCA.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btae181","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

Abstract Motivation The ENCODE project generated a large collection of eCLIP-seq RNA binding protein (RBP) profiling data with accompanying RNA-seq transcriptomes of shRNA knockdown of RBPs. These data could have utility in understanding the functional impact of genetic variants, however their potential has not been fully exploited. We implement INCA (Integrative annotation scores of variants for impact on RBP activities) as a multi-step genetic variant scoring approach that leverages the ENCODE RBP data together with ClinVar and integrates multiple computational approaches to aggregate evidence. Results INCA evaluates variant impacts on RBP activities by leveraging genotypic differences in cell lines used for eCLIP-seq. We show that INCA provides critical specificity, beyond generic scoring for RBP binding disruption, for candidate variants and their linkage-disequilibrium partners. As a result, it can, on average, augment scoring of 46.2% of the candidate variants beyond generic scoring for RBP binding disruption and aid in variant prioritization for follow-up analysis. Availability and implementation INCA is implemented in R and is available at https://github.com/keleslab/INCA.

查看原文本刊更多论文

变体对 RNA 结合蛋白活性影响的整合注释得分

摘要研究动机 ENCODE 项目产生了大量的 eCLIP-seq RNA 结合蛋白（RBP）谱分析数据，并附带了 shRNA 敲除 RBP 的 RNA-seq 转录组。这些数据有助于了解遗传变异的功能影响，但其潜力尚未得到充分挖掘。我们采用 INCA（变异对 RBP 活性影响的整合注释评分）作为一种多步骤遗传变异评分方法，该方法利用 ENCODE RBP 数据和 ClinVar，并整合了多种计算方法来汇总证据。结果 INCA 利用 eCLIP-seq 所用细胞系的基因型差异，评估了变异对 RBP 活性的影响。我们发现，INCA 除了对 RBP 结合破坏进行一般评分外，还为候选变异及其链接-失衡伙伴提供了关键的特异性。因此，INCA 平均可为 46.2% 的候选变体提高评分，超过 RBP 结合中断的一般评分，并有助于为后续分析确定变体的优先次序。INCA用R语言实现，可在https://github.com/keleslab/INCA。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.