RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data

IF 6.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Briefings in bioinformatics Pub Date : 2024-05-26 DOI:10.1093/bib/bbae251

Sandeep Singh, Xinrui Shi, Samuel Haddox, Justin Elfman, Syed Basil Ahmad, Sarah Lynch, Tommy Manley, Claire Piczak, Christopher Phung, Yunan Sun, Aadi Sharma, Hui Li

引用次数: 0

Abstract

Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.

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RTCpredictor：从 RNA 测序数据中识别通读嵌合 RNA

通读嵌合 RNA 被认为是扩大功能转录组的一种手段，一旦被错误调控，就会导致癌症肿瘤的发生。然而，目前的软件工具往往无法预测它们。我们开发了 RTCpredictor，利用快速 ripgrep 工具搜索亲代基因对的所有可能外显子-外显子组合。我们还增加了外显子变异，允许搜索包含常见 SNP 的基因。据我们所知，这是第一种同时提供断点坐标的通读嵌合 RNA 特异预测方法。与其他 10 种流行工具相比，RTCpredictor 在一个模拟数据集和三个真实数据集上实现了高灵敏度。此外，RTCpredictor 对内存的要求更低，执行时间更快，因此非常适合应用于大型数据集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Briefings in bioinformatics 生物-生化研究方法

CiteScore

13.20

自引率

13.70%

发文量

549

审稿时长

6 months

期刊介绍： Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.