并行构建RNA数据库,广泛预测lncRNA-RNA相互作用

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Applied Computing Review Pub Date : 2023-03-27 DOI:10.1145/3555776.3577772
Iñaki Amatria-Barral, J. González-Domínguez, J. Touriño
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引用次数: 0

摘要

长链非编码RNA序列(lncRNAs)已经完全改变了科学家研究遗传学的方式。虽然一些人认为许多lncrna是虚假转录的结果,但最近的证据表明,它们存在数千个,并且它们具有功能并调节关键的生物过程。对于lncrna的实验表征,已经开发了许多工具,试图预测它们与其他rna的相互作用。然而,一些最快和更准确的工具需要在确定每个lncRNA的相互作用伙伴之前进行缓慢的数据库构建步骤。本文提出了一种新的、高效的并行数据库构建方法。在16节点多核集群上的基准测试结果表明,我们的并行算法构建数据库的速度比市场上仅使用256个CPU内核的其他工具快318倍。本工作中开发的所有代码都可以根据MIT许可证(https://github.com/UDC-GAC/pRIblast)在GitHub上下载。
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Parallel construction of RNA databases for extensive lncRNA-RNA interaction prediction
Long non-coding RNA sequences (lncRNAs) have completely changed how scientists approach genetics. While some believe that many lncRNAs are results of spurious transcriptions, recent evidence suggests that there exist thousands of them and that they have functions and regulate key biological processes. For the experimental characterization of lncRNAs, many tools that try to predict their interactions with other RNAs have been developed. Some of the fastest and more accurate tools, however, require a slow database construction step prior to the identification of interaction partners for each lncRNA. This paper presents a novel and efficient parallel database construction procedure. Benchmarking results on a 16-node multicore cluster show that our parallel algorithm can build databases up to 318 times faster than other tools in the market using just 256 CPU cores. All the code developed in this work is available to download at GitHub under the MIT License (https://github.com/UDC-GAC/pRIblast).
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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