Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
J Mol Biol. 2023 Jul 15;435(14):167904. doi: 10.1016/j.jmb.2022.167904. Epub 2022 Dec 1.
The multiple sequence alignment (MSA) is the entry point of many RNA structure modeling tasks, such as prediction of RNA secondary structure (rSS) and contacts. However, there are few automated programs for generating high quality MSAs of target RNA molecules. We have developed rMSA, a hierarchical pipeline for sensitive search and accurate alignment of RNA homologs for a target RNA. On a diverse set of 365 non-redundant RNA structures, rMSA significantly outperforms an existing MSA generation method (RNAcmap) by approximately 20% and 5% higher F1-scores for rSS and long-range contact prediction, respectively. rMSA is available at https://zhanggroup.org/rMSA/ and https://github.com/pylelab/rMSA.
多序列比对（MSA）是许多 RNA 结构建模任务的起点，例如 RNA 二级结构（rSS）和接触预测。然而，很少有自动程序可以生成目标 RNA 分子的高质量 MSA。我们开发了 rMSA，这是一种分层管道，用于对目标 RNA 的 RNA 同源物进行敏感搜索和精确比对。在一组 365 个非冗余 RNA 结构的多样性数据集上，rMSA 在 rSS 和远程接触预测方面的 F1 分数分别比现有 MSA 生成方法（RNAcmap）高约 20%和 5%。rMSA 可在 https://zhanggroup.org/rMSA/ 和 https://github.com/pylelab/rMSA 上获得。
