Semenchenko Egor, Tsybulskyi Volodymyr, Meyer Irmtraud M
Laboratory of bioinformatics of RNA Structure and Transcriptome Regulation, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, 13125 Berlin, Germany.
Department of Biology, Chemistry and Pharmacy, Institute of Chemistry and Biochemistry, Thielallee 63, Freie Universität Berlin, 14195 Berlin, Germany.
Nucleic Acids Res. 2025 Apr 10;53(7). doi: 10.1093/nar/gkaf266.
For a few years, it has been possible to experimentally probe the universe of cis and trans RNA-RNA interactions in a transcriptome-wide manner. These experiments give rise to so-called duplex data, i.e. short reads generated via high-throughput sequencing that each encode information on a cis or trans RNA-RNA interaction. These raw duplex data require complex, subsequent computational analyses in order to be interpreted as solid evidence for actual cis and trans RNA-RNA interactions. While several methods have already been proposed to tackle this challenge, almost all of them lack one or more desirable feature-computational efficiency, ability to readily alter the main processing steps and parameter values, p-value estimation for predictions, and interoperability with the common bioinformatics tools for transcriptomics. To overcome these challenges, we present DuplexDiscoverer-a computational method and R package that allows for the efficient, adjustable, and conceptually coherent analysis of duplex data. DuplexDiscoverer is readily adaptable to analysing data from different experimental protocols and its results seamlessly integrate with the most commonly used bioinformatics tools for transcriptomics in R. Most importantly, DuplexDiscoverer generates predictions that are of superior or comparable quality to those of the existing methods while significantly improving time and memory efficiency.
几年来，人们已经能够以全转录组的方式对顺式和反式RNA - RNA相互作用的领域进行实验探测。这些实验产生了所谓的双链数据，即通过高通量测序生成的短读段，每个读段都编码有关顺式或反式RNA - RNA相互作用的信息。这些原始双链数据需要进行复杂的后续计算分析，才能被解释为实际顺式和反式RNA - RNA相互作用的确凿证据。虽然已经提出了几种方法来应对这一挑战，但几乎所有方法都缺乏一个或多个理想的特性——计算效率、易于改变主要处理步骤和参数值的能力、预测的p值估计以及与转录组学常用生物信息学工具的互操作性。为了克服这些挑战，我们提出了DuplexDiscoverer——一种计算方法和R包，它允许对双链数据进行高效、可调整且概念上连贯的分析。DuplexDiscoverer很容易适应分析来自不同实验方案的数据，其结果可以无缝集成到R中最常用的转录组学生物信息学工具中。最重要的是，DuplexDiscoverer生成的预测在质量上优于或可与现有方法相媲美，同时显著提高了时间和内存效率。
