PATO: genome-wide prediction of lncRNA-DNA triple helices.

Long non-coding RNA (lncRNA) plays a key role in many biological processes. For instance, lncRNA regulates chromatin using different molecular mechanisms, including direct RNA-DNA hybridization via triplexes, cotranscriptional RNA-RNA interactions, and RNA-DNA binding mediated by protein complexes. While the functional annotation of lncRNA transcripts has been widely studied over the last 20 years, barely a handful of tools have been developed with the specific purpose of detecting and evaluating lncRNA-DNA triple helices. What is worse, some of these tools have nearly grown a decade old, making new triplex-centric pipelines depend on legacy software that cannot thoroughly process all the data made available by next-generation sequencing (NGS) technologies. We present PATO, a modern, fast, and efficient tool for the detection of lncRNA-DNA triplexes that matches NGS processing capabilities. PATO enables the prediction of triple helices at the genome scale and can process in as little as 1 h more than 60 GB of sequence data using a two-socket server. Moreover, PATO's efficiency allows a more exhaustive search of the triplex-forming solution space, and so PATO achieves higher levels of prediction accuracy in far less time than other tools in the state of the art. Source code, user manual, and tests are freely available to download under the MIT License at https://github.com/UDC-GAC/pato.

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© The Author(s) 2023. Published by Oxford University Press.