BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo

Hongliang Li and Bin Liu

PLOS Computational Biology, 2023, vol. 19, issue 6, 1-19

Abstract: As the key for biological sequence structure and function prediction, disease diagnosis and treatment, biological sequence similarity analysis has attracted more and more attentions. However, the exiting computational methods failed to accurately analyse the biological sequence similarities because of the various data types (DNA, RNA, protein, disease, etc) and their low sequence similarities (remote homology). Therefore, new concepts and techniques are desired to solve this challenging problem. Biological sequences (DNA, RNA and protein sequences) can be considered as the sentences of “the book of life”, and their similarities can be considered as the biological language semantics (BLS). In this study, we are seeking the semantics analysis techniques derived from the natural language processing (NLP) to comprehensively and accurately analyse the biological sequence similarities. 27 semantics analysis methods derived from NLP were introduced to analyse biological sequence similarities, bringing new concepts and techniques to biological sequence similarity analysis. Experimental results show that these semantics analysis methods are able to facilitate the development of protein remote homology detection, circRNA-disease associations identification and protein function annotation, achieving better performance than the other state-of-the-art predictors in the related fields. Based on these semantics analysis methods, a platform called BioSeq-Diabolo has been constructed, which is named after a popular traditional sport in China. The users only need to input the embeddings of the biological sequence data. BioSeq-Diabolo will intelligently identify the task, and then accurately analyse the biological sequence similarities based on biological language semantics. BioSeq-Diabolo will integrate different biological sequence similarities in a supervised manner by using Learning to Rank (LTR), and the performance of the constructed methods will be evaluated and analysed so as to recommend the best methods for the users. The web server and stand-alone package of BioSeq-Diabolo can be accessed at http://bliulab.net/BioSeq-Diabolo/server/.Author summary: Inspired by the similarities between the biological sequences and human languages, we apply the semantics analysis techniques derived from the natural language processing (NLP) to comprehensively and accurately analyse the biological sequence similarities, and propose a platform called BioSeq-Diabolo for intelligently and automatically analysing biological sequence similarities. BioSeq-Diabolo is an important updated version of BioSeq-BLM focusing on the homogeneous and heterogeneous biological sequence similarity analysing, which is beyond the reach of any exiting software tool or platform. BioSeq-Diabolo is named after a popular traditional sport in China whose components reflect its analysing procedures. When playing diabolo, the diabolo stably spins reflecting that the BioSeq-Diabolo is able to automatically construct the optimized predictors, and analyse the corresponding performance for a specific biological sequence similarity analysis task.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011214

DOI: 10.1371/journal.pcbi.1011214

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