PHASIS: A COMPREHENSIVE COMPUTATIONAL TOOL FOR UNRAVELING PHASED SMALL INTERFERING RNA (PHASIRNA) LOCI

Thales Henrique Cherubino Ribeiro; Atul Krakana; Vinicius Andrade Maia; Scott Lewis; Blake C. Meyers

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Submission

Paper Title

PHASIS: A COMPREHENSIVE COMPUTATIONAL TOOL FOR UNRAVELING PHASED SMALL INTERFERING RNA (PHASIRNA) LOCI

Authors

Thales Henrique Cherubino Ribeiro
Atul Krakana
Vinicius Andrade Maia
Scott Lewis
Blake C. Meyers

Modality

Poster

Subject area

Database and Software Development

Publishing Date

21/11/2024

Country of Publishing

Brazil | Brasil

Language of Publishing

Inglês

Paper Page

https://www.even3.com.br/anais/xmeeting-2024/831995-phasis--a-comprehensive-computational-tool-for-unraveling-phased-small-interfering-rna-(phasirna)-loci

ISBN

978-65-272-0843-3

Keywords

small RNAs, phasiRNAs, regulatory networks

Summary

Phased small interfering RNAs (phasiRNAs) are emerging as key players in the intricate landscape of gene regulation across diverse plant species. These short, processed transcripts play crucial roles in a variety of biological regulation processes, including development, plant defense, epigenetic modifications, and hormone response. A thorough comprehension of phasiRNA-generating loci is important for deciphering their regulatory functions, especially in recently sequenced plant genomes. In this study, we introduce Phasis, an innovative computational tool for the discovery and characterization of phasiRNA-producing (PHAS) loci. By leveraging small RNA (sRNA) sequencing libraries and reference genomes/transcriptomes Phasis can identify the characteristic incremental processing of DICER-LIKE (DCL) that is a hallmark feature of phasiRNA biogenesis. Phasis integrates sophisticated analytical methods, including Gaussian Mixture Modeling (GMM), supervised K Nearest Neighbors (KNN) classification, hypergeometric tests, and Wilcoxon rank sum test, ensuring high accuracy in predicting and evaluating phasiRNA-generating loci, with high precision and recall rates. Phasis takes into account multiple discriminative features, including strand bias, sRNA complexity, and the proportion of mapped reads of specific phasing lengths (typically 21- or 24-nucleotides). This analysis framework enables Phasis to discriminate between phasiRNA-producing loci and other RNA clusters, ensuring robust and reliable results. Comparative analyses across plant species underscore Phasis's robust predictive capabilities and its efficacy in identifying novel PHAS efficiently. Using Phasis, we predicted 21-PHAS in maize, coffee, and 24-PHAS in Petunia. In maize, the GMM approach detected 361 PHAS with a high precision of 0.99 but lower recall of 0.73, while the KNN model identified 522 PHAS with slightly lower precision (0.98) but higher recall (0.91). In comparison, PhasTank exhibited good precision (0.98) but lower recall (0.52). For Petunia, Phasis achieved a precision of 0.94 and recall of 0.96, whereas PhasTank failed to predict any PHAS. In coffee, Phasis identified 519 out of 643 expected loci with a precision of 0.95 and recall of 0.81. However, PhasTank did not detect any known 24-PHAS loci in coffee. Accessible under the OSI Artistic License 2.0, Phasis provides comprehensive analytical capabilities, making it a valuable asset for advancing our understanding of sRNA biology.

Title of the Event: 20º Congresso Brasileiro de Bioinformática: X-Meeting 2024
City of the Event: Salvador
Title of the Proceedings of the event: X-Meeting presentations
Name of the Publisher: Even3
Means of Dissemination: Meio Digital

How to cite

RIBEIRO, Thales Henrique Cherubino et al.. PHASIS: A COMPREHENSIVE COMPUTATIONAL TOOL FOR UNRAVELING PHASED SMALL INTERFERING RNA (PHASIRNA) LOCI.. In: X-Meeting presentations. Anais...Salvador(BA) Hotel Deville Prime, 2024. Available in: https//www.even3.com.br/anais/xmeeting-2024/831995-PHASIS--A-COMPREHENSIVE-COMPUTATIONAL-TOOL-FOR-UNRAVELING-PHASED-SMALL-INTERFERING-RNA-(PHASIRNA)-LOCI. Access in: 16/07/2025