ArticleBMC bioinformatics2021
Feature selection for RNA cleavage efficiency at specific sites using the LASSO regression model in Arabidopsis thaliana.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 21 citations in OpenAlex.
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- Combined brain network topological metrics with machine learning algorithms to identify essential tremor.Frontiers in neuroscience · 2022Article
- Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm.Frontiers in genetics · 2022Article
- Glycosyltransferase-related long non-coding RNA signature predicts the prognosis of colon adenocarcinoma.Frontiers in oncology · 2022Article
- Machine Learning in the Differentiation of Soft Tissue Neoplasms: Comparison of Fat-Suppressed T2WI and Apparent Diffusion Coefficient (ADC) Features-Based Models.Journal of digital imaging · 2021Article
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5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundRNA degradation is important for the regulation of gene expression. Despite the identification of proteins and sequences related to deadenylation-dependent RNA degradation in plants, endonucleolytic cleavage-dependent RNA degradation has not been studied in detail. Here, we developed truncated RNA end sequencing in Arabidopsis thaliana to identify cleavage sites and evaluate the efficiency of cleavage at each site. Although several features are related to RNA cleavage efficiency, the effect of each feature on cleavage efficiency has not been evaluated by considering multiple putative determinants in A. thaliana.
resultsCleavage site information was acquired from a previous study, and cleavage efficiency at the site level (CS
conclusionsFeature selection using the LASSO regression model in A. thaliana identified 155 features. Correlation coefficients revealed that whole RNA features are important for determining cleavage efficiency in addition to features around the cleavage sites. The LASSO regression model can predict cleavage efficiency in endogenous and exogenous genes using only sequence information. The model revealed the significance of the effect of multiple determinants on cleavage efficiency, suggesting that sequence features are important for RNA degradation mechanisms in A. thaliana.
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