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• W3032912807 abstract "Long noncoding RNA (lncRNA) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. RNA sequencing is used to capture their transcripts from certain cell types or conditions. For some studies, lncRNA interactions with other biomolecules have also been captured, which can give clues to their mechanisms of action. Complementary textit{in silico} methods have been proposed to predict non-coding nature of transcripts and to analyze available RNA interaction data. Here we provide a critical review of such methods and identify associated challenges. Broadly, these can be categorized as reference-based and reference-free or textit{ab initio}, with the former category of methods requiring a comprehensive annotated reference. The textit{ab initio} methods can make use of machine learning classifiers that are trained on features extracted from sequences, making them suitable to predict novel transcripts, especially in non-model species. Machine learning approaches such as Logistic Regression, Support Vector Machines, Random Forest, and Deep Learning are commonly used. Initial approaches relied on basic sequential features to train the model, whereas the use of secondary structural features appears to be a promising approach for functional annotation. However, adding secondary features will result in model complexities, thus demanding an algorithm that can handle it and furthermore, considerably increasing the utilization of computation resources. Computational strategies combining identification and functional annotation which can be easily customized are currently lacking. These can be of immense value to accelerate research in this class of RNAs." @default.
• W3032912807 created "2020-06-12" @default.
• W3032912807 creator A5026685311 @default.
• W3032912807 creator A5056884808 @default.
• W3032912807 creator A5069146692 @default.
• W3032912807 date "2020-06-09" @default.
• W3032912807 modified "2023-09-23" @default.
• W3032912807 title "Computational Approaches to Functionally Annotate Long Noncoding RNA (lncRNA)" @default.
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• W3032912807 doi "https://doi.org/10.20944/preprints202006.0116.v1" @default.
• W3032912807 hasPublicationYear "2020" @default.
• W3032912807 type Work @default.
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