Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome.

Monga, Isha and Kumar, Manoj (2019) Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome. Methods in molecular biology (Clifton, N.J.), 1912. pp. 215-250. ISSN 1940-6029

Full text not available from this repository. (Request a copy)
Official URL:


microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.

Item Type: Article
Additional Information: Copyright of this article belongs to Springer.
Uncontrolled Keywords: Algorithm; Analysis tools; Database; Machine learning tools; Transcription factor; microRNA
Subjects: Q Science > QR Microbiology
Depositing User: Dr. K.P.S.Sengar
Date Deposited: 18 Mar 2019 12:42
Last Modified: 18 Mar 2019 12:42

Actions (login required)

View Item View Item