Computational analysis of lncRNA functional signatures and disease regulatory associations

Abstract

The advancements in the areas of gene sequencing and other technologies leavened newlinethe generation of the massive volume of biological data and necessitated the evolution newlineof computational tools for their analysis. Significantly, the trends in machine learning newlineand data mining considerably moved the analysis forward from gene identification newlineto promising personalised treatment. In essence, the bio-big data mining unveiled newlinemany hidden stories of life and helped to understand the syntax and semantics of the newlinelanguage of life. newlineThe earlier research in genetic analysis concentrated on Genomics and Proteomics. Evidence from the high throughput sequencing analysis rescripted the newlinethen prominent central dogma of molecular biology by defining the functions of newlinenon-coding RNAs, which does not convert into proteins. Based on the length of newlinetranscripts, these non-coding RNAs could be short or long. Short non-coding RNAs, newlineincluding microRNA (miRNA) and transfer RNA (tRNA), have been studied extensively for their biological characteristics and functional roles. Long non-coding newlineRNAs (lncRNA) and circular RNAs (circRNAs) are relatively recent entries that newlinebecome popular among non-coding RNAs. Long non-coding RNAs are RNA transcripts with more than 200 nucleotides in length and lack protein-coding potential. newlineRecent studies have shown that lncRNAs play a significant role in controlling gene newlineexpression, epigenetic regulation, and genomic imprinting. Dysregulation of lncRNA newlineexpression may lead to complex diseases like cancers, Alzheimer s, and psoriasis. newlineCompared to DNA, proteins, and other non-coding RNAs, lncRNAs show rare newlineconservation in sequence-structure features, making lncRNA identification and characterisation challenging. For effective machine learning based analysis, efficient newlinefeature representation is important. Identifying the role of lncRNAs in diseases will help to explore the missing links in many disease mechanisms.