Computational approaches and challenges for identification and annotation of non-coding RNAs using RNA-Seq

Kiran Dindhoria; Isha Monga; Amarinder Singh Thind

doi:10.1007/s10142-022-00915-y

Computational approaches and challenges for identification and annotation of non-coding RNAs using RNA-Seq

Funct Integr Genomics. 2022 Dec;22(6):1105-1112. doi: 10.1007/s10142-022-00915-y. Epub 2022 Nov 21.

Authors

Kiran Dindhoria¹, Isha Monga², Amarinder Singh Thind^{3

4}

Affiliations

¹ Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India.
² Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA.
³ Graduate school of Medicine, University of Wollongong, Wollongong, Australia. athind@uow.edu.au.
⁴ Illawarra Health and Medical Research Institute, Wollongong, Australia. athind@uow.edu.au.

PMID: 36409436
DOI: 10.1007/s10142-022-00915-y

Abstract

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNASeq applications have evolved in conjunction with sequence technology and bioinformatic tool advances. In this review, we explained various computational resources, tools, and bioinformatics analyses advancement for small and large non-coding RNAs. These include non-coding RNAs (ncRNAs) such as piwi, micro, circular, and long ncRNAs. In addition, this article discusses future challenges, single-cell level sequencing for non-coding RNAs, and advantages of using long-read sequencing to annotate lncRNAs.

Keywords: Bioinformatics advancements; Next-generation sequencing; Omics; RNAseq applications; Transcriptomics.

Publication types

Review

MeSH terms

Computational Biology
High-Throughput Nucleotide Sequencing*
RNA, Long Noncoding* / genetics
RNA-Seq

Substances

RNA, Long Noncoding