Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques

Abstract

The majority of the human transcriptome is defined as non-coding RNA (ncRNA), since only a small fraction of human DNA encodes for proteins, as reported by the ENCODE project. Several distinct classes of ncRNAs, such as transfer RNA, microRNA, and long non-coding RNA, have been classified, each with its own three-dimensional folding and specific function. As ncRNAs are highly abundant in living organisms and have been discovered to play important roles in many biological processes, there has been an ever increasing need to investigate the entire ncRNAome in further unbiased detail. Recently, the advent of next-generation sequencing (NGS) technologies has substantially increased the throughput of transcriptome studies, allowing an unprecedented investigation of ncRNAs, as regulatory pathways and novel functions involving ncRNAs are now also emerging. The huge amount of transcript data produced by NGS has progressively required the development and implementation of suitable bioinformatics workflows, complemented by knowledge-based approaches, to identify, classify, and evaluate the expression of hundreds of ncRNAs in normal and pathological conditions, such as cancer. In this mini-review, we present and discuss current bioinformatics advances in the development of such computational approaches to analyze and classify the ncRNA component of human transcriptome sequence data obtained from NGS technologies.

Noncoding RNA: Current Deep Sequencing Data Analysis Approaches and Challenges

“…As extensively seen so far, thanks to the advent of HTS technology we have indeed witnessed a radical improvement in the accuracy and sensitivity of disease characterization in comparison with previous expression profiling techniques, especially in relation to the detection of ncRNAs as we have also previously reported [Veneziano et al., ]. In fact, HTS technology is progressively becoming essential, particularly in the genome‐wide identification and investigation of polymorphisms (SNPs and INDELs) occurring in ncRNA sequences, such as miRNA seed regions (MSRs) as well as within target 3'‐UTRs, which can consequently disrupt miRNA function (http://compbio.uthsc.edu/miRSNP/) [Bhattacharya et al., 2014] in many human diseases, including cancers [Calin et al., ; Gao et al., ; Nicoloso et al., ; Yue et al., ; Brewster et al., ].…”

cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs

“…Currently, high-throughput analytical techniques are massively applied to further the understanding of the non-coding transcriptome [1]. Still, the full complexity of non-coding RNAs is only partially understood.…”

Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

“…During the last ten years, many efforts on lncRNA identification have been made and many approaches have been developed to make a more accurate discrimination. Several studies [26, 27] have summarised and reviewed the approaches of ncRNAs identification and analysis, but a few report the discussion of lncRNAs prediction methods. Wang et al [26] discussed several ncRNA detection methods based on homology information and common features.…”

“…Hence, the summary of these methods is more theoretical than practical. Veneziano et al [27] summarised some computational approaches of ncRNA analysis based on deep sequencing technology. Some lncRNA prediction tools were discussed briefly but many other helpful tools were excluded.…”