Predicting long non-coding RNAs using RNA sequencing

Nicholas E Ilott, Chris P Ponting

Research output: Contribution to journalArticlepeer-review

Abstract

The advent of next-generation sequencing, and in particular RNA-sequencing (RNA-seq), technologies has expanded our knowledge of the transcriptional capacity of human and other animal, genomes. In particular, recent RNA-seq studies have revealed that transcription is widespread across the mammalian genome, resulting in a large increase in the number of putative transcripts from both within, and intervening between, known protein-coding genes. Long transcripts that appear to lack protein-coding potential (long non-coding RNAs, lncRNAs) have been the focus of much recent research, in part owing to observations of their cell-type and developmental time-point restricted expression patterns. A variety of sequencing protocols are currently available for identifying lncRNAs including RNA polymerase II occupancy, chromatin state maps and - the focus of this review - deep RNA sequencing. In addition, there are numerous analytical methods available for mapping reads and assembling transcript models that predict the presence and structure of lncRNAs from RNA-seq data. Here we review current methods for identifying lncRNAs using large-scale sequencing data from RNA-seq experiments and highlight analytical considerations that are required when undertaking such projects.

Original languageEnglish
Pages (from-to)50-9
Number of pages10
JournalMethods
Volume63
Issue number1
DOIs
Publication statusPublished - 1 Sep 2013

Keywords

  • Base Sequence
  • Chromatin
  • High-Throughput Nucleotide Sequencing
  • Humans
  • RNA Polymerase II
  • RNA, Long Noncoding
  • Transcription, Genetic

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