Predicting long non-coding RNAs using RNA sequencing.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Next-generation sequencing technologies, especially RNA-sequencing (RNA-seq), have significantly advanced our understanding of mammalian genome transcriptional capacity.
• Recent RNA-seq studies indicate pervasive transcription across the genome, identifying numerous novel transcripts, including long non-coding RNAs (lncRNAs).
• lncRNAs are of particular interest due to their cell-type and developmental stage-specific expression patterns.

Purpose of the Study:

• To review current methodologies for identifying long non-coding RNAs (lncRNAs) using large-scale RNA-sequencing data.
• To highlight key analytical considerations for projects focused on lncRNA discovery and characterization via RNA-seq.

Main Methods:

• Deep RNA sequencing as a primary method for lncRNA identification.
• Review of various analytical approaches for read mapping and transcript assembly from RNA-seq data.
• Comparison with other methods like RNA polymerase II occupancy and chromatin state mapping.

Main Results:

• RNA-seq has dramatically increased the identification of putative transcripts, including lncRNAs, across the mammalian genome.
• Widespread transcription is a common feature, occurring both within and between known protein-coding genes.
• lncRNAs exhibit distinct expression patterns, often restricted to specific cell types or developmental time points.

Conclusions:

• Deep RNA sequencing is a powerful tool for discovering and characterizing lncRNAs.
• Effective identification of lncRNAs requires careful consideration of analytical methods for processing RNA-seq data.
• Further research into lncRNA function and regulation is warranted given their prevalence and specific expression patterns.