nocoRNAc: characterization of non-coding RNAs in prokaryotes

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Non-coding RNAs (ncRNAs) play crucial roles in various biological processes, leading to extensive gene discovery.
• However, the non-coding transcriptome remains largely uncharacterized in many organisms.
• Existing experimental methods for ncRNA identification are costly and time-consuming, necessitating efficient computational approaches for genome-wide analysis.

Purpose of the Study:

• To develop a computational method for the genome-wide prediction of functional non-coding RNA transcripts in bacteria.
• To integrate transcriptional feature detection with ncRNA loci prediction to determine accurate transcript coordinates.
• To provide a tool that increases confidence in predicted ncRNA loci, particularly those that are transcribed.

Main Methods:

• Development of NOCORNAc, a program for predicting ncRNA transcripts in bacterial genomes.
• Integration of RNAz and NOCORNAc for genome-wide analysis.
• Application to Streptomyces coelicolor genome, followed by custom microarray analysis to validate predicted transcripts.

Main Results:

• Over 800 putative ncRNA transcripts were detected in Streptomyces coelicolor, predominantly antisense to protein-coding regions.
• Microarray analysis confirmed transcription of over 300 of these elements.
• 38 novel ncRNA genes were identified in intergenic regions, with many exhibiting complex expression patterns and high correlation with protein-coding partners.

Conclusions:

• NOCORNAc facilitates automated characterization of functional ncRNAs by increasing confidence in predicted loci.
• The framework is applicable to whole microbial genomes, not limited to intergenic regions.
• NOCORNAc enhances the discovery of transcribed ncRNAs, including novel genes, aiding further experimental investigation.