Research article

Identification of candidate structured RNAs in the marine organism 'Candidatus Pelagibacter ubique'

Michelle M Meyer¹ , Tyler D Ames¹ , Daniel P Smith⁴ , Zasha Weinberg³ , Michael S Schwalbach⁴ , Stephen J Giovannoni⁴ and Ronald R Breaker^1,2,3

¹Department of Molecular Cellular and Developmental Biology, Yale University, Box 208103, New Haven, CT 06520, USA

²Department of Molecular Biophysics and Biochemistry, Yale University, Box 208103, New Haven, CT 06520, USA

³Howard Hughes Medical Institute, Yale University, Box 208103, New Haven, CT 06520, USA

⁴Department of Microbiology, Oregon State University, Corvallis, OR 97333, USA

author email corresponding author email

BMC Genomics 2009, 10:268doi:10.1186/1471-2164-10-268

Published:	16 June 2009

Abstract

Background

Metagenomic sequence data are proving to be a vast resource for the discovery of biological components. Yet analysis of this data to identify functional RNAs lags behind efforts to characterize protein diversity. The genome of 'Candidatus Pelagibacter ubique' HTCC 1062 is the closest match for approximately 20% of marine metagenomic sequence reads. It is also small, contains little non-coding DNA, and has strikingly low GC content.

Results

To aid the discovery of RNA motifs within the marine metagenome we exploited the genomic properties of 'Cand. P. ubique' by targeting our search to long intergenic regions (IGRs) with relatively high GC content. Analysis of known RNAs (rRNA, tRNA, riboswitches etc.) shows that structured RNAs are significantly enriched in such IGRs. To identify additional candidate structured RNAs, we examined other IGRs with similar characteristics from 'Cand. P. ubique' using comparative genomics approaches in conjunction with marine metagenomic data. Employing this strategy, we discovered four candidate structured RNAs including a new riboswitch class as well as three additional likely cis-regulatory elements that precede genes encoding ribosomal proteins S2 and S12, and the cytoplasmic protein component of the signal recognition particle. We also describe four additional potential RNA motifs with few or no examples occurring outside the metagenomic data.

Conclusion

This work begins the process of identifying functional RNA motifs present in the metagenomic data and illustrates how existing completed genomes may be used to aid in this task.