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. 2009 Apr;7(2):373-88.
doi: 10.1142/s0219720009004126.

Finding non-coding RNAs through genome-scale clustering

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Finding non-coding RNAs through genome-scale clustering

Huei-Hun Tseng et al. J Bioinform Comput Biol. 2009 Apr.

Abstract

Non-coding RNAs (ncRNAs) are transcripts that do not code for proteins. Recent findings have shown that RNA-mediated regulatory mechanisms influence a substantial portion of typical microbial genomes. We present an efficient method for finding potential ncRNAs in bacteria by clustering genomic sequences based on homology inferred from both primary sequence and secondary structure. We evaluate our approach using a set of predominantly Firmicutes sequences. Our results showed that, though primary sequence based-homology search was inaccurate for diverged ncRNA sequences, through our clustering method, we were able to infer motifs that recovered nearly all members of most known ncRNA families. Hence, our method shows promise for discovering new families of ncRNA.

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Figures

Fig. 1
Fig. 1
Merging nodes. (a) Segment x1x3 and x2x4 overlap significantly, so they are merged into a single node representing x1x4 (b) segment x1x3 has a homologous hit with segment y1y3, and x2x4 with y2y4, the result is two nodes, one representing x1x4, and one for y1y4.
Fig. 2
Fig. 2
Consensus motif comparison. (a) Predicted structure of best glycine riboswitch cluster for folded-BLAST. Other homology methods produced highly similar structures and are therefore omitted. (b) Rfam annotated consensus structure.

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