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This article is part of the supplement: Selected papers from the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009)

Open Access Research

Computational prediction of novel non-coding RNAs in Arabidopsis thaliana

Dandan Song1, Yang Yang2, Bin Yu36, Binglian Zheng3, Zhidong Deng1, Bao-Liang Lu24, Xuemei Chen3* and Tao Jiang5*

Author Affiliations

1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China

2 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China

3 Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA

4 Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, Shanghai 200240, PR China

5 Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA

6 Current address: The School of Biological Sciences and the Center for Plant Science Innovation, University of Nebraska, Lincoln, NE 68588, USA

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BMC Bioinformatics 2009, 10(Suppl 1):S36  doi:10.1186/1471-2105-10-S1-S36

Published: 30 January 2009

Abstract

Background

Non-coding RNA (ncRNA) genes do not encode proteins but produce functional RNA molecules that play crucial roles in many key biological processes. Recent genome-wide transcriptional profiling studies using tiling arrays in organisms such as human and Arabidopsis have revealed a great number of transcripts, a large portion of which have little or no capability to encode proteins. This unexpected finding suggests that the currently known repertoire of ncRNAs may only represent a small fraction of ncRNAs of the organisms. Thus, efficient and effective prediction of ncRNAs has become an important task in bioinformatics in recent years. Among the available computational methods, the comparative genomic approach seems to be the most powerful to detect ncRNAs. The recent completion of the sequencing of several major plant genomes has made the approach possible for plants.

Results

We have developed a pipeline to predict novel ncRNAs in the Arabidopsis (Arabidopsis thaliana) genome. It starts by comparing the expressed intergenic regions of Arabidopsis as provided in two whole-genome high-density oligo-probe arrays from the literature with the intergenic nucleotide sequences of all completely sequenced plant genomes including rice (Oryza sativa), poplar (Populus trichocarpa), grape (Vitis vinifera), and papaya (Carica papaya). By using multiple sequence alignment, a popular ncRNA prediction program (RNAz), wet-bench experimental validation, protein-coding potential analysis, and stringent screening against various ncRNA databases, the pipeline resulted in 16 families of novel ncRNAs (with a total of 21 ncRNAs).

Conclusion

In this paper, we undertake a genome-wide search for novel ncRNAs in the genome of Arabidopsis by a comparative genomics approach. The identified novel ncRNAs are evolutionarily conserved between Arabidopsis and other recently sequenced plants, and may conduct interesting novel biological functions.