Computational Prediction of Novel Non-Coding RNAs in Arabidopsis thaliana

Authors

Dandan Song, Tsinghua University, Beijing 100084, PR China
Yang Yang, Shanghai Jiao Tong University, Shanghai 200240, PR China
Bin Yu, University of Nebraska - LincolnFollow
Binglian Zheng, University of California, Riverside, CA 92521, USA
Zhidong Deng, Tsinghua University, Beijing 100084, PR China
Bao-Liang Lu, Shanghai Center for Systems Biomedicine, Shanghai 200240, PR China
Xuemei Chen, University of California, Riverside, CA 92521, USA
Tao Jiang, University of Nebraska - Lincoln

Document Type

Article

Date of this Version

2009

Comments

Published in BMC Bioinformatics 2009, 10(Suppl 1):S36 doi:10.1186/1471-2105-10-S1-S36 This article is available from: http://www.biomedcentral.com/1471-2105/10/S1/S36 Copyrigth © 2009 Song et al; licensee BioMed Central Ltd. Used by permission.

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 wholegenome 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.