Affiliations: Australian Research Council (ARC) Centre of Excellence
for Integrative Legume Research and Bioinformatics Laboratory, Research School
of Biological Sciences, Australian National University, Canberra,
Australia | Life Sciences and Statistical Machine Learning Groups,
National ICT Australia, and Research School of Information Sciences and
Engineering, Australian National University, Canberra, Australia
Abstract: Accumulating evidence suggests that that non-coding RNAs (ncRNAs)
play key roles in gene regulation and may form the basis of an inter-gene
communication system. Many ncRNAs are synthesized similar to mRNAs and can be
detected through screening of polyA-rich EST or cDNA libraries. We developed a
computational pipeline to screen EST and genomic sequence data for those
transcribed genes with limited protein coding potential and applied this
pipeline to the model legume Medicago truncatula. This process identified
a set of 503 mRNA-like transcripts that appear not to encode proteins. Further
computational analysis showed that many of these ncRNA candidates share
structural similarities to known ncRNAs and that they clearly differ from
protein coding genes and non-transcribed regions in their base and
oligonucleotide compositions, as well as in aspects of secondary structure. By
using a machine learning approach, we show that the distinctive ncRNA features
presented in this study can be used to discriminate most ncRNAs and may thus be
useful for improving ncRNA prediction. Computational analysis of EST isolation
frequencies in various plant tissues showed that the expression levels and
expression profiles of the putative ncRNAs and mRNAs differ – most
interestingly, the putative ncRNAs are highly expressed relative to mRNAs in
the root nodule tissue and conserved only in closely related plants. The work
presented here constitutes the first large-scale prediction and
characterization of ncRNAs in legumes, and provides a basis for further
research on elucidating ncRNA function in legume genomics.
Keywords: ncRNA, mRNA-like ncRNA, EST, Medicago truncatula, model legume, SVM, feature classification
