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Open Access Highly Accessed Research article

Computational prediction and experimental validation of Ciona intestinalis microRNA genes

Trina M Norden-Krichmar1*, Janette Holtz2, Amy E Pasquinelli2 and Terry Gaasterland1

Author Affiliations

1 Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, MSC 0202, La Jolla, CA 92093 USA

2 Department of Biology, University of California, San Diego, La Jolla, CA 92093 USA

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BMC Genomics 2007, 8:445  doi:10.1186/1471-2164-8-445

Published: 29 November 2007

Abstract

Background

This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata) whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development.

Results

In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling.

Conclusion

The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.