A Multicassette Gateway Vector Set for High Throughput and Comparative Analyses in Ciona and Vertebrate Embryos

BACKGROUND: The past few years have seen a vast increase in the amount of genomic data available for a growing number of taxa, including sets of full length cDNA clones and cis-regulatory sequences. Large scale cross-species comparisons of protein function and cis-regulatory sequences may help to understand the emergence of specific traits during evolution. PRINCIPAL FINDINGS: To facilitate such comparisons, we developed a Gateway compatible vector set, which can be used to systematically dissect cis-regulatory sequences, and overexpress wild type or tagged proteins in a variety of chordate systems. It was developed and first characterised in the embryos of the ascidian Ciona intestinalis, in which large scale analyses are easier to perform than in vertebrates, owing to the very efficient embryo electroporation protocol available in this organism. Its use was then extended to fish embryos and cultured mammalian cells. CONCLUSION: This versatile vector set opens the way to the mid- to large-scale comparative analyses of protein function and cis-regulatory sequences across chordate evolution. A complete user manual is provided as supplemental material

Mammalian MicroRNA Prediction through a Support Vector Machine Model of Sequence and Structure

BACKGROUND: MicroRNAs (miRNAs) are endogenous small noncoding RNA gene products, on average 22 nt long, found in a wide variety of organisms. They play important regulatory roles by targeting mRNAs for degradation or translational repression. There are 377 known mouse miRNAs and 475 known human miRNAs in the May 2007 release of the miRBase database, the majority of which are conserved between the two species. A number of recent reports imply that it is likely that many mammalian miRNAs remain to be discovered. The possibility that there are more of them expressed at lower levels or in more specialized expression contexts calls for the exploitation of genome sequence information to accelerate their discovery. METHODOLOGY/PRINCIPAL FINDINGS: In this article, we describe a computational method-mirCoS-that uses three support vector machine models sequentially to discover new miRNA candidates in mammalian genomes based on sequence, secondary structure, and conservation. mirCoS can efficiently detect the majority of known miRNAs and predicts an extensive set of hairpin structures based on human-mouse comparisons. In total, 3476 mouse candidates and 3441 human candidates were found. These hairpins are more similar to known miRNAs than to negative controls in several aspects not considered by the prediction algorithm. A significant fraction of predictions is supported by existing expression evidence. CONCLUSIONS/SIGNIFICANCE: Using a novel approach, mirCoS performs comparably to or better than existing miRNA prediction methods, and contributes a significant number of new candidate miRNAs for experimental verification