Manuel Ares

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

TRIzol solubilization and extraction is a relatively recently developed general method for deproteinizing RNA. This method is particularly advantageous in situations where cells or tissues are enriched for endogenous RNases or when separation of cytoplasmic RNA from nuclear RNA is impractical. TRIzol (or TRI Reagent) is a monophasic solution of phenol and guanidinium isothiocyanate that simultaneously solubilizes biological material and denatures protein. After solubilization, the addition of chloroform causes phase separation (much like extraction with phenol:chloroform:isoamyl alcohol), where protein is extracted to the organic phase, DNA resolves at the interface, and RNA remains in the aqueous phase. Therefore, RNA, DNA, and protein can be purified from a single sample (hence, the name TRIzol). TRIzol extraction is also an effective method for isolating small RNAs, such as microRNAs, piwi-associated RNAs, or endogeneous, small interfering RNAs. However, TRIzol is expensive and RNA pellets can be difficult to resuspend. Thus, the use of TRIzol is not recommend when regular phenol extraction is practical.

Regulation of gene expression is executed in many cases by RNA-binding proteins (RBPs) that bind to mRNAs as well as to non-coding RNAs. RBPs recognize their RNA target via specific binding sites on the RNA. Predicting the binding sites of RBPs is known to be a major challenge. We present a new webserver, RBPmap, freely accessible through the website http://rbpmap.technion.ac.il/ for accurate prediction and mapping of RBP binding sites. RBPmap has been developed specifically for mapping RBPs in human, mouse and Drosophila melanogaster genomes, though it supports other organisms too. RBPmap enables the users to select motifs from a large database of experimentally defined motifs. In addition, users can provide any motif of interest, given as either a consensus or a PSSM. The algorithm for mapping the motifs is based on a Weighted-Rank approach, which considers the clustering propensity of the binding sites and the overall tendency of regulatory regions to be conserved. In addition, RBPmap incorporates a position-specific background model, designed uniquely for different genomic regions, such as splice sites, 5' and 3' UTRs, non-coding RNA and intergenic regions. RBPmap was tested on high-throughput RNA-binding experiments and was proved to be highly accurate.