Discriminative pattern discovery for the characterization of different network populations.

MOTIVATION: An interesting problem is to study how gene co-expression varies in two different populations, associated with healthy and unhealthy individuals, respectively. To this aim, two important aspects should be taken into account: (i) in some cases, pairs/groups of genes show collaborative attitudes, emerging in the study of disorders and diseases; (ii) information coming from each single individual may be crucial to capture specific details, at the basis of complex cellular mechanisms; therefore, it is important avoiding to miss potentially powerful information, associated with the single samples. RESULTS: Here, a novel approach is proposed, such that two different input populations are considered, and represented by two datasets of edge-labeled graphs. Each graph is associated to an individual, and the edge label is the co-expression value between the two genes associated to the nodes. Discriminative patterns among graphs belonging to different sample sets are searched for, based on a statistical notion of 'relevance' able to take into account important local similarities, and also collaborative effects, involving the co-expression among multiple genes. Four different gene expression datasets have been analyzed by the proposed approach, each associated to a different disease. An extensive set of experiments show that the extracted patterns significantly characterize important differences between healthy and unhealthy samples, both in the cooperation and in the biological functionality of the involved genes/proteins. Moreover, the provided analysis confirms some results already presented in the literature on genes with a central role for the considered diseases, still allowing to identify novel and useful insights on this aspect. AVAILABILITY AND IMPLEMENTATION: The algorithm has been implemented using the Java programming language. The data underlying this article and the code are available at https://github.com/CriSe92/DiscriminativeSubgraphDiscovery.

Mining sponge phenomena in RNA expression data.

In the last few years, the interactions among competing endogenous RNAs (ceRNAs) have been recognized as a key post-transcriptional regulatory mechanism in cell differentiation, tissue development, and disease. Notably, such sponge phenomena substracting active microRNAs from their silencing targets have been recognized as having a potential oncosuppressive, or oncogenic, role in several cancer types. Hence, the ability to predict sponges from the analysis of large expression data sets (e.g. from international cancer projects) has become an important data mining task in bioinformatics. We present a technique designed to mine sponge phenomena whose presence or absence may discriminate between healthy and unhealthy populations of samples in tumoral or normal expression data sets, thus providing lists of candidates potentially relevant in the pathology. With this aim, we search for pairs of elements acting as ceRNA for a given miRNA, namely, we aim at discovering miRNA-RNA pairs involved in phenomena which are clearly present in one population and almost absent in the other one. The results on tumoral expression data, concerning five different cancer types, confirmed the effectiveness of the approach in mining interesting knowledge. Indeed, 32 out of 33 miRNAs and 22 out of 25 protein-coding genes identified as top scoring in our analysis are corroborated by having been similarly associated with cancer processes in independent studies. In fact, the subset of miRNAs selected by the sponge analysis results in a significant enrichment of annotation for the KEGG32 pathway "microRNAs in cancer" when tested with the commonly used bioinformatic resource DAVID. Moreover, often the cancer datasets where our sponge analysis identified a miRNA as top scoring match the one reported already in the pertaining literature.

FEDRO: a software tool for the automatic discovery of candidate ORFs in plants with c →u RNA editing.

BACKGROUND: RNA editing is an important mechanism for gene expression in plants organelles. It alters the direct transfer of genetic information from DNA to proteins, due to the introduction of differences between RNAs and the corresponding coding DNA sequences. Software tools successful for the search of genes in other organisms not always are able to correctly perform this task in plants organellar genomes. Moreover, the available software tools predicting RNA editing events utilise algorithms that do not account for events which may generate a novel start codon. RESULTS: We present FEDRO, a Java software tool implementing a novel strategy to generate candidate Open Reading Frames (ORFs) resulting from Cytidine to Uridine (c→u) editing substitutions which occur in the mitochondrial genome (mtDNA) of a given input plant. The goal is to predict putative proteins of plants mitochondria that have not been yet annotated. In order to validate the generated ORFs, a screening is performed by checking for sequence similarity or presence in active transcripts of the same or similar organisms. We illustrate the functionalities of our framework on a model organism. CONCLUSIONS: The proposed tool may be used also on other organisms and genomes. FEDRO is publicly available at http://math.unipa.it/rombo/FEDRO .

IP6K gene identification in plant genomes by tag searching.

BACKGROUND: Plants have played a special role in inositol polyphosphate (IP) research since in plant seeds was discovered the first IP, the fully phosphorylated inositol ring of phytic acid (IP6). It is now known that phytic acid is further metabolized by the IP6 Kinases (IP6Ks) to generate IP containing pyro-phosphate moiety. The IP6K are evolutionary conserved enzymes identified in several mammalian, fungi and amoebae species. Although IP6K has not yet been identified in plant chromosomes, there are many clues suggesting its presences in vegetal cells. RESULTS: In this paper we propose a new approach to search for the plant IP6K gene, that lead to the identification in plant genome of a nucleotide sequence corresponding to a specific tag of the IP6K family. Such a tag has been found in all IP6K genes identified up to now, as well as in all genes belonging to the Inositol Polyphosphate Kinases superfamily (IPK). The tag sequence corresponds to the inositol-binding site of the enzyme, and it can be considered as characterizing all IPK genes. To this aim we applied a technique based on motif discovery. We exploited DLSME, a software recently proposed, which allows for the motif structure to be only partially specified by the user. First we applied the new method on mitochondrial DNA (mtDNA) of plants, where such a gene could have been nested, possibly encrypted and hidden by virtue of the editing and/or trans-splicing processes. Then we looked for the gene in nuclear genome of two model plants, Arabidopsis thaliana and Oryza sativa. CONCLUSIONS: The analysis we conducted in plant mitochondria provided the negative, though we argue relevant, result that IP6K does not actually occur in vegetable mtDNA. Very interestingly, the tag search in nuclear genomes lead us to identify a promising sequence in chromosome 5 of Oryza sativa. Further analyses are in course to confirm that this sequence actually corresponds to IP6K mammalian gene.