Abstract: [No abstract available]

Abstract: We consider how the orthology/paralogy information can be corrected in order to represent a gene tree, a problem that has recently gained interest in phylogenomics. Interestingly, the problem is related to the Minimum CoGraph Editing problem on the relation graph that represents orthology/paralogy information, where we want to minimize the number of edit operations on the given relation graph in order to obtain a cograph. In this paper we provide both theoretical and experimental results on the Minimum CoGraph Editing problem. On the theoretical side, we provide approximation algorithms for bounded degree relation graphs, for the general problem and for the problem restricted to deletion of edges. On the experimental side, we present a genetic algorithm for Minimum CoGraph Editing and we provide an experimental evaluation of the genetic algorithm on synthetic data.

Abstract: MicroRNAs (miRNAs) are small non-coding RNA molecules that have an important role in a wide range of biological processes, since they interact with specific mRNAs affecting the expression of the corresponding proteins. The role of miRNA can be deeply influenced by Single Nucleotide Polymorphisms (SNPs), in particular in their seed sites, since these variations may modify their affinity with particular transcripts, but they may also generate novel binding capabilities for specific miRNA binding sites or destroy them. Several computational tools for miRNA-target site predictions have been developed, but the obtained results are often not in agreement, making the study the binding sites hard, and the analysis of SNP effects even harder. For these reasons, we developed a web application called Rank miRNA, which allows to retrieve and aggregate the results of three prediction tools, but also to process and compare new input miRNA sequences, allowing the analysis of how variations impact on their function. Therefore, our tool is also able to predict the impact of SNPs (and any other kind of variations) on miRNA-mRNA binding capability and also to find the target genes of (potentially new) miRNA sequences. We evaluated the performance of Rank miRNA on specific human SNPs, which are likely to be involved in several mental disorder diseases, showing the potentiality of our tool in helping the study of miRNA-target interactions.

Abstract: In high-variation genomics datasets, such as found in metagenomics or complex polyploid genome analysis, error detection and variant calling are impeded by the difficulty in discerning sequencing errors from actual biological variation. Confirming base candidates with high frequency of occurrence is no longer a reliable measure, because of the natural variation and the presence of rare bases. This work employs machine learning models to classify bases into erroneous and rare variations, after preselecting potential error candidates with a weighted frequency measure, which aims to focus on unexpected variations by using the inter-sequence pairwise similarity. Different similarity measures are used to account for different types of datasets. Four machine learning models are tested.

Abstract: The human brain is a complex organ. An important first step to understand the function of such network is to model and to analyze its elements and connections, i.e. the connectome, in order to achieve a comprehensive description of the network. In this work we apply the graph theory formalisms to represent the connectomes. The human brain connectomes are usually derived from neuroimages; then an atlas-free random parcellation is used to define network nodes of individual brain networks. In this network domine, the question of comparison of the structure of networks arises. Such issue may be modeled as a network alignment (NA) problem. The use of different NA approaches, widely applied in molecular biology, has not been explored in relation to MRI connectomics. In this paper, we first defined the problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.