Abstract: Population genetics studies based on the analysis of mtDNA and mitochondrial disease studies have produced a huge quantity of sequence data and related information. These data, classified as RFLPs, mtDNA SNPs, pathogenic mutations, HVS1 and HVS2 sequences, and complete mtDNA sequences, are distributed in databases differently organised:: MITOMAP, HVRBASE, mtSNPs and mtDB. The two latter databases more or less report frequency data associated with the mitochondrial SNPs, while MITOMAP simply associates the mtSNP to the different phenotypes. HmtDB, stores human complete mitochondrial genomes annotated with variability data estimated through the application of specific algorithms implemented in an automatically running Variability Generation Work Flow (VGWF). Another Work Flow, called Classification Work Flow (CWF), is implemented to perform the automatic classification of newly sequenced genomes. The aims of HmtDB are to collect and integrate all human mitochondrial genomes publicly available, to produce and provide the scientific community with site-specific nucleotidic and aminoacidic variability data estimated on all available human mitochondrial genome sequences through the automatic application of VGWF, to allow researchers to analyse their own complete or partial mitochondrial genomes in order to automatically detect the nucleotidic variants respect to the revised Cambridge Reference Sequence (rCRS) and to predict their haplogroup paternity. At present, 1255 genomes classified according to their continental origin are stored in HmtDB.

Abstract: Motivation:Similar to what has been achieved by comparing genome structures and protein sequences, we hope to obtain valuable information about systems evolution by comparing the organization of interaction networks stored in protein interaction databases and by analyzing their variation and conservation. Equally significantly we can learn whether and how to extend the network information obtained experimentally in well-characterized model systems to different organisms.

Abstract: Analysis of inherited diseases and their associated phenotypes is of great importance to gain insight into the underlying genetic interactions and could ultimately give clinically useful insights into disease processes, including complex diseases influenced by multiple genetic loci. Nevertheless, to date few computational contributions have been proposed for this purpose mainly due to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. To enable performing comprehensive evaluations of gene annotations sparsely available in numerous different databanks accessible via Internet, we previously developed GFINDer, a Web server that dynamically aggregates functional annotations of user uploaded gene lists and allows performing their statistical analysis and mining (http://www.bioinformatics.polimi.it/GFINDer/). Exploiting and structuring information present in textual form in the Online Mendelian Inheritance in Man (OMIM) databank, we developed and made available within GFINDer new original modules specifically devoted to the analysis of inherited disorder related genes. They allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease and phenotypic location categories, and statistically analyzing the obtained classifications.

Abstract: Data integration is a fundamental process in Bioinformatics because the enormous quantity of information available is often difficult to interpret. However, using a high performance platform such as GRID, it will be possible to complete important studies to improve the understanding of the biological process. Through facing this challenge, the importance of creating a data management system that guarantees efficiency on a distributed platform has emerged. This study concerns the definition of an innovative tool for the databases management in GRID technology and the implementation of a concrete case of use in integrating biological data. The core software is a Web Service that allows the execution of SQL query on a series of distributed databases, available on different computer, through the SOAP protocol. In this way, through the client, that can be run from a GRID Computing Element, it is possible to interact with the database. The data extracted from each local database are then integrated by the Web Server and sent to the application that asks for them, optimizing the communication times. Through this software it is possible to perform elaborations that involve data access and integration on GRID easily. This Web Service has been tested during the development of an integration pipeline among two important biological databases as UNIPROT and ENSEMBL in order to coordinate different information about a certain protein sequences. Thanks to this distributed system of data access it has been possible to conduct a systematic GRID analysis of the kinase sequence references in these important databases.

Abstract: The Gene Ontology (GO) project is a collaborative effort to construct and apply controlled vocabularies, or ontologies, to facilitate the biologically meaningful annotation of genes and their products in a wide variety of databases. Participating groups include the major model organism databases and other database groups such as the UniProt Consortium (Swiss-Prot + TrEMBL + PIR), the Genome Knowledgebase project, The Institute for Genomic Research (TIGR), and others. The GO project maintains three vocabularies describing different aspects of molecular and cell biology: Molecular function describes activities, such as catalytic or binding activities, at the molecular level. Biological process describes broad objectives, each accomplished by one or more ordered assemblies of molecular functions. Cellular component describes locations where a gene product may act, and includes both subcellular structures and macromolecular complexes. The GO vocabularies were originally developed for the description of gene products in databases, and many annotation data sets are made available to the public by GO Consortium members. The GO vocabularies and annotations are part of community resource that also includes software tools for working with the ontologies and annotations, project documentation, and links to relevant literature. The GO project has also provided a model for the development of ontologies for additional aspects of biology. Chief among the more recently developed vocabularies is the Sequence Ontology (SO), which provides a structured controlled vocabulary for sequence annotation, for the exchange of annotation data and for the description of sequence objects in databases. The SO and other emerging shared, structured vocabularies are publicly available from the Open Biology Ontologies web site (http://obo.sourceforge.net/). Ontologies must meet five criteria for inclusion in OBO: openness, sharable syntax (such as the GO syntax or OWL), orthogonality to other OBO ontologies, shared ID space, and term definitions.

Abstract: The CODE project aims to provide a comparative genomics analysis of curated disease genes families. It integrates experimental and human verified information into automated gene-centric pipelines, which regularly map disease genes and related features across available sequenced metazoan genomes. Of particular interest to the outcome of the project is the semi-automated annotation of non-coding sequences (ncRNA, promoters, enhancers and splice regulators). Considerable attention is paid to the evolutionary clues provided by the analysis in particular when model animals are concerned. Finally, the establishment of a community portal, complementary to the existent international projects, will disseminate the results of the research and augment the annotation of disease genes.

Abstract: Statistical and clustering analyses of gene expression results from high-throughput microarray experiments produce lists of hundreds of genes candidate regulated, or with particular expression profile patterns, in the conditions under study. Independently of the microarray platforms and analysis methods used to identify and cluster differentially expressed genes, the common task any researcher faces is to translate the identified lists of genes into a better understanding of the patho-physiological phenomena involved. To this aim, many biological annotations are available within numerous heterogeneous and widely distributed databases. Although several tools have been developed for annotating lists of genes, most of them do not provide methods to evaluate the relevance of the retrieved annotations for the considered set of genes, or to estimate the functional bias introduced by the gene set present on the specific array used to identify the considered gene list. Lately, few tools have been proposed that use gene annotations provided through the Gene Ontology (GO) [1] controlled vocabularies to enrich lists of genes with biological information. Some of them (e.g. Affymetrix Data Mining Tool, DAVID, FatiGO, GoMiner, MAPPFinder) also present the GO categories more relevant for a given set of genes according to the number of genes of the considered set belonging to a given category, or in relation to their statistical evaluation performed using some basic tests. To extend these functionalities we created GFINDer (i.e. Genome Function INtegrated Discoverer, http://www.medinfopoli.polimi.it/GFINDer/), a web server able to automatically provide large-scale lists of user-classified genes with the statistically significant functional profiles that biologically characterize the different gene classes in a considered gene list.

Abstract: We describe here the rationale, implementation and results of a bioinformatic strategy for large-scale identification and annotation of chromosomal translocations in tumours, based on sequence and annotation comparison between human transcriptome and EST partial cDNA sequences derived from tissues or cell lines. We also illustrate how the sequencing and subsequent careful bioinformatic analysis of a number of identified candidate translocation cDNAs revealed the complexity of distinguishing recombination from true translocation events. Finally, we suggest some EST filtering and cleaning strategy for pursuing EST-based “in silico” translocation identification projects.