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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: 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 at present distributed worldwide in differently organised databases and web sites, not well integrated among them. Several mitochondrial specialised databases and databases related with variability data have been designed and implemented, but generally they are structured as simple databases where data are stored, without the possibility to perform any analysis. Moreover it is not generally possible for the user to submit and contemporarily analyse its own data comparing them with the content of a given database and this is valid both for population genetics data, and for mitochondrial disease data. As far as population genetics data, for example, the problem of sequence classification in haplogroups is becoming more and more important as the improvement of sequencing technologies is increasing the availability of new complete mitochondrial genomes. Indeed up to now the only way to establish the haplogroup paternity of a given mitochondrial sequence is to manually observe its variant sites respect to a reference sequence, referring to literature in order to define its haplogroup-specific polymorphisms. Also as far as mitochondrial disease data, despite the large number of disease-associated mutations already discovered in the last few years, the sequencing of the complete human mt genome is allowing the discovery of new pathogenic mutations. Indeed, up to now, the pathogenicity of mtDNA mutations has been, in most cases, prevalently validated by their segregation with the disease and their consequent loss of function when the mutation involves a structural gene. However, no systematic statistical analysis of the mtDNA SNPs has been performed until now. Here we present the design of a Human Mitochondrial genome DataBase (HMDB) that will collect the complete human mitochondrial genomes publicly available interfaced to analysis programs, allowing the classification of newly sequenced human mitochondrial genomes, and the prediction, through site-specific nucleotidic and aminoacidic analysis[, of the pathogenic potential of mitochondrial polymorphisms.

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Abstract: Collecting, analyzing and extracting useful information from a very large amount of biomedical texts is a difficult task for researchers in biomedicine who need to keep up with scientific advances. Nowadays several domains in medical practice, drug development, and health care require support for such actives such as bioinformatics, medical informatics, clinical genomics, and many other sectors. Moreover, for this particular task, the data to be examined (i.e. textual data) are generally unstructured as in the case of Medline abstracts and the available resources (e.g. PubMed) and as many other textual resources such as medical records, patents etc. and they do not still provide adequate mechanisms for retrieving the required information as well as to help humans in “deeply analyse” very large amount of content. In this work we present a Text-Mining framework aiming to support biomedical researchers in the task of disease-genes relationships identification from scientific abstracts retrieved by querying Medline.

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Abstract: Building integrated bioinformatic platforms is one of the most challenging tasks which Bioinformatics community is dealing with in recent years [1-2]. Facing this task, a number of specific problems arises connected to data integration, integration of specialized tools and algorithms. The solution described in this paper goes in the direction to solve this challenge. It is characterized by two original assumptions: 1) a quite sharp division between the data realm of a bioinformatics analysis and its components in terms of algorithms and processes, 2) the conception of a rigorous algebra that allows researchers to formalize their analyses in terms of atomic process workflows. As a result of this approach two bioinformatics web tools, BioWBI and WEE, have been designed and prototyped by our group to provide researchers with a virtual collaborative workspace in which defining their data-sources, drawing graphically as well as executing analysis workflows. These tools constitute the basic components of a much more general bioinformatic e-workplace.

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