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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.

Abstract: The maintenance of biological databases is at present a problem of great interest since the progress made in many experimental procedures has led to an ever increasing amount of data. These data need to be structured and stored in databases and made accessible to the biological community in user-friendly ways. Although both the interest and the need of accessing biological databases are high, the mechanisms to fund their maintenance are unclear. Funding agencies cannot support data annotation in terms of labour costs and hence the development of new tools based on “data miming” technologies could greatly contribute to keep biological databases updated. Here we present a new approach aimed to contribute to the annotation in the HmtDB resource (http://www.hmdb.uniba.it/) of variability data associated to clinical phenotypes [1]. These data are prevalently available in literature where they are reported in a completely free style. Thus, we suggest the construction of a knowledge base derived from browsing papers on web and to be used in the retrieval phase. Nevertheless, problems in extracting data from literature come not only from the heterogeneity of presentation styles but mainly from the unstructured format (i.e. the natural language) in which they are represented. In this scenario, the goal is to feed a knowledge base by identifying occurrences of specific biological entities and their features as well as the particular method and experimental setting of the scientific study adopted in the publication. In this work, we describe some solutions to the problem of structuring information contained in scientific literature in digital (i.e., pdf) or paper format.

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