Advances in translational bioinformatics: computational approaches for the hunting of disease genes

Abstract

Over a 100 years ago, William Bateson provided, through his observations of the transmission of alkaptonuria in first cousin offspring, evidence of the application of Mendelian genetics to certain human traits and diseases. His work was corroborated by Archibald Garrod (Archibald AE. The incidence of alkaptonuria: a study in chemical individuality. Lancert 1902;ii:1616-20) and William Farabee (Farabee WC. Inheritance of digital malformations in man. In: Papers of the Peabody Museum of American Archaeology and Ethnology. Cambridge, Mass: Harvard University, 1905; 65-78), who recorded the familial tendencies of inheritance of malformations of human hands and feet. These were the pioneers of the hunt for disease genes that would continue through the century and result in the discovery of hundreds of genes that can be associated with different diseases. Despite many ground-breaking discoveries during the last century, we are far from having a complete understanding of the intricate network of molecular processes involved in diseases, and we are still searching for the cures for most complex diseases. In the last few years, new genome sequencing and other high-throughput experimental techniques have generated vast amounts of molecular and clinical data that contain crucial information with the potential of leading to the next major biomedical discoveries. The need to mine, visualize and integrate these data has motivated the development of several informatics approaches that can broadly be grouped in the research area of 'translational bioinformatics'. This review highlights the latest advances in the field of translational bioinformatics, focusing on the advances of computational techniques to search for and classify disease genes.

Figure 1:

Histogram of cumulative growth of disease gene discovery. Counts from 1981 to 2005 correspond to the number of diseases for which the underlying genetic defect is known. Values for the last 3 years also include some selected diseases for which a genetic association has been reported, but no causation has been shown.

Figure 2:

Distribution of length of proteins from disease and non-disease genes (black and gray, respectively). Disease genes (from OMIM [99]) are significantly longer than non-disease genes (RefSeq [35]).

Figure 3:

Screenshot of a protein domain page from the DMDM website. The query result for the homeodomain, a DNA-binding protein domain, is depicted. For each position of the protein domain, the weblogo is shown aligned to a histogram indicating the number of SNPs and disease mutations known in all the human proteins aligned to the domain (from 318 human proteins). In addition, bars underneath each position indicate the functional sites of the domain (e.g. DNA-binding site). The cursor shows data for domain position number 4 for which 12 mutations associated with disease (from OMIM [99] and Swiss-Prot [100]) and one SNP (from dbSNP [138]) are known. The data show only a subset of the results.