Computational approaches to disease-gene prediction: rationale, classification and successes

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Abstract

The identification of genes involved in human hereditary diseases often requires the time-consuming and expensive examination of a great number of possible candidate genes, since genome-wide techniques such as linkage analysis and association studies frequently select many hundreds of ‘positional’ candidates. Even considering the positive impact of next-generation sequencing technologies, the prioritization of candidate genes may be an important step for disease-gene identification. In this paper we develop a basic classification scheme for computational approaches to disease-gene prediction and apply it to exhaustively review bioinformatics tools that have been developed for this purpose, focusing on conceptual aspects rather than technical detail and performance. Finally, we discuss some past successes obtained by computational approaches to illustrate their beneficial contribution to medical research.

Even considering the positive impact of next-generation sequencing technologies, the identification of genes involved in hereditary disorders remains a challenging task that can be aided by computational disease gene prediction. We develop a basic classification scheme to review bioinformatics tools that have been developed for this purpose, and discuss some past successes to illustrate their beneficial contribution to medical research.

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