The next generation sequencing field is reaching a solid robustness for what concern the techniques and now most of the weaknesses that characterized the first steps at the beginning of this new era have been mostly fixed or reduced. At the state of art, what still represent a "bottle neck" in finding the candidate disease gene, specially in exome sequencing and genome sequencing studies, is the gene prioritization step. Not always there is the availability of linkage or association data and the number of variants on which focusing remains high. The need to prioritize the genes inside a list could be crucial in a genetic study and many tools are now available. Here we suggest a chapter, taken from “Translational Bioinformatics" collection for PLOS Computational Biology, in which several tools are described with some of their successful applications.
This article is part of the “Translational Bioinformatics" collection for PLOS Computational Biology.
Abstract
Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.
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