Key Points
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Gene prioritization aims to integrate complex, heterogeneous data to identify the most promising genes for biological validation among a set of candidates. Its goal is to help biological researchers who face mountains of public and private omics data to maximize the yield of downstream biological validation.
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Prioritization methods leverage prior knowledge of the phenotype or biological process of interest, either in the form of keywords describing the phenotype of interest or of sets of genes that were previously associated to the phenotype or the process. They then either profile data from candidates against this prior knowledge or diffuse this knowledge across a biological network to identify the most closely associated candidates; methods also exist for the case in which little or no prior knowledge is available.
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Gene prioritization has contributed to the discovery of many disease-causing genes. High ranking of a candidate gene in prioritization for a phenotype is now accepted as contributing evidence in proving that mutations in this gene cause the phenotype.
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Numerous prioritization tools are publicly available, often via the Web, and they can easily be used by biologists without specific bioinformatics expertise. Although no tool performs best in all situations, the different tools cover together most experimental situations in which gene prioritization is useful.
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Computational validation of prioritization results — using procedures such as cross-validation, appropriate negative controls and functional enrichment — is essential to guarantee the effectiveness of the prioritization. More complex prioritization strategies are available to increase the effectiveness of prioritization methods further.
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Although prioritization methods are now firmly established, many refinements that improve their performance and usability by biologists can be expected. Moreover, prioritization of sequencing variants identified by next-generation sequencing is emerging as a major need for the biological community, in which data integration can have an important role and for which new prioritization strategies are needed.
Abstract
At different stages of any research project, molecular biologists need to choose — often somewhat arbitrarily, even after careful statistical data analysis — which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets — such as expression data, sequence information, functional annotation and the biomedical literature — allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.
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Acknowledgements
This work was supported in part by the following grants: KUL PFV/10/016 SymBioSys, KUL GOA MaNet, Hercules III PacBio RS and FP7-HEALTH CHeartED.
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Glossary
- Homozygosity mapping
-
A form of recombination mapping that allows the localization of rare recessive traits by identifying unusually long stretches of homozygosity at consecutive markers.
- Guilt by association
-
A statistical rule of thumb that asserts that reliable predictions about the function or disease involvement ('guilt') of a gene or protein can generally be made if several of its partners (for example, genes with correlated expression profiles or protein–protein interaction partners) share a corresponding 'guilty' status ('association').
- Machine learning methods
-
The design and development of algorithms that allow computers automatically to learn to recognize complex patterns in data and to make intelligent decisions on the basis of such data.
- Principal components analysis
-
A statistical method that is used to simplify a complex data set by transforming a series of correlated variables into a smaller number of uncorrelated variables called principal components.
- Interologue
-
A protein–protein interaction that is conserved between orthologous proteins in different species.
- Random walk
-
A mathematical formalization of the path resulting from taking successive random steps. Classical examples of random walks are Brownian motion, the fortune of a gambler flipping a coin or fluctuations of the stock market. In the context of graphs, a random walk typically describes a process in which a 'walker' moves from one node of the graph into another with a probability proportional to the weight of the edge connecting them.
- Diffusion kernel
-
A type of kernel similarity matrix that is derived from the notion of a random walk on a graph. Diffusion kernels measure similarity between nodes of a graph (in this case, between genes) — for example, by estimating the average length of a random walk from one node to the other.
- Locus heterogeneity
-
The appearance of phenotypically similar characteristics that result from mutations at different genetic loci. Differences in effect size or in replication between studies and samples are often ascribed to different loci leading to the same disease.
- Multiple testing
-
A statistical problem that arises from carrying out multiple hypothesis tests together. P values obtained from hypothesis tests under the assumption of a single test must be appropriately corrected to reflect multiple testing.
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Moreau, Y., Tranchevent, LC. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet 13, 523–536 (2012). https://doi.org/10.1038/nrg3253
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DOI: https://doi.org/10.1038/nrg3253
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