Abstract
Genomic studies and high-throughput experiments often produce large lists of candidate genes among which only a small fraction are truly relevant to the disease, phenotype or biological process of interest. Gene prioritization tackles this problem by ranking candidate genes by profiling candidates across multiple genomic data sources and integrating this heterogeneous information into a global ranking. We describe an extended version of our gene prioritization method, Endeavour, now available for six species and integrating 75 data sources. The performance (Area Under the Curve) of Endeavour on cross-validation benchmarks using ‘gold standard’ gene sets varies from 88% (for human phenotypes) to 95% (for worm gene function). In addition, we have also validated our approach using a time-stamped benchmark derived from the Human Phenotype Ontology, which provides a setting close to prospective validation. With this benchmark, using 3854 novel gene–phenotype associations, we observe a performance of 82%. Altogether, our results indicate that this extended version of Endeavour efficiently prioritizes candidate genes. The Endeavour web server is freely available at https://endeavour.esat.kuleuven.be/.
Highlights
Biologists often use a combination of high-throughput methods, to produce large-scale data and generate hypotheses, and of low-throughput methods, to experimentally validate these hypotheses and create biological knowledge
This problem is conspicuous in medical genetics, with many human complex traits and Mendelian disorders remaining unexplained despite the availability of huge amounts of genome-scale data
An example in medical genetics is the identification of the genomic factors underlying human Mendelian disorders
Summary
Biologists often use a combination of high-throughput methods, to produce large-scale data and generate hypotheses, and of low-throughput methods, to experimentally validate these hypotheses and create biological knowledge. One challenge in current biology is the gap between the large amount of genomic data that are being generated and the pace at which novel knowledge is created from it This problem is conspicuous in medical genetics, with many human complex traits and Mendelian disorders remaining unexplained despite the availability of huge amounts of genome-scale data. In this situation, computational biology aims at reducing this gap by proposing in silico methods that analyze these data to derive hypotheses that can be validated experimentally. Their final result suggests a role for OTX2 (ranked 1st) in human craniofacial development
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