A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Selection and Characterization of the most Promising Tools <s> Motivation: Gene prioritization aims at identifying the most promising candidate genes among a large pool of candidates—so as to maximize the yield and biological relevance of further downstream validation experiments and functional studies. During the past few years, several gene prioritization tools have been defined, and some of them have been implemented and made available through freely available web tools. In this study, we aim at comparing the predictive performance of eight publicly available prioritization tools on novel data. We have performed an analysis in which 42 recently reported disease-gene associations from literature are used to benchmark these tools before the underlying databases are updated. ::: ::: Results: Cross-validation on retrospective data provides performance estimate likely to be overoptimistic because some of the data sources are contaminated with knowledge from disease-gene association. Our approach mimics a novel discovery more closely and thus provides more realistic performance estimates. There are, however, marked differences, and tools that rely on more advanced data integration schemes appear more powerful. ::: ::: Contact: yves.moreau@esat.kuleuven.be ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online. <s> BIB001 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Selection and Characterization of the most Promising Tools <s> Motivation: Genome‐wide association studies have identified thousands of loci associated with human disease, but identifying the causal genes at these loci is often difficult. Several methods prioritize genes most likely to be disease causing through the integration of biological data, including protein‐protein interaction and phenotypic data. Data availability is not the same for all genes however, potentially influencing the performance of these methods. Results: We demonstrate that whilst disease genes tend to be associated with greater numbers of data, this may be at least partially a result of them being better studied. With this observation we develop PhenoRank, which prioritizes disease genes whilst avoiding being biased towards genes with more available data. Bias is avoided by comparing gene scores generated for the query disease against gene scores generated using simulated sets of phenotype terms, which ensures that differences in data availability do not affect the ranking of genes. We demonstrate that whilst existing prioritization methods are biased by data availability, PhenoRank is not similarly biased. Avoiding this bias allows PhenoRank to effectively prioritize genes with fewer available data and improves its overall performance. PhenoRank outperforms three available prioritization methods in cross‐validation (PhenoRank area under receiver operating characteristic curve [AUC]=0.89, DADA AUC = 0.87, EXOMISER AUC = 0.71, PRINCE AUC = 0.83, P < 2.2 × 10−16). Availability and implementation: PhenoRank is freely available for download at https://github.com/alexjcornish/PhenoRank. Supplementary information: Supplementary data are available at Bioinformatics online. <s> BIB002
Since many of gene prioritization tools were not available or not supported by their authors, we decided to focus on the most promising ones. The process of selection of the most up-to-date and user-friendly tools for gene prioritization in human disorders is shown in Figure 3 . First of all, we excluded from consideration methods which implementations were not published (i.e. no web link was found), what reduced the list to 84 tools. Next, we kept only open source tools with implementations relying on freely available components and tried to run the remaining 75 tools. After exclusion of tools which were offline, failed to run or executed with errors, the list reduced down to 33 tools. Thus, only 34% of 96 gene prioritization tools published since 2002 remained available for users at the beginning of 2018. Finally, we favored tools with the most up-to-date evidence sources, because the completeness of evidence sources is crucial for gene prioritization tool performance BIB001 . Therefore we checked the dates of the last database update and selected for detailed comparison 14 tools, which have updated their databases since 2016. We categorized these 14 gene prioritization tools according to assumption and prioritization approach they use ( Table 1 ) and types of evidence sources they rely on ( Table 2) . Five tools integrated gene-disease associations and accepted disease terms instead of seed genes. Seven other tools searched for genes associated with user-defined seeds and four of them applied network analysis techniques. Two remaining tools, Phenolyzer and PhenoRank, took into account both gene-gene and gene-disease associations and automatically identified most relevant genes for given diseases terms. Brief descriptions of the methods used by all 14 tools and their additional parameters are provided in Supplementary Table 2 . We also paid attention to interfaces of selected tools because they correspond to the level of expertise required from the user. All of the selected tools, except PhenoRank BIB002 , had web interfaces and therefore were available for a broad audience of users without special programming skills. However, only 6 tools provided programmatic access options (e.g. web API interfaces, scripts or command line tools), advantageous for tasks requiring batch execution. Table 2 : Types of evidence sources used by each of 14 gene prioritization tools.