A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Validation Data <s> The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorgelike birth defects. The approach described here offers an alternative integrative method for gene discovery. <s> BIB001 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Validation Data <s> G2D (genes to diseases) is a web resource for prioritizing genes as candidates for inherited diseases. It uses three algorithms based on different prioritization strategies. The input to the server is the genomic region where the user is looking for the disease-causing mutation, plus an additional piece of information depending on the algorithm used. This information can either be the disease phenotype (described as an online Mendelian inheritance in man (OMIM) identifier), one or several genes known or suspected to be associated with the disease (defined by their Entrez Gene identifiers), or a second genomic region that has been linked as well to the disease. In the latter case, the tool uses known or predicted interactions between genes in the two regions extracted from the STRING database. The output in every case is an ordered list of candidate genes in the region of interest. For the first two of the three methods, the candidate genes are first retrieved through sequence homology search, then scored accordingly to the corresponding method. This means that some of them will correspond to well-known characterized genes, and others will overlap with predicted genes, thus providing a wider analysis. G2D is publicly available at http://www.ogic.ca/projects/g2d_2/ <s> BIB002 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Validation Data <s> Congenital diaphragmatic hernia (CDH) is characterized by incomplete formation of the diaphragm occurring as either an isolated defect or in association with other anomalies. Genetic factors including aneuploidies and copy number variants are important in the pathogenesis of many cases of CDH, but few single genes have been definitively implicated in human CDH. In this study, we used whole exome sequencing (WES) to identify a paternally inherited novel missense GATA4 variant (c.754C>T; p.R252W) in a familial case of CDH with incomplete penetrance. Phenotypic characterization of the family included magnetic resonance imaging of the chest and abdomen demonstrating asymptomatic defects in the diaphragm in the two “unaffected” missense variant carriers. Screening 96 additional CDH patients identified a de novo heterozygous GATA4 variant (c.848G>A; p.R283H) in a non-isolated CDH patient. In summary, GATA4 is implicated in both familial and sporadic CDH, and our data suggests that WES may be a powerful tool to discover rare variants for CDH. <s> BIB003 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Validation Data <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> BIB004 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Validation Data <s> Prior biological knowledge and phenotype information may help to identify disease genes from human whole-genome and whole-exome sequencing studies. We developed Phenolyzer (http://phenolyzer.usc.edu), a tool that uses prior information to implicate genes involved in diseases. Phenolyzer exhibits superior performance over competing methods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms entered as free text. <s> BIB005
Despite gene prioritization results in a ranked list of genes, in many works, it is turned into a binary classification task, where genes associated with the disease must be distinguished from unrelated ones. The assessment of classification quality requires multiple positive and negative instances, i.e. genes associated and not associated with a disease. Many studies prove the ability of gene prioritization tools to facilitate the identification of genes carrying monogenic diseases variants, e.g. BIB001 , BIB003 . Gene prioritization can also advance identification genes relevant for complex traits, allow selection the most promising candidates for GWAS BIB002 and selection of targets for RNA interference screening  . However, none of these studies represents performance evaluation in its strict sense, with the determination of type I and type II error rates and experimental verification of gene roles. Since experimental testing of a sufficiently large number of genes is expensive and time-consuming, most of the researchers turn to publicly available databases or scientific papers and construct golden datasets of high-confidence curated associations BIB004 , BIB005 . Genes without any evidence of association with the disease are considered as negative exemplars. Finally, positive and negative instances are distributed between testing and training sets (if necessary) and are used to calculate one or several performance measures.