A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Although approximately one-quarter of the roughly 4,000 genetically inherited diseases currently recorded in respective databases (LocusLink, OMIM) are already linked to a region of the human genome, about 450 have no known associated gene. Finding disease-related genes requires laborious examination of hundreds of possible candidate genes (sometimes, these are not even annotated; see, for example, refs 3,4). The public availability of the human genome draft sequence has fostered new strategies to map molecular functional features of gene products to complex phenotypic descriptions, such as those of genetically inherited diseases. Owing to recent progress in the systematic annotation of genes using controlled vocabularies, we have developed a scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene. In a benchmark of the system with 100 known disease-associated genes, the disease-associated gene was among the 8 best-scoring genes with a 25% chance, and among the best 30 genes with a 50% chance, showing that there is a relationship between the score of a gene and its likelihood of being associated with a particular disease. The scoring also indicates that for some diseases, the chance of identifying the underlying gene is higher. <s> BIB001 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <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> BIB002 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Genetic mapping provides a powerful approach to identify genes and biological processes underlying any trait influenced by inheritance, including human diseases. We discuss the intellectual foundations of genetic mapping of Mendelian and complex traits in humans, examine lessons emerging from linkage analysis of Mendelian diseases and genome-wide association studies of common diseases, and discuss questions and challenges that lie ahead. <s> BIB003 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Congenital heart defects (CHDs) are the most common major developmental anomalies and the most frequent cause for perinatal mortality, but their etiology remains often obscure. We identified a locus for CHDs on 6q24-q25. Genotype-phenotype correlations in 12 patients carrying a chromosomal deletion on 6q delineated a critical 850 kb region on 6q25.1 harboring five genes. Bioinformatics prioritization of candidate genes in this locus for a role in CHDs identified the TGF-β-activated kinase 1/MAP3K7 binding protein 2 gene (TAB2) as the top-ranking candidate gene. A role for this candidate gene in cardiac development was further supported by its conserved expression in the developing human and zebrafish heart. Moreover, a critical, dosage-sensitive role during development was demonstrated by the cardiac defects observed upon titrated knockdown of tab2 expression in zebrafish embryos. To definitively confirm the role of this candidate gene in CHDs, we performed mutation analysis of TAB2 in 402 patients with a CHD, which revealed two evolutionarily conserved missense mutations. Finally, a balanced translocation was identified, cosegregating with familial CHD. Mapping of the breakpoints demonstrated that this translocation disrupts TAB2. Taken together, these data clearly demonstrate a role for TAB2 in human cardiac development. <s> BIB004 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Whole exome sequencing has become a pivotal methodology for rapid and cost-effective detection of pathogenic variations in Mendelian disorders. A major challenge of this approach is determining the causative mutation from a substantial number of bystander variations that do not play any role in the disease etiology. Current strategies to analyze variations have mainly relied on genetic and functional arguments such as mode of inheritance, conservation, and loss of function prediction. Here, we demonstrate that disease-network analysis provides an additional layer of information to stratify variations even in the presence of incomplete sequencing coverage, a known limitation of exome sequencing. We studied a case of Hereditary Spastic Paraparesis (HSP) in a single inbred Palestinian family. HSP is a group of neuropathological disorders that are characterized by abnormal gait and spasticity of the lower limbs. Forty-five loci have been associated with HSP and lesions in 20 genes have been documented to induce the disorder. We used whole exome sequencing and homozygosity mapping to create a list of possible candidates. After exhausting the genetic and functional arguments, we stratified the remaining candidates according to their similarity to the previously known disease genes. Our analysis implicated the causative mutation in the motor domain of KIF1A, a gene that has not yet associated with HSP, which functions in anterograde axonal transportation. Our strategy can be useful for a large class of disorders that are characterized by locus heterogeneity, particularly when studying disorders in single families. <s> BIB005 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies. <s> BIB006 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Prioritization of most likely etiological genes entails predicting and defining a set of characteristics that are most likely to fit the underlying disease gene and scoring candidates according to their fit to this "perfect disease gene" profile. This requires a full understanding of the disease phenotype, characteristics, and any available data on the underlying genetics of the disease. Public databases provide enormous and ever-growing amounts of information that can be relevant to the prioritization of etiological genes. Computational approaches allow this information to be retrieved in an automated and exhaustive way and can therefore facilitate the comprehensive mining of this information, including its combination with sets of empirically generated data, in the process of identifying most likely candidate disease genes. <s> BIB007 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Finding the most promising genes among large lists of candidate genes has been defined as the gene prioritization problem. It is a recurrent problem in genetics in which genetic conditions are reported to be associated with chromosomal regions. In the last decade, several different computational approaches have been developed to tackle this challenging task. In this study, we review 19 computational solutions for human gene prioritization that are freely accessible as web tools and illustrate their differences. We summarize the various biological problems to which they have been successfully applied. Ultimately, we describe several research directions that could increase the quality and applicability of the tools. In addition we developed a website (http://www.esat.kuleuven.be/gpp) containing detailed information about these and other tools, which is regularly updated. This review and the associated website constitute together a guide to help users select a gene prioritization strategy that suits best their needs. <s> BIB008 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Many efforts are still devoted to the discovery of genes involved with specific phenotypes, in particular, diseases. High-throughput techniques are thus applied frequently to detect dozens or even hundreds of candidate genes. However, the experimental validation of many candidates is often an expensive and time-consuming task. Therefore, a great variety of computational approaches has been developed to support the identification of the most promising candidates for follow-up studies. The biomedical knowledge already available about the disease of interest and related genes is commonly exploited to find new gene-disease associations and to prioritize candidates. In this review, we highlight recent methodological advances in this research field of candidate gene prioritization. We focus on approaches that use network information and integrate heterogeneous data sources. Furthermore, we discuss current benchmarking procedures for evaluating and comparing different prioritization methods. <s> BIB009 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Various studies (such as genetic linkage or 'omics'-based approaches) generate large lists of candidate genes, of which only a minority may be biologically relevant for a phenotype or disease of interest. This Review discusses computational tools for gene prioritization, emphasizing key considerations for how biologists can incorporate these tools into their research, and it includes hands-on tutorials. <s> BIB010 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <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> BIB011 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Recent genome-wide association studies (GWAS) have successfully identified several gene loci associated with multiple sclerosis (MS) susceptibility, severity or interferon-beta (IFN-s) response. However, due to the nature of these studies, the functional relevance of these loci is not yet fully understood. We have utilized a systems biology based approach to explore the genetic interactomes of these MS related traits. We hypothesised that genes and pathways associated with the 3 MS related phenotypes might interact collectively to influence the heterogeneity and unpredictable clinical outcomes observed. Individual genetic interactomes for each trait were constructed and compared, followed by prioritization of common interactors based on their frequencies. Pathway enrichment analyses were performed to highlight shared functional pathways. Biologically relevant genes ABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A and TUBA4A were identified as common to all 3 MS phenotypes. We observed that the highest number of first degree interactors were shared between MS susceptibility and MS severity (p = 1.34×10−79) with UBC as the most prominent first degree interactor for this phenotype pair from the prioritisation analysis. As expected, pairwise comparisons showed that MS susceptibility and severity interactomes shared the highest number of pathways. Pathways from signalling molecules and interaction, and signal transduction categories were found to be highest shared pathways between 3 phenotypes. Finally, FYN was the most common first degree interactor in the MS drugs-gene network. By applying the systems biology based approach, additional significant information can be extracted from GWAS. Results of our interactome analyses are complementary to what is already known in the literature and also highlight some novel interactions which await further experimental validation. Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS. <s> BIB012 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> 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. <s> BIB013 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Bioinformatics aids in the understanding of the biological processes of living beings and the genetic architecture of human diseases. The discovery of disease-related genes improves the diagnosis and therapy design for the disease. To save the cost and time involved in the experimental verification of the candidate genes, computational methods are employed for ranking the genes according to their likelihood of being associated with the disease. Only top-ranked genes are then verified experimentally. A variety of methods have been conceived by the researchers for the prioritization of the disease candidate genes, which differ in the data source being used or the scoring function used for ranking the genes. A review of various aspects of computational disease gene prioritization and its research issues is presented in this article. The aspects covered are gene prioritization process, data sources used, types of prioritization methods, and performance assessment methods. This article provides a brief overview and acts as a quick guide for disease gene prioritization. <s> BIB014 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> BackgroundPrioritization methods have become an useful tool for mining large amounts of data to suggest promising hypotheses in early research stages. Particularly, network-based prioritization tools use a network representation for the interactions between different biological entities to identify novel indirect relationships. However, current network-based prioritization tools are strongly tailored to specific domains of interest (e.g. gene-disease prioritization) and they do not allow to consider networks with more than two types of entities (e.g. genes and diseases). Therefore, the direct application of these methods to accomplish new prioritization tasks is limited.ResultsThis work presents ProphNet, a generic network-based prioritization tool that allows to integrate an arbitrary number of interrelated biological entities to accomplish any prioritization task. We tested the performance of ProphNet in comparison with leading network-based prioritization methods, namely rcNet and DomainRBF, for gene-disease and domain-disease prioritization, respectively. The results obtained by ProphNet show a significant improvement in terms of sensitivity and specificity for both tasks. We also applied ProphNet to disease-gene prioritization on Alzheimer, Diabetes Mellitus Type 2 and Breast Cancer to validate the results and identify putative candidate genes involved in these diseases.ConclusionsProphNet works on top of any heterogeneous network by integrating information of different types of biological entities to rank entities of a specific type according to their degree of relationship with a query set of entities of another type. Our method works by propagating information across data networks and measuring the correlation between the propagated values for a query and a target sets of entities. ProphNet is available at: http://genome2.ugr.es/prophnet. A Matlab implementation of the algorithm is also available at the website. <s> BIB015 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10−8). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms. <s> BIB016 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> BackgroundThe identification of genes and uncovering the role they play in diseases is an important and complex challenge. Genome-wide linkage and association studies have made advancements in identifying genetic variants that underpin human disease. An important challenge now is to identify meaningful disease-associated genes from a long list of candidate genes implicated by these analyses. The application of gene prioritization can enhance our understanding of disease mechanisms and aid in the discovery of drug targets. The integration of protein-protein interaction networks along with disease datasets and contextual information is an important tool in unraveling the molecular basis of diseases.ResultsIn this paper we propose a computational pipeline for the prioritization of disease-gene candidates. Diverse heterogeneous data including: gene-expression, protein-protein interaction network, ontology-based similarity and topological measures and tissue-specific are integrated. The pipeline was applied to prioritize Alzheimer's Disease (AD) genes, whereby a list of 32 prioritized genes was generated. This approach correctly identified key AD susceptible genes: PSEN1 and TRAF1. Biological process enrichment analysis revealed the prioritized genes are modulated in AD pathogenesis including: regulation of neurogenesis and generation of neurons. Relatively high predictive performance (AUC: 0.70) was observed when classifying AD and normal gene expression profiles from individuals using leave-one-out cross validation.ConclusionsThis work provides a foundation for future investigation of diverse heterogeneous data integration for disease-gene prioritization. <s> BIB017 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Over 100 associated genetic loci have been robustly associated with schizophrenia. Gene prioritization and pathway analysis have focused on a priori hypotheses and thus may have been unduly influenced by prior assumptions and missed important causal genes and pathways. Using a data-driven approach, we show that genes in associated loci: (1) are highly expressed in cortical brain areas; (2) are enriched for ion channel pathways (false discovery rates <0.05); and (3) contain 62 genes that are functionally related to each other and hence represent promising candidates for experimental follow up. We validate the relevance of the prioritized genes by showing that they are enriched for rare disruptive variants and de novo variants from schizophrenia sequencing studies (odds ratio 1.67, P = 0.039), and are enriched for genes encoding members of mouse and human postsynaptic density proteomes (odds ratio 4.56, P = 5.00 × 10(-4); odds ratio 2.60, P = 0.049).The authors wish it to be known that, in their opinion, the first 2 authors should be regarded as joint First Author. <s> BIB018 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Background: Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance. Results: We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group. Conclusions: Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon. <s> BIB019 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> In order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly an ... <s> BIB020 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> Abstract Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future. <s> BIB021 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Introduction <s> BackgroundHypertension and bronchial asthma are a major issue for people’s health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235–330 million people globally have been affected by asthma and approximately 250,000–345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets.ResultsUsing ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system.ConclusionsThe application of methods of reconstruction and analysis of gene networks is a productive tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance to the comorbid condition of asthma and hypertension was employed that resulted in prediction of 10 genes, playing the key role in the development of the comorbid condition. The results can be utilised to plan experiments for identification of novel candidate genes along with searching for novel pharmacological targets. <s> BIB022
Gene prioritization problem emerged together with the growth of popularity of genetic linkage analysis. Genetic mapping yielded large loci containing many candidate genes, only a few of which were indeed associated with the investigated phenotype. In order to determine causative gene variants, dozens of candidate genes from those loci had to be examined and it was economically reasonable to start from genes more likely to impact the observed phenotype. Selection of the most promising candidates can be done on the basis of previous knowledge about these genes, e.g. functional associations or similarity with known disease genes. In the early 2000s, aggregation of publicly available sequence data and growth of functional annotation of the human genome enabled automatization of gene prioritization BIB001 . In 2006 Aerts et al. BIB002 prioritized 58 candidate genes from a 2-Mb region of chromosome 22 according to their similarity with known disease genes in ten distinct evidence sources. They predicted YPEL1 as a novel gene involved in atypical DiGeorge syndrome (DGS) and validated this prediction in vivo: knock-down of YPEL1 homolog in Zebrafish embryos led to craniofacial defects and confirmed its role in pharyngeal arch morphogenesis BIB002 . With the advent of high throughput methods, genetic linkage analysis was replaced by Genome-wide association studies (GWAS) BIB003 allowing cheaper, faster and more precise genetic mapping. However, the necessity of ranking long list of candidate genes according to their relevance to phenotypes had not disappeared. Modern high-throughput experiments, such as genome-wide association studies (GWAS) or differential expression studies generate hundreds or thousands of potential associations, requiring further exploration. Some of these associations may appear by chance or due to systematic biases and therefore may be poorly reproducible. In parallel with the simplification of the candidate gene search, the amount of available information about genes increased. This information includes the data on gene-gene interactions or interactions of genes with other biological entities, their involvement in various biological processes, such as the development of disorders or other phenotypic traits. The emergence of various biological databases and the explosive growth of relevant scientific publications further complicated manual evaluation of candidate genes and stimulated the development of computational methods and tools for gene prioritization. Gene prioritization tools were extensively applied for prediction of genes involved in Mendelian BIB002 , BIB004 , BIB005 ,  and complex diseases BIB018 , BIB012 , BIB006 ,  and other Olga Zolotareva is the corresponding author. © 2019, Olga Zolotareva and Maren Kleine published by Walter de Gruyter GmbH, Berlin/Boston. This work is licensed under the Creative Commons Attribution 4.0 Public License. polygenic traits  , BIB016 . In addition to the evaluation of gene relevance for single diseases, gene prioritization was used for the selection of genes potentially responsible for the comorbidity between two complex diseases -asthma and hypertension BIB022 . Moreover, taking into account the predicted importance of candidate genes, i.e. score assigned in the result of prioritization, improved the results of pathway enrichment analysis BIB018 ,  , BIB016 , enhanced models for drug response BIB019 , and disease outcome predictions from gene expression profiles BIB017 . At the moment, hundreds of research papers on gene prioritization have been published  and about a hundred of them describe computational tools. In the course of development of this field, many reviews BIB007 , BIB008 ,  , BIB009 , BIB010 , BIB013 , BIB014 and benchmark BIB011 ,  , BIB020 works have been published. However, either they cover a very small number of tools and are more focused on theoretical aspects BIB021 or describe outdated programs no longer supported. Recently, Seyyedrazzagi and Navimipour  published a comprehensive literature review where they selected 19 gene prioritization methods for comparison. However, only 7 of those 19 works published between 2011 and 2015 provided links to either web service or code. We managed to access five of them and successfully run only one tool, ProphNet BIB015 , which became unavailable during the preparation of this work. Therefore, in contrast with Seyyedrazzagi and Navimipour  , we focused on articles describing computational tools and inspected their availability and usability. This review is aimed to give an updated view of existing gene prioritization tools with a specific focus on the most competitive, user-friendly and up-to-date tools. Here, we provide the classification of gene prioritization techniques, develop criteria for selection of the most up-to date and user-friendly tools and briefly describe selected tools.