A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> An antibacterial agent is provided, which has an action for inhibiting growth of Helicobacter pylori participating to occurrence of chronic gastritis and gastric ulcer, and a highly safe substance is used as an effective component therein. An antibacterial agent for Helicobacter pylori is characterized in that at least one substance selected from the group consisting of ethylenediaminetetraacetic acid and its metal salts is contained as an effective component. <s> BIB001 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> Motivation: Hunting disease genes is a problem of primary importance in biomedical research. Biologists usually approach this problem in two steps: first a set of candidate genes is identified using traditional positional cloning or high-throughput genomics techniques; second, these genes are further investigated and validated in the wet lab, one by one. To speed up discovery and limit the number of costly wet lab experiments, biologists must test the candidate genes starting with the most probable candidates. So far, biologists have relied on literature studies, extensive queries to multiple databases and hunches about expected properties of the disease gene to determine such an ordering. Recently, we have introduced the data mining tool ENDEAVOUR (Aerts et al., 2006), which performs this task automatically by relying on different genome-wide data sources, such as Gene Ontology, literature, microarray, sequence and more. ::: ::: Results: In this article, we present a novel kernel method that operates in the same setting: based on a number of different views on a set of training genes, a prioritization of test genes is obtained. We furthermore provide a thorough learning theoretical analysis of the method's guaranteed performance. Finally, we apply the method to the disease data sets on which ENDEAVOUR (Aerts et al., 2006) has been benchmarked, and report a considerable improvement in empirical performance. ::: ::: Availability: The MATLAB code used in the empirical results will be made publicly available. ::: ::: Contact:tijl.debie@gmail.com or yves.moreau@esat.kuleuven.be <s> BIB002 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> BackgroundThis paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L∞, L1, and L2 MKL. In particular, L2 MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L∞ MKL method. In real biomedical applications, L2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.ResultsWe provide a theoretical analysis of the relationship between the L2 optimization of kernels in the dual problem with the L2 coefficient regularization in the primal problem. Understanding the dual L2 problem grants a unified view on MKL and enables us to extend the L2 method to a wide range of machine learning problems. We implement L2 MKL for ranking and classification problems and compare its performance with the sparse L∞ and the averaging L1 MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. L2 MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel L2 MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing.ConclusionsThis paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in L∞ MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing L2 kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL.AvailabilityThe MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html. <s> BIB003 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist. <s> BIB004 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> Strong evidence suggests that rare mutations of severe effect are responsible for a substantial portion of complex human disease. Evolutionary forces generate vast genetic heterogeneity in human illness by introducing many new variants in each generation. Current sequencing technologies offer the possibility of finding rare disease-causing mutations and the genes that harbor them. <s> BIB005 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases. Here we propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases. <s> BIB006 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <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> Future Directions <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> Future Directions <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> Future Directions <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> Future Directions <s> The discovery of novel disease-associated variations in genes is often a daunting task in highly heterogeneous disease classes. We seek a generalizable algorithm that integrates multiple publicly available genomic data sources in a machine-learning model for the prioritization of candidates identified in patients with retinal disease. To approach this problem, we generate a set of feature vectors from publicly available microarray, RNA-seq, and ChIP-seq datasets of biological relevance to retinal disease, to observe patterns in gene expression specificity among tissues of the body and the eye, in addition to photoreceptor-specific signals by the CRX transcription factor. Using these features, we describe a novel algorithm, positive and unlabeled learning for prioritization (PULP). This article compares several popular supervised learning techniques as the regression function for PULP. The results demonstrate a highly significant enrichment for previously characterized disease genes using a logistic regression method. Finally, a comparison of PULP with the popular gene prioritization tool ENDEAVOUR shows superior prioritization of retinal disease genes from previous studies. The java source code, compiled binary, assembled feature vectors, and instructions are available online at https://github.com/ahwagner/PULP. <s> BIB011 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <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> BIB012 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <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> BIB013 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> Display Omitted We use Hybrid Relative Specificity Similarity algorithm to measure semantic similarity.We combine semantic similarity information and a random walk based algorithm on the PPI network to predict causal genes.The algorithm is robust, no matter whether different test sets or different parameter values are used. The identification of gene-phenotype relationships is very important for the treatment of human diseases. Studies have shown that genes causing the same or similar phenotypes tend to interact with each other in a protein-protein interaction (PPI) network. Thus, many identification methods based on the PPI network model have achieved good results. However, in the PPI network, some interactions between the proteins encoded by candidate gene and the proteins encoded by known disease genes are very weak. Therefore, some studies have combined the PPI network with other genomic information and reported good predictive performances. However, we believe that the results could be further improved. In this paper, we propose a new method that uses the semantic similarity between the candidate gene and known disease genes to set the initial probability vector of a random walk with a restart algorithm in a human PPI network. The effectiveness of our method was demonstrated by leave-one-out cross-validation, and the experimental results indicated that our method outperformed other methods. Additionally, our method can predict new causative genes of multifactor diseases, including Parkinson's disease, breast cancer and obesity. The top predictions were good and consistent with the findings in the literature, which further illustrates the effectiveness of our method. <s> BIB014 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> To interpret genetic variants discovered from next-generation sequencing, integration of heterogeneous information is vital for success. This article describes a framework named PERCH (Polymorphism Evaluation, Ranking, and Classification for a Heritable trait), available at http://BJFengLab.org/. It can prioritize disease genes by quantitatively unifying a new deleteriousness measure called BayesDel, an improved assessment of the biological relevance of genes to the disease, a modified linkage analysis, a novel rare-variant association test, and a converted variant call quality score. It supports data that contain various combinations of extended pedigrees, trios, and case–controls, and allows for a reduced penetrance, an elevated phenocopy rate, liability classes, and covariates. BayesDel is more accurate than PolyPhen2, SIFT, FATHMM, LRT, Mutation Taster, Mutation Assessor, PhyloP, GERP++, SiPhy, CADD, MetaLR, and MetaSVM. The overall approach is faster and more powerful than the existing quantitative method pVAAST, as shown by the simulations of challenging situations in finding the missing heritability of a complex disease. This framework can also classify variants of unknown significance (variants of uncertain significance) by quantitatively integrating allele frequencies, deleteriousness, association, and co-segregation. PERCH is a versatile tool for gene prioritization in gene discovery research and variant classification in clinical genetic testing. <s> BIB015 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> For clinical cases of Mendelian disease that lack a genetic diagnosis, genome and exome sequencing are increasingly used for seeking the genetic cause. This Review discusses the strategies and computational tools for prioritizing the many genetic variants identified in each genome into those that are most likely to be causal for disease. The authors discuss how diverse types of biochemical, evolutionary, pedigree and clinical-phenotype information are used, and they highlight common pitfalls to be aware of for responsible variant prioritization. <s> BIB016 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <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> BIB017 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> Uncovering modular structure in networks is fundamental for systems in biology, physics, and engineering. Community detection identifies candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a biological laboratory. Only a few communities can typically be validated, and it is thus important to prioritize which communities to select for downstream experimentation. Here we develop CRANK, a mathematically principled approach for prioritizing network communities. CRANK efficiently evaluates robustness and magnitude of structural features of each community and then combines these features into the community prioritization. CRANK can be used with any community detection method. It needs only information provided by the network structure and does not require any additional metadata or labels. However, when available, CRANK can incorporate domain-specific information to further boost performance. Experiments on many large networks show that CRANK effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization. <s> BIB018 </s> A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases <s> Future Directions <s> Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian diseases the challenge is to discover the single etiological variant among thousands of benign or functionally unrelated variants. After calling variants from aligned sequencing reads, variant prioritisation tools are used to examine the conservation or potential functional consequences of variants. We hypothesised that the performance of variant prioritisation tools may vary by disease phenotype. To test this we created benchmark data sets for variants associated with different disease phenotypes. We found that performance of 24 tested tools is highly variable and differs by disease phenotype. The task of identifying a causative variant amongst a large number of benign variants is challenging for all tools, highlighting the need for further development in the field. Based on our observations, we recommend use of five top performers found in this study (FATHMM, M-CAP, MetaLR, MetaSVM and VEST3). In addition we provide tables indicating which analytical approach works best in which disease context. Variant prioritisation tools are best suited to investigate variants associated with well-studied genetic diseases, as these variants are more readily available during algorithm development than variants associated with rare diseases. We anticipate that further development into disease focussed tools will lead to significant improvements. The performance of software tools used to distinguish disease-causing genetic variants depends on the type of disease under investigation. Denise Anderson and Timo Lassmann from the Telethon Kids Institute in Subiaco, Australia, compared 24 software tools commonly used to narrow down DNA variants found in next-generation sequencing data to those likely to cause a particular disease. By looking at more than 4000 disease phenotypes, the researchers found that the different prioritisation tools, owing to their different methodologies and algorithms, varied in their ability to discriminate between pathogenic and benign gene variants. The top-performing tools all used machine-learning techniques and worked best in cases of well-studied genetic diseases. The findings highlight the need for additional disease-focused tool development, and offer a resource to help researchers decide which approach to use in different disease contexts. <s> BIB019
With the advance of high-throughput technologies, in the last two decades the volume of biomedical knowledge, the variety of evidence sources and their completeness have constantly increased. Large comprehensive data portals collecting gene and gene-disease associations for human and other organisms have been created. Incorporation of new or rarely used evidence sources and improvement of data quality are important directions for future work in the field of gene prioritization. Simultaneously with extensive development of evidence sources, great progress in the development of gene prioritization algorithms has been made. Although recent studies have shown that machine learning methods tend to outperform simpler score aggregation methods based on statistical approaches BIB011 , BIB003 , BIB006 , BIB002 , no single method outperforming others exist and different tools seem to be complementary. Therefore, the analysis of the results coming from several tools relying on different principles and data sources remains beneficial. Besides that, there is also an advancement in usability and flexibility of gene prioritization tools. Modern gene prioritization tools allow selection of the algorithm and tuning its parameters, choosing evidence sources and incorporation of user-defined data. They provide a variety of user interfaces and programmatic access options facilitating integration with other computational tools. Despite the active development and great success of gene prioritization tools during past decades, some challenges remain not fully addressed. In many works, gene prioritization is referred as the task of ranking genes by their relevance to the disease BIB007 , BIB009 , BIB010 , BIB013 , or, more specifically, according to the probability to be causal for this disease BIB008 ,  , BIB012 , BIB014 . If for monogenic diseases, one or several validated causal variants exist, complex disorders are associated with some variants, whose causal roles are not always confirmed. Furthermore, in complex disorders, many genes may act together and modify the effect of each other jointly contributing to disease development. Despite this, most of the reviewed tools rank candidate genes separately, what fits Mendelian diseases but for complex disorders may be disadvantageous. Although in some tools BIB017 , BIB004 scores of candidate genes influence each other, nearly none of them explicitly boosts scores of candidates forming a putative pathway or complex. Therefore, it might be reasonable to separate the task of gene prioritization for complex disorders from gene prioritization for monogenic disorders and reformulate it. Instead of ranking all the candidates independently, one can look for subsets of functionally interconnected candidate genes, enriched by associations with a specified disease  , BIB018 . The development of specialized protocols for validation of the results gene prioritization for complex diseases is also necessary. Another aspect of the problem was not addressed by current gene prioritization tools is disease heterogeneity. Disease heterogeneity implies that the disease may be represented by latent subclasses, phenotypically similar but molecularly distinct. The evidence of heterogeneity are shown for many human diseases, complex and monogenic BIB005 , BIB001 . In order to take into account disease heterogeneity, one should perform gene prioritization together with analysis of patient-level experimental data. One example of such data integration is variant prioritization, which identifies likely disease-causing rare point mutations BIB015 , BIB019 , BIB016 .