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A one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein-protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method).

POCUS / Prioritization Of Candidate genes Using Statistics

Prioritizes candidate disease genes. POCUS is based on over-representation of functional annotation between loci for the same disease. It is able to suggest counter intuitive candidates. The tool does not require prior knowledge of the etiology of the disease under study to enrich disease genes. It rapidly provides functional annotation for the genes and high enrichment of real disease genes. POCUS produces some candidate-gene shortlists as output.


A network prioritization server dedicated to Drosophila that covers approximately 95% of the coding genome. FlyNet has several distinctive features, including (i) prioritization for both genes and functions; (ii) two complementary network algorithms: direct neighborhood and network diffusion; (iii) spatiotemporal-specific networks as an additional prioritization strategy for traits associated with a specific developmental stage or tissue and (iv) prioritization for human disease genes. FlyNet is expected to serve as a versatile hypothesis-generation platform for genes and functions in the study of basic animal genetics, developmental biology and human disease.


A web-based database and a knowledge extraction engine. Lynx supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.


Provides a gene score method based on the mutation patterns in the exomes of 60 706 unrelated individuals from the Exome Aggregation Consortium (ExAC) dataset. LoFtool uses ratio of loss-of-function (LoF) to synonymous mutations for each gene, adjusting for the gene de novo mutation rate and evolutionary protein conservation. The software intends to predict genome wide de novo haploinsufficient mutations and can be used for investigating complex brain diseases with strong genetic effects.

DAWN / Detecting Association With Networks

Offers a general approach to gene discovery, which can be applied to many complex disorders. DAWN is an algorithm that leverages genetic and gene expression data effectively to predict probable risk genes and subnetworks. This resource is successful in predicting the genes that will accumulate new dnLoF mutations better than any existing methods. It could identify more than 120 genes that plausibly affect risk, and a set of likely autism spectrum disorder (ASD) subnetworks.


Prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 allows user to upload lists of genes and their scores, which can denote tissue-specific expression levels. It facilitates prioritization of genes based on user-specified genome-wide association (GWA) summary statistics. The web server can also be used to rank genes based on their gene products’ propensity. This tool permits user to upload a larger number of phenotype specific data sets.


An online search and discovery engine that attempts to simplify disease-gene identification by automating the typical approaches. Beegle starts by mining the literature to quickly extract a set of genes known to be linked with a given query, then it integrates the learning methodology of Endeavour (a gene prioritization tool) to train a genomic model and rank a set of candidate genes to generate novel hypotheses. In a realistic evaluation setup, Beegle has an average recall of 84% in the top 100 returned genes as a search engine, which improves the discovery engine by 12.6% in the top 5% prioritized genes.


Prioritizes candidate genes and diseases by employing a heterogeneous method consisting of a network of genes/proteins and a phenotypic disease similarity system. HGPEC is a Cytoscape app based on a random walk with restart algorithm on a heterogeneous network of genes and diseases. This app is expected to effectively predict novel disease-gene and disease-disease associations and support network- and rank-based visualization as well as biomedical evidences for such the associations.


Provides broad support for the VCF format, custom annotations, large VCF files, and flexible analysis types. VCF.Filter is an easy-to-use, standalone, graphical software that allows the user to interactively define, run, and save filter chains of any complexity using default and custom variant annotations. It can use and also help generate such cohort-specific tables of allele frequencies. VCF.Filter was developed in close collaboration with medical geneticists working on rare diseases of the immune system.

PERCH / Polymorphism Evaluation Ranking and Classification for Heritable traits

Studies disease genes by quantitatively unifying a new deleteriousness measure called BayesDel, an improved assessment of biological relevance of genes to the disease of interest (BayesGBA), a modified linkage analysis (BayesSeg), a novel rare-variant association score (BayesHLR), and a converted VQSLOD. PERCH supports data which contain a various combination of extended pedigrees, trios and case controls. More, it allows for a reduced penetrance, an elevated phenocopy rate, liability classes and covariates. This tool can be used for the classification of variants of unknow significance (VUS) in clinical genetic testing.

G2D / Genes to Diseases

Relates genes to human inherited diseases that combines the extraction of relations between phenotypes and gene functions in sequence, disease, and literature databases, with sequence similarity searches. G2D allows users to analyse diseases and genetic regions of their interest. It takes advantage of the combination of positional data with gene expression data to guide disease-gene data mining. This tool is a method for the prioritization of genes according to their relation to a disease.

GLAD4U / Gene List Automatically Derived For You

Creates expert candidate gene lists. GLAD4U is a web-based gene retrieval and prioritization tool. It uses NCBI eSearch API to find publications related to a user query and on the gene-to-publication link table to identify genes from the retrieved publications. It also provides additional functionalities such as sending queries towards WebGestalt for analyzing functional enrichment, or provides a direct link to visualize interactions among the protein products of the genes based on the Cytoscape Web utility.


A free web-based phenotype-dependent NGS variant prioritizer, which leverages the wealth of information in GeneCards and its affiliated databases. VarElect employs GeneCards’ powerful search and scoring capacities, and its algorithm affords inferring direct as well as indirect links between sequenced genes and disease/symptom/phenotype keywords. The indirect links benefit from GeneCards’ excellent capacity to relate genes to each other via numerous annotations. VarElect thus provides a robust facility for ranking genes and pointing out their likelihood to be related to a patient’s disease.

Scuba / SCalable UnBAlanced gene prioritization

Performs genome-wide prioritizations. Scuba prioritizes from candidate genes from multiple gene networks given by various data sources. This tool is built on a computational kernel-based method that guides the identification of novel disease genes. It takes multiple heterogeneous data sources to exploits complementary biological knowledge. The default algorithm is scalable to the size of input data, number of kernel transformations used and number of training examples.

GoD / Gene ranking based on Diseases

Enables the prioritization of a list of input genes with respect to a selected disease. GoD is based on HPO ontology and GO ontology for the calculation of ranking. It ranks a given set of genes based on ontology annotations. It is also able to support the ranking of a set of genes or proteins or other molecules such as miRNA on the basis of disease-related annotations. The strength of GoD consists in the use of semantic similarity measures in the prioritization process of genes with respect to a selected disease.

iSyTE / integrated Systems Tool for Eye gene discovery

Permits prioritization of candidate genes associated with human congenital cataract. iSyTE was created using an in silico subtraction approach by which lens microarray data sets are compared to a developmentally matched microarray data set representing the whole embryonic body from which the ocular tissue was removed by microdissection. The tool was used in case of human congenital cataract in which a translocation breakpoint ostensibly responsible for the proband’s phenotype was located within a relatively gene-poor genomic interval.


Improves the stability of any given base ranking method. staRank applied the concept of stability selection to gene rankings to generate more reproducible ordered hit lists for data generated from phenotypic RNAi experiments. Stability ranking is very flexible and can be applied to any gene ranking method. It does not only improve ranking statistics that ignore the gene-wise variance, such as mean or median, but it also improved the reproducibility of a statistic like the rank-sum test. Thus, irrespective of the chosen ranking statistic, it appears beneficial to complement the selection of top scoring genes with stable genes to increase validation rates in secondary screens.


Detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. RobustRankAggreg is a rank aggregation algorithm that is very well suited for such bioinformatic settings. The aggregation is based on the comparison of actual data with a null model that assumes random order of input lists. It can amplify the biological signal if it exists in the input data. The aggregated ranking displays stronger signal than most of the inputs lists.

HyDRA / Hybrid Distance-score Rank Aggregation

Combines the advantages of score-based and combinatorial aggregation techniques. HyDRA was tested on a number of gene sets, including Autism, Breast cancer, Colorectal cancer, Endometriosis, Ischeemic stroke, Leukemia, Lymphoma, and Osteoarthritis. Furthermore, iterative gene discovery was performed for Glioblastoma, Meningioma and Breast cancer, using a sequentially augmented list of training genes related to the Turcot syndrome, Li-Fraumeni condition and other diseases. The methods outperform state-of-the-art software tools such as ToppGene and Endeavour.

HumanNet / Human gene functional interaction Network

A probabilistic functional gene network of 18,714 validated protein-encoding genes of Homo sapiens, constructed by a modified Bayesian integration of 21 types of 'omics' data from multiple organisms, with each data type weighted according to how well it links genes that are known to function together in H. sapiens. Each interaction in HumanNet has an associated log-likelihood score (LLS) that measures the probability of an interaction representing a true functional linkage between two genes.


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A web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein-protein interaction network. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed and downloaded by the user.


A web server that gathers and combines data from a series of databases. All database searches are performed via the web interfaces provided with the original databases, guaranteeing that the most recent data are queried, and obviating data warehousing. GeneSeeker makes the same selection of candidate genes as the human geneticists would have performed, and thus reducing the time-consuming process to a few minutes. GeneSeeker is particularly well suited for syndromes in which the disease gene displays altered expression patterns in the affected tissue(s).

GUILD / Prioritization of Genes Underlying Inheritance Linked Disorders

A framework prioritization of disease candidate genes. GUILD uses a priori gene-disease associations and protein interactions and implementsvarious network-based disease-gene prioritization algorithms such as NetScore, NetZcore, NetShort, FunctionalFlow, Random Walk with Restart and Network Propagation. This tool can also be used as a standalone tool. GUILD is able to significantly highlight disease-gene associations that are not used a priori, and helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.

PGA / Post-GWAS Analysis

Predicts disease causal genes and assigns them evidence-based scores. PGA integrates both gene network and annotation data with genome-wide association studies (GWAS) signals. The software performs: (1) identification of candidate disease genes given a set of GWAS-reported variants and (2) scoring of these candidate genes to prioritize those most likely to be the sources of the disease-association signals. It can predict disease genes both proximal and distal to GWAS signals and regulatory elements that could harbor non-coding causal single nucleotide polymorphisms (SNPs).

IGSP / Integrated Gene Signal Processing

Prioritizes genes implicated by rare variants for disease risk in sequencing-based genome-wide association studies (GWAS). IGSP scores genes by integrating their disease association signals using both gene network as well as phenotype information. The software consists of: (1) gene scoring based on genotype, network, and phenotype information and a model-based score integration, and (2) a Markov chain Monte Carlo (MCMC) algorithm. It was able to uncover putative risk genes of congenital heart disease (CHD) among individuals with 22q11.2 deletion syndrome.

CRIMEtoYHU / Choosing the Right Cancer-Associated Mutation for Evaluation to Yeast HUmanization

Simultaneously finds yeast homologous genes and identifies the correspondent missense variant. CRIMEtoYHU is a multi-tier web application that integrates, processes and annotates data streams from different sources, especially data flows from databases and web services RESTful. It aims to guide users in the selection of somatic mutations associated with human tumors which can be translated in yeast model organism and to assess its functional impact at simpler organism level.

HDR-del / Hamming Distance Ratio

Allows prioritization of variants/regions in exome sequencing for being pathogenic. HDR-del is a statistical approach that detects chromosomal deletions by comparing sequencing datasets between affected individual and control individuals by way of the Hamming distance. The software was applied to prioritize known disease causing chromosomal deletions in four childhood mitochondrial disease patients. It can be applied to whole genome sequencing datasets, as well as exome sequencing datasets.

IFGFA / Identification of Featured Genes from genomic data using Factor Analysis

Allows users to identify featured genes and feature factors related to a disease of interest from gene expression data. IFGFA is a standalone software that mainly focuses on cancer-related gene prediction. It provides a graphical interface based on a latent factor model Bayesian Factor and Regression Model (BFRM) and on a Bayesian statistics model using Markov chain Monte Carlo (MCMC) methods to compute large hierarchical models. It also can be used as a visualization tool for factor analysis.


Ranks genes according to their association with cancer, based on available biomedical literature. OncoScore is a bioinformatics text-mining tool capable of automatically scanning the biomedical literature by means of dynamically updatable web queries and measuring gene-specific cancer association in terms of gene citations. OncoScore performance improved the state-of-the-art on manually curated datasets, therefore it could be useful in all cases where an efficient differentiation between potential drivers and passenger genes is needed.


An easy-to-use web server for the phenotypic characterization of genes. GUILDify offers a prioritization approach based on the protein-protein interaction network where the initial phenotype-gene associations are retrieved via free text search on biological databases. GUILDify web server does not restrict the prioritization to any predefined phenotype, supports multiple species and accepts user-specified genes. It also prioritizes drugs based on the ranking of their targets, unleashing opportunities for repurposing drugs for novel therapies.


A Cytoscape plug-in to help identify putative genes likely to be associated with specific diseases or pathways. In the plug-in, gene prioritization is performed through a random walk with restart algorithm, a state-of-the art network-based method, along with a gene/protein relationship network. The plug-in also allows users efficiently collect biomedical evidence for highly ranked candidate genes. A set of known genes, candidate genes and a gene/protein relationship network can be provided in a flexible way.