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Sophisticated genomic navigation strongly benefits from a capacity to establish a similarity metric among genes. GeneDecks is a novel analysis tool that provides such a metric by highlighting shared descriptors between pairs of genes, based on the rich annotation within the GeneCards compendium of human genes. The current implementation addresses information about pathways, protein domains, Gene Ontology (GO) terms, mouse phenotypes, mRNA expression patterns, disorders, drug relationships, anddoi:10.1089/omi.2009.0069 pmid:20001862 fatcat:nmldxe24mzbylfspjqt3ozvpxu
more »... equence-based paralogy. Gene-Decks has two modes: (1) Paralog Hunter, which seeks functional paralogs based on combinatorial similarity of attributes; and (2) Set Distiller, which ranks descriptors by their degree of sharing within a given gene set. GeneDecks enables the elucidation of unsuspected putative functional paralogs, and a refined scrutiny of various gene-sets (e.g., from high-throughput experiments) for discovering relevant biological patterns.
Systems medicine provides insights into mechanisms of human diseases, and expedites the development of better diagnostics and drugs. To facilitate such strategies, we initiated MalaCards, a compendium of human diseases and their annotations, integrating and often remodeling information from 64 data sources. MalaCards employs, among others, the proven automatic data-mining strategies established in the construction of GeneCards, our widely used compendium of human genes. The development ofdoi:10.1002/0471250953.bi0124s47 pmid:25199789 fatcat:vowmssjxt5c5pnc56rviyj4ot4
more »... rds poses many algorithmic challenges, such as disease name unification, integrated classification, gene-disease association, and disease-targeted expression analysis. MalaCards displays a Web card for each of >19,000 human diseases, with 17 sections, including textual summaries, related diseases, related genes, genetic variations and tests, and relevant publications. Also included are a powerful search engine and a variety of categorized disease lists. This unit describes two basic protocols to search and browse MalaCards effectively.
The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, therebydoi:10.1093/database/bav006 pmid:25725062 pmcid:PMC4343183 fatcat:mf37aem2avhe7nhcqrcdgk32se
more »... ting 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths' capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource. Database URL: http://pathcards.genecards.org/ Database, 2015, 1-13
GeneCards is a one-stop shop for searchable human gene annotationsdoi:10.1093/database/baw030 pmid:27048349 pmcid:PMC4820835 fatcat:o2qmsm6l2ve7bpyvhbj2pnu6re
Additional file 2. TGex report for the trichohepatoenteric syndrome Demo exampledoi:10.6084/m9.figshare.11480619 fatcat:3hgdptj3rbgxldp5sb354ma7m4
BMC Medical Genomics
The clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient's phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants. We present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform,doi:10.1186/s12920-019-0647-8 pmid:31888639 pmcid:PMC6937949 fatcat:ixi7j3mqqzflrgzaj5wwwb3xjm
more »... remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex's main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex's broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards' recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters. Examining use-cases from a variety of TGex users world-wide, we demonstrate its high diagnostic yields (42% for single exome and 50% for trios in 1500 rare genetic disease cases) and critical actionable genetic findings. The platform's support for integration with EHR and LIMS through dedicated APIs facilitates automated retrieval of patient data for TGex's customizable reporting engine, establishing a rapid and cost-effective workflow for an entire range of clinical genetic testing, including rare disorders, cancer predisposition, tumor biopsies and health screening. TGex is an innovative tool for the annotation, analysis and prioritization of coding and non-coding genomic variants. It provides access to an extensive knowledgebase of genomic annotations, with intuitive and flexible configuration options, allows quick adaptation, and addresses various workflow requirements. It thus simplifies and accelerates variant interpretation in clinical genetics workflows, with remarkable diagnostic yield, as exemplified in the described use cases. TGex is available at http://tgex.genecards.org/.
Citation details: Rappaport,N., Nativ,N., Stelzer,G., et al. MalaCards: an integrated compendium for diseases and their annotation.doi:10.1093/database/bat018 pmid:23584832 pmcid:PMC3625956 fatcat:ju462m3cezgqtkai2jjrdvatuq
., 2006; Stein et al., 2005) ; miRNAs-micro RNAs, $20 nt long mediators of transcript silencing (Bushati and Cohen, 2007) ; tRNA-adaptors between codons and the coded amino acid (Kubli, 1981) ; rRNA-RNA ...doi:10.1093/bioinformatics/bts676 pmid:23172862 fatcat:eva7zsn2bbg3zfevzxohslvdxu
A key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient's disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utilitydoi:10.1186/s12938-017-0359-2 pmid:28830434 pmcid:PMC5568599 fatcat:nr5iblpqh5bn5glqnrgelrdehm
more »... for NGS interpretation as well as for large scale bioinformatic analyses.
The MalaCards human disease database (http://www. malacards.org/) is an integrated compendium of annotated diseases mined from 68 data sources. MalaCards has a web card for each of ∼20 000 disease entries, in six global categories. It portrays a broad array of annotation topics in 15 sections, including Summaries, Symptoms, Anatomical Context, Drugs, Genetic Tests, Variations and Publications. The Aliases and Classifications section reflects an algorithm for disease name integration acrossdoi:10.1093/nar/gkw1012 pmid:27899610 pmcid:PMC5210521 fatcat:gc65doqdurcxrdfs4fbqhanxbi
more »... -conflicting sources, providing effective annotation consolidation. A central feature is a balanced Genes section, with scores reflecting the strength of disease-gene associations. This is accompanied by other gene-related disease information such as pathways, mouse phenotypes and GO-terms, stemming from MalaCards' affiliation with the GeneCards Suite of databases. MalaCards' capacity to inter-link information from complementary sources, along with its elaborate search function, relational database infrastructure and convenient data dumps, allows it to tackle its rich disease annotation landscape, and facilitates systems analyses and genome sequence interpretation. MalaCards adopts a 'flat' disease-card approach, but each card is mapped to popular hierarchical ontologies (e.g. International Classification of Diseases, Human Phenotype Ontology and Unified Medical Language System) and also contains information about multi-level relations among diseases, thereby providing an optimal tool for disease representation and scrutiny.
These authors contributed equally to this work. Citation details: Fishilevich,S., Nudel,R., Rappaport,N. et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Abstract A major challenge in understanding gene regulation is the unequivocal identification of enhancer elements and uncovering their connections to genes. We present GeneHancer, a novel database of human enhancers and their inferred target genes, in the framework of GeneCards. First, we integrated adoi:10.1093/database/bax028 pmid:28605766 pmcid:PMC5467550 fatcat:xslg453wargdbalhgq7rmj73pq
more »... l of 434 000 reported enhancers from four different genome-wide databases: the Encyclopedia of DNA Elements (ENCODE), the Ensembl regulatory build, the functional annotation of the mammalian genome (FANTOM) project and the VISTA Enhancer Browser. Employing an integration algorithm that aims to remove redundancy, GeneHancer portrays 285 000 integrated candidate enhancers (covering 12.4% of the genome), 94 000 of which are derived from more than one source, and each assigned an annotation-derived confidence score. GeneHancer subsequently links enhancers to genes, using: tissue co-expression correlation between genes and enhancer RNAs, as well as enhancer-targeted transcription factor genes; expression quantitative trait loci for variants within enhancers; and capture Hi-C, a promoter-specific genome conformation assay. The individual scores based on each of these four methods, along with gene-enhancer genomic distances, form the basis for GeneHancer's combinatorial likelihood-based scores for enhancer-gene pairing. Finally, we define 'elite' enhancergene relations reflecting both a high-likelihood enhancer definition and a strong enhancer-gene association. GeneHancer predictions are fully integrated in the widely used GeneCards Suite, whereby candidate enhancers and their annotations are displayed on every relevant GeneCard. This assists in the mapping of non-coding variants to enhancers, and via the linked genes, forms a basis for variant-phenotype interpretation of whole-genome sequences in health and disease.
Next generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing downdoi:10.1186/s12864-016-2722-2 pmid:27357693 pmcid:PMC4928145 fatcat:zbmio3et65h23phhmmgyve46z4
more »... rther towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates.
ACKNOWLEDGEMENTS The authors wish to thank Ron Ophir, Itai Yanai, Tsippi Iny Stein and the reviewer for their important suggestions and help. ...doi:10.1093/nar/gki122 pmid:15608261 pmcid:PMC540076 fatcat:nsxin72ru5efxhn3u325tojx7a
Marilyn Safran, Irina Dalah, Justin Alexander, Naomi Rosen, Tsippi Iny Stein, Michael Shmoish, Noam Nativ, Iris Bahir, Tirza Doniger, Hagit Krug, et al. ... Cell systems, Safran, Irina Dalah, Justin Alexander, Naomi Rosen, Tsippi Iny Stein, Michael Shmoish, Noam Nativ, Iris Bahir, Tirza Doniger, Hagit Krug, et al. ...doi:10.1101/2021.06.10.437122 fatcat:j3e45xnuz5a3tdrud6rpaogzge
Acknowledgements We thank Prof Yoram Groner whose inquiry seeded the idea of GIFtS, Naomi Rosen (who incorporated GIFtS into the display of each GeneCard), Tsippi Iny Stein (who incorporated GIFtS into ...doi:10.1186/1471-2105-10-348 pmid:19852797 pmcid:PMC2774327 fatcat:e7jfqzhntrfpnongo5zgulj72m