Marilyn Safran

The GeneCaRNA human gene database is a member of the GeneCards Suite. It presents ~280,000 human non-coding RNA genes, identified algorithmically from ~690,000 RNAcentral transcripts. This expands by ~tenfold the ncRNA gene count relative to other sources. GeneCaRNA thus contains ~120,000 long non-coding RNAs (LncRNAs, >200 bases long), including ~100,000 novel genes. The latter have sparse functional information, a vast terra incognita for future research. LncRNA genes are uniformly represented on all nuclear chromosomes, with 10 genes on mitochondrial DNA. Data obtained from MalaCards, another GeneCards Suite member, finds 1547 genes associated with 1 to 50 diseases. About 15% of the associations portray experimental evidence, with cancers tending to be multigenic. Preliminary text mining within GeneCaRNA discovers interactions of lncRNA transcripts with target gene products, with 25% being ncRNAs and 75% proteins. GeneCaRNA has a biological pathways section, which at present shows 131 pathways for 38 lncRNA genes, a basis for future expansion. Finally, our GeneHancer database provides regulatory elements for ~110,000 lncRNA genes, offering pointers for co-regulated genes and genetic linkages from enhancers to diseases. We anticipate that the broad vista provided by GeneCaRNA will serve as an essential guide for further lncRNA research in disease decipherment.

RNA-Seq technology is routinely used to characterize the transcriptome, and to detect gene expression differences among cell types, genotypes and conditions. Advances in short-read sequencing instruments such as Illumina Next-Seq have yielded easy-to-operate machines, with high throughput, at a lower price per base. However, processing this data requires bioinformatics expertise to tailor and execute specific solutions for each type of library preparation. In order to enable fast and user-friendly data analysis, we developed an intuitive and scalable transcriptome pipeline that executes the full process, starting from cDNA sequences derived by RNA-Seq [Nat Rev Genet 10:57-63, 2009] and bulk MARS-Seq [Science 343:776-779, 2014] and ending with sets of differentially expressed genes. Output files are placed in structured folders, and results summaries are provided in rich and comprehensive reports, containing dozens of plots, tables and links. Our User-friendly Transcriptome Analysis Pipeline (UTAP) is an open source, web-based intuitive platform available to the biomedical research community, enabling researchers to efficiently and accurately analyse transcriptome sequence data.

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 utility for NGS interpretation as well as for large scale bioinformatic analyses. VarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness—aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status—high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards’ diverse gene-to-gene relationships, including SuperPaths—integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of “disease SuperPaths”, generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease–disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond protein coding genes. GeneCards is highly useful in this respect, as it also addresses 101,976 non-protein-coding RNA genes. In a more recent development, we are currently adding an inclusive map of regulatory elements and their inferred target genes, generated by integration from 4 resources. MalaCards provides a rich big-data scaffold for in silico biomedical discovery within the gene-disease universe. VarElect, which depends significantly on both GeneCards and MalaCards power, is a potent tool for supporting the interpretation of wet-lab experiments, notably NGS analyses of disease. The GeneCards suite has thus transcended its 2-decade role in biomedical research, maturing into a key player in clinical investigation.

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 down further 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. We describe a novel tool, VarElect ( http://ve.genecards.org ), a comprehensive phenotype-dependent variant/gene prioritizer, based on the widely-used GeneCards, which helps rapidly identify causal mutations with extensive evidence. The GeneCards suite offers an effective and speedy alternative, whereby >120 gene-centric automatically-mined data sources are jointly available for the task. VarElect cashes on this wealth of information, as well as on GeneCards’ powerful free-text Boolean search and scoring capabilities, proficiently matching variant-containing genes to submitted disease/symptom keywords. The tool also leverages the rich disease and pathway information of MalaCards, the human disease database, and PathCards, the unified pathway (SuperPaths) database, both within the GeneCards Suite. The VarElect algorithm infers direct as well as indirect links between genes and phenotypes, the latter benefitting from GeneCards’ diverse gene-to-gene data links in GenesLikeMe. Finally, our tool offers an extensive gene-phenotype evidence portrayal (“MiniCards”) and hyperlinks to the parent databases. We demonstrate that VarElect compares favorably with several often-used NGS phenotyping tools, thus providing a robust facility for ranking genes, pointing out their likelihood to be related to a patient’s disease. VarElect’s capacity to automatically process numerous NGS cases, either in stand-alone format or in VCF-analyzer mode (TGex and VarAnnot), is indispensable for emerging clinical projects that involve thousands of whole exome/genome NGS analyses.

GeneCards (www.genecards.org) is a comprehensive, authoritative compendium of annotative information about human genes, widely used for nearly 15 years. Its gene-centric content is automatically mined and integrated from over 80 digital sources, resulting in a web-based deep-linked card for each of >73 000 human gene entries, encompassing the following categories: protein coding, pseudogene, RNA gene, genetic locus, cluster and uncategorized. We now introduce GeneCards Version 3, featuring a speedy and sophisticated search engine and a revamped, technologically enabling infrastructure, catering to the expanding needs of biomedical researchers. A key focus is on gene-set analyses, which leverage GeneCards’ unique wealth of combinatorial annotations. These include the GeneALaCart batch query facility, which tabulates user-selected annotations for multiple genes and GeneDecks, which identifies similar genes with shared annotations, and finds set-shared annotations by descriptor enrichment analysis. Such set-centric features address a host of applications, including microarray data analysis, cross-database annotation mapping and gene-disorder associations for drug targeting. We highlight the new Version 3 database architecture, its multi-faceted search engine, and its semi-automated quality assurance system. Data enhancements include an expanded visualization of gene expression patterns in normal and cancer tissues, an integrated alternative splicing pattern display, and augmented multi-source SNPs and pathways sections. GeneCards now provides direct links to gene-related research reagents such as antibodies, recombinant proteins, DNA clones and inhibitory RNAs and features gene-related drugs and compounds lists. We also portray the GeneCards Inferred Functionality Score annotation landscape tool for scoring a gene’s functional information status. Finally, we delineate examples of applications and collaborations that have benefited from the GeneCards suite. Database URL:www.genecards.org

Gene annotation is a pivotal component in computational genomics, encompassing prediction of gene function, expression analysis, and sequence scrutiny. Hence, quantitative measures of the annotation landscape constitute a pertinent bioinformatics tool. GeneCards® is a gene-centric compendium of rich annotative information for over 50,000 human gene entries, building upon 68 data sources, including Gene Ontology (GO), pathways, interactions, phenotypes, publications and many more.

Improvements in genome sequence annotation revealed discrepancies in the original probeset/gene assignment in Affymetrix microarray and the existence of differences between annotations and effective alignments of probes and transcription products. In the current generation of Affymetrix human GeneChips, most probesets include probes matching transcripts from more than one gene and probes which do not match any transcribed sequence. We developed a novel set of custom Chip Definition Files (CDF) and the corresponding Bioconductor libraries for Affymetrix human GeneChips, based on the information contained in the GeneAnnot database. GeneAnnot-based CDFs are composed of unique custom-probesets, including only probes matching a single gene. GeneAnnot-based custom CDFs solve the problem of a reliable reconstruction of expression levels and eliminate the existence of more than one probeset per gene, which often leads to discordant expression signals for the same transcript when gene differential expression is the focus of the analysis. GeneAnnot CDFs are freely distributed and fully compliant with Affymetrix standards and all available software for gene expression analysis. The CDF libraries are available from http://www.xlab.unimo.it/GA_CDF , along with supplementary information (CDF libraries, installation guidelines and R code, CDF statistics, and analysis results).

GeneCards^® is an automatically mined database of human genes that strives to create, along with its auxiliary databases—GeneLoc, GeneNote and GeneAnnot—the most inclusive resource of gene-centered information of the human genome. GeneTide, the Gene Terra Incognita Discovery Endeavor (http://genecards.weizmann.ac.il/genetide/), the newest addition to this family, is a transcriptome-focused database which aims to enhance GeneCards with additional expressed sequence tag (EST)-based genes. This is achieved by comprehensively mapping >85% of the ∼5.6 million human ESTs currently available at dbEST to known genes by means of data mining and integration of genomic resources including UniGene, DoTS, AceView and in-house resources. GeneTide thus creates comprehensive links between ESTs and GeneCards genes. Furthermore, groups of unassociated transcripts serve as a basis for defining novel EST-based GeneCards Candidates (EGCs). These EGCs, nearly 25 000 of which were defined in version 0.3 of GeneTide, are further annotated with various parameters, including splicing evidence and expression data extracted from the GeneNote database, to determine their validity as possible de novo genes.

Motivation:Despite the numerous available whole-genome mapping resources, no comprehensive, integrated map of the human genome yet exists. Results: GeneLoc, software adjunct to GeneCards and UDB, integrates gene lists by comparing genomic coordinates at the exon level and assigns unique and meaningful identifiers to each gene. Availability: http://bioinfo.weizmann.ac.il/genecards and http://genecards.weizmann.ac.il/udb Supplementary information: http://bioinfo.weizmann.ac.il/cards-bin/AboutGCids.cgi, http://genecards.weizmann.ac.il/GeneLocAlg.html Contact: marilyn.safran@weizmann.ac.il