Noam Nativ

GeneCards Version 3: the human gene integrator

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

In-silico human genomics with GeneCards

Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org). This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of tools (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot) for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.

Personal receptor repertoires: olfaction as a model

BackgroundInformation on nucleotide diversity along completely sequenced human genomes has increased tremendously over the last few years. This makes it possible to reassess the diversity status of distinct receptor proteins in different human individuals. To this end, we focused on the complete inventory of human olfactory receptor coding regions as a model for personal receptor repertoires.ResultsBy performing data-mining from public and private sources we scored genetic variations in 413 intact OR loci, for which one or more individuals had an intact open reading frame. Using 1000 Genomes Project haplotypes, we identified a total of 4069 full-length polypeptide variants encoded by these OR loci, average of ~10 per locus, constituting a lower limit for the effective human OR repertoire. Each individual is found to harbor as many as 600 OR allelic variants, ~50% higher than the locus count. Because OR neuronal expression is allelically excluded, this has direct effect on smell perception diversity of the species. We further identified 244 OR segregating pseudogenes (SPGs), loci showing both intact and pseudogene forms in the population, twenty-six of which are annotatively “resurrected” from a pseudogene status in the reference genome. Using a custom SNP microarray we validated 150 SPGs in a cohort of 468 individuals, with every individual genome averaging 36 disrupted sequence variations, 15 in homozygote form. Finally, we generated a multi-source compendium of 63 OR loci harboring deletion Copy Number Variations (CNVs). Our combined data suggest that 271 of the 413 intact OR loci (66%) are affected by nonfunctional SNPs/indels and/or CNVs.ConclusionsThese results portray a case of unusually high genetic diversity, and suggest that individual humans have a highly personalized inventory of functional olfactory receptors, a conclusion that might apply to other receptor multigene families.

PathCards: multi-source consolidation of human biological pathways.

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, thereby consolidating 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/

General olfactory sensitivity database (GOSdb): candidate genes and their genomic variations.

Genetic variations in olfactory receptors likely contribute to the diversity of odorant‐specific sensitivity phenotypes. Our working hypothesis is that genetic variations in auxiliary olfactory genes, including those mediating transduction and sensory neuronal development, may constitute the genetic basis for general olfactory sensitivity (GOS) and congenital general anosmia (CGA). We thus performed a systematic exploration for auxiliary olfactory genes and their documented variation. This included a literature survey, seeking relevant functional in vitro studies, mouse gene knockouts and human disorders with olfactory phenotypes, as well as data mining in published transcriptome and proteome data for genes expressed in olfactory tissues. In addition, we performed next‐generation transcriptome sequencing (RNA‐seq) of human olfactory epithelium and mouse olfactory epithelium and bulb, so as to identify sensory‐enriched transcripts. Employing a global score system based on attributes of the 11 data sources utilized, we identified a list of 1,680 candidate auxiliary olfactory genes, of which 450 are shortlisted as having higher probability of a functional role. For the top‐scoring 136 genes, we identified genomic variants (probably damaging single nucleotide polymorphisms, indels, and copy number deletions) gleaned from public variation repositories. This database of genes and their variants should assist in rationalizing the great interindividual variation in human overall olfactory sensitivity (http://genome.weizmann.ac.il/GOSdb).

MalaCards: A Comprehensive Automatically‐Mined Database of Human Diseases

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 of MalaCards 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. Curr. Protoc. Bioinform. 47:1.24.1‐1.24.19.

HORDE: Comprehensive Resource for Olfactory Receptor Genomics

Olfactory receptors (ORs) constitute the largest gene family in the mammalian genome. The existence of these proteins underlies the nature of, and variability in, odorant perception. The Human Olfactory Receptor Data Explorer (HORDE, http://genome.weizmann.ac.il/horde/ ) is a free online resource, which presents a complete compendium of all OR genes and pseudogenes in the genome of human and four other vertebrates. HORDE includes three parts: (1) an automated pipeline, which mines OR gene and pseudogene sequences out of complete genomes, and generates gene symbols based on sequence similarity; (2) a card generator that obtains and displays annotative information on individual ORs retrieved from external databases and relevant studies; and (3) a search engine that allows user retrieval of OR information. For human ORs, HORDE specifically addresses the universe of interindividual variation, as obtained from several sources, including whole genome sequences made possible by next-generation sequencing. This encompasses single nucleotide polymorphisms (SNP) and copy number variation (CNV), including deleterious mutational events. HORDE also hosts a number of tools designed specifically to assist in the study of OR evolution and function. In this chapter, we describe the status of HORDE (build #43). We also discuss plans for future enhancements and a road map for HORDE to become a better community-based bioinformatics tool. We highlight HORDEs role as a major research tool in the study of an expanding cohort of OR repertoires.

An Overview of Synergistic Data Tools for Biological Scrutiny

A network of biological databases is reviewed, supplying a framework for studies of human genes and the association of their genomic variations with human pheno- types. The network is composed of GeneCards, the human gene compendium, which provides comprehensive informa- tion on all known and predicted human genes, along with its suite members GeneDecks and GeneLoc. Two databases are shown that address genes and variations focusing on ol- factory reception (HORDE) and transduction (GOSdb). In the realm of disease scrutiny, we portray MalaCards, a novel comprehensive database of human diseases and their anno- tations. Also shown is GeneKid, a tool aimed at generating novel kidney disease biomarkers using systems biology, as well as Xome, a database for whole-exome next-generation DNA sequences for human diseases in the Israeli popula- tion. Finally, we show LifeMap Discovery, a database of em- bryonic development, stem cell research and regenerative medicine, which links to both GeneCards and MalaCards.

MalaCards: A Comprehensive Automatically-Mined Database of Human Diseases: MalaCards: Comprehensive Database of Human Diseases

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 of MalaCards 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. Curr. Protoc. Bioinform. 47:1.24.1‐1.24.19.

UNIT 1.24 MalaCards: A Comprehensive Automatically-Mined Database of Human Diseases

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 of MalaCards 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. Curr. Protoc. Bioinform. 47:1.24.1‐1.24.19.