Personal receptor repertoires: olfaction as a model.

BACKGROUND: Information 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. RESULTS: By 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. CONCLUSIONS: These 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.

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.

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.