Non-redundant compendium of human ncRNA genes in GeneCards.

MOTIVATION: Non-coding RNA (ncRNA) genes are increasingly acknowledged for their importance in the human genome. However, there is no comprehensive non-redundant database for all such human genes. RESULTS: We leveraged the effective platform of GeneCards, the human gene compendium, together with the power of fRNAdb and additional primary sources, to judiciously unify all ncRNA gene entries obtainable from 15 different primary sources. Overlapping entries were clustered to unified locations based on an algorithm employing genomic coordinates. This allowed GeneCards' gamut of relevant entries to rise ∼5-fold, resulting in ∼80,000 human non-redundant ncRNAs, belonging to 14 classes. Such 'grand unification' within a regularly updated data structure will assist future ncRNA research. AVAILABILITY AND IMPLEMENTATION: All of these non-coding RNAs are included among the ∼122,500 entries in GeneCards V3.09, along with pertinent annotation, automatically mined by its built-in pipeline from 100 data sources. This information is available at www.genecards.org. CONTACT: Frida.Belinky@weizmann.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Pain Management - A Decade's Perspective of a New Subspecialty.

OBJECTIVE: Pain management is increasingly recognized as a formal medical subspecialty worldwide. Israel was among the first to offer a board-certified subspecialty, formalized by the Israeli Medical Association in 2010 which is open to all clinicians with a state-recognized specialization. This paper aims at evaluating the current program across several quality control measures. DESIGN: A survey among pain medicine specialists who graduated from the Israeli Pain Management subspecialty. METHODS: All 43 graduates of the program were sent a web-based questionnaire, each related to a different time in the participants' residency period - prior to, during and after training. RESULTS: Forty-one physicians responded to the survey (95% response rate). The most common primary specialty was Anesthesiology (44%), followed by Family Medicine (22%). One-third of the respondents applied to the program over five years after completing their initial residency. Two-thirds reported that they acquired all or most of the professional tools required by a pain specialist. Insufficient training was mentioned regarding addiction management (71%), special population needs (54%) and interventional treatment (37%). A high proportion (82%) responded that the examination contributed to their training and almost all perceived their period of subspecialty as having a positive value in their personal development. Two-thirds of respondents had not yet actively engaged beyond the clinical aspect with other entities responsible for formulating guidelines and other strategic decision-making. CONCLUSION: We hope the findings of this first-of-a-kind survey will encourage other medical authorities to construct formal training in pain medicine and enable this discipline to further evolve.

Viral adaptation to host: a proteome-based analysis of codon usage and amino acid preferences.

Viruses differ markedly in their specificity toward host organisms. Here, we test the level of general sequence adaptation that viruses display toward their hosts. We compiled a representative data set of viruses that infect hosts ranging from bacteria to humans. We consider their respective amino acid and codon usages and compare them among the viruses and their hosts. We show that bacteria-infecting viruses are strongly adapted to their specific hosts, but that they differ from other unrelated bacterial hosts. Viruses that infect humans, but not those that infect other mammals or aves, show a strong resemblance to most mammalian and avian hosts, in terms of both amino acid and codon preferences. In groups of viruses that infect humans or other mammals, the highest observed level of adaptation of viral proteins to host codon usages is for those proteins that appear abundantly in the virion. In contrast, proteins that are known to participate in host-specific recognition do not necessarily adapt to their respective hosts. The implication for the potential of viral infectivity is discussed.

ProTeus: identifying signatures in protein termini.

ProTeus (PROtein TErminUS) is a web-based tool for the identification of short linear signatures in protein termini. It is based on a position-based search method for revealing short signatures in termini of all proteins. The initial step in ProTeus development was to collect all signature groups (SIGs) based on their relative positions at the termini. The initial set of SIGs went through a sequential process of inspection and removal of SIGs, which did not meet the attributed statistical thresholds. The SIGs that were found significant represent protein sets with minimal or no overall sequence similarity besides the similarity found at the termini. These SIGs were archived and are presented at ProTeus. The SIGs are sorted by their strong correspondence to functional annotation from external databases such as GO. ProTeus provides rich search and visualization tools for evaluating the quality of different SIGs. A search option allows the identification of terminal signatures in new sequences. ProTeus (ver 1.2) is available at http://www.proteus.cs.huji.ac.il.

Functional grouping based on signatures in protein termini.

The two ends of each protein are known as the amino (N-) and carboxyl (C-) termini. Short signatures in a protein's termini often carry vital cellular function. No systematic research has been conducted to address the importance of short signatures (3 to 10 amino acids) in protein termini at the proteomic level. Specifically, it is unknown whether such signatures are evolutionarily conserved, and if so, whether this conservation confers shared biological functions. Current signature detection methods fail to detect such short signatures due to inadequate statistical scores. The findings presented in this study strongly support the notion that functional significance of protein sets may be captured by short signatures at their termini. A positional search method was applied to over one million proteins from the UniProt database. The result is a collection of about a thousand significant signature groups (SIGs) that include previously identified as well as many novel signatures in protein termini. These SIGs represent protein sets with minimal or no overall sequence similarity excepting the similarity at their termini. The most significant SIGs are assigned by their strong correspondence to functional annotations derived from external databases such as Gene Ontology. Each of the SIGs is associated with the statistical significance of its functional association. These SIGs provide a valuable source for testing previously overlooked signatures in protein termini and allow for the investigation of the role played by such signatures throughout evolution. The SIGs archive and advanced search options are available at http://www.proteus.cs.huji.ac.il.

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.

MalaCards: an integrated compendium for diseases and their annotation.

Comprehensive disease classification, integration and annotation are crucial for biomedical discovery. At present, disease compilation is incomplete, heterogeneous and often lacking systematic inquiry mechanisms. We introduce MalaCards, an integrated database of human maladies and their annotations, modeled on the architecture and strategy of the GeneCards database of human genes. MalaCards mines and merges 44 data sources to generate a computerized card for each of 16 919 human diseases. Each MalaCard contains disease-specific prioritized annotations, as well as inter-disease connections, empowered by the GeneCards relational database, its searches and GeneDecks set analyses. First, we generate a disease list from 15 ranked sources, using disease-name unification heuristics. Next, we use four schemes to populate MalaCards sections: (i) directly interrogating disease resources, to establish integrated disease names, synonyms, summaries, drugs/therapeutics, clinical features, genetic tests and anatomical context; (ii) searching GeneCards for related publications, and for associated genes with corresponding relevance scores; (iii) analyzing disease-associated gene sets in GeneDecks to yield affiliated pathways, phenotypes, compounds and GO terms, sorted by a composite relevance score and presented with GeneCards links; and (iv) searching within MalaCards itself, e.g. for additional related diseases and anatomical context. The latter forms the basis for the construction of a disease network, based on shared MalaCards annotations, embodying associations based on etiology, clinical features and clinical conditions. This broadly disposed network has a power-law degree distribution, suggesting that this might be an inherent property of such networks. Work in progress includes hierarchical malady classification, ontological mapping and disease set analyses, striving to make MalaCards an even more effective tool for biomedical research. Database URL: http://www.malacards.org/

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.