Expanding and Enriching the LncRNA Gene-Disease Landscape Using the GeneCaRNA Database.

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.

The GARD Prebiotic Reproduction Model Described in Order and Complexity.

Early steps in the origin of life were necessarily connected to the unlikely formation of self-reproducing structures from chaotic chemistry. Simulations of chemical kinetics based on the graded autocatalysis replication domain (GARD) model demonstrate the ability of a micellar system to become self-reproducing units away from equilibrium. Even though they may be very rare in the initial state of the system, the property of their endogenous mutually catalytic networks being dynamic attractors greatly enhanced reproduction propensity, revealing their potential for selection and Darwinian evolution processes. In parallel, order and complexity have been shown to be crucial parameters in successful evolution. Here, we probe these parameters in the dynamics of GARD-governed entities in an attempt to identify characteristic mechanisms of their development in non-covalent molecular assemblies. Using a virtual random walk perspective, a value for consecutive order is defined based on statistical thermodynamics. The complexity, on the other hand, is determined by the size of a minimal algorithm fully describing the statistical properties of the random walk. By referring to a previously published diagonal line in an order/complexity diagram that represents the progression of evolution, it is shown that the GARD model has the potential to advance in this direction. These results can serve as a solid foundation for identifying general criteria for future analyses of evolving systems.

The olfactory receptor universe - from whole genome analysis to structure and evolution

Olfactory receptors (ORs) constitute the largest gene-family in the vertebrate genome. We have attempted to provide a comprehensive view of the OR universe through diverse tools of bioinformatics and computational biology. Among others, we have constructed the Human Olfactory Receptor Data Exploratorium (HORDE, http://bioportal.weizmann.ac.il/HORDE/) as a free online resource, which integrates information on ORs from different species. We studied the genomic organization of 853 human ORs and divided the repertoire into 135 clusters, accessible through our new cluster viewer feature. An analysis of intact and pseudogenized ORs in different clusters, as well as of OR expression patterns, is provided, relevant to OR transcription control. Coding single nucleotide polymorphisms were integrated; these are to be used for genotype-phenotype correlation studies. HORDE allows a unique opportunity for discerning protein structural and functional information of the individual OR proteins. By applying novel data analysis strategies to the >3000 OR genes of mouse, dog and human within HORDE, we have generated a set of refined rhodopsin-based homology models for ORs. For model improvement, we employed a novel analysis of specific positions along the seven transmembrane helices at which prolines generate helix-breaking kinks. The model shows family-specif ic structural features, including idiosyncratic kink patterns, which lead to significant differences in the inferred odorant binding site structure. Such analyses form a basis for a comprehensive sequence-based classification of OR proteins in terms of potential odorant binding specificities.