A comprehensive functional analysis of tissue specificity of human gene expression

<p>Abstract</p> <p>Background</p> <p>In recent years, the maturation of microarray technology has allowed the genome-wide analysis of gene expression patterns to identify tissue-specific and ubiquitously expressed ('housekeeping') genes. We have performed a functional and topological analysis of housekeeping and tissue-specific networks to identify universally necessary biological processes, and those unique to or characteristic of particular tissues.</p> <p>Results</p> <p>We measured whole genome expression in 31 human tissues, identifying 2374 housekeeping genes expressed in all tissues, and genes uniquely expressed in each tissue. Comprehensive functional analysis showed that the housekeeping set is substantially larger than previously thought, and is enriched with vital processes such as oxidative phosphorylation, ubiquitin-dependent proteolysis, translation and energy metabolism. Network topology of the housekeeping network was characterized by higher connectivity and shorter paths between the proteins than the global network. Ontology enrichment scoring and network topology of tissue-specific genes were consistent with each tissue's function and expression patterns clustered together in accordance with tissue origin. Tissue-specific genes were twice as likely as housekeeping genes to be drug targets, allowing the identification of tissue 'signature networks' that will facilitate the discovery of new therapeutic targets and biomarkers of tissue-targeted diseases.</p> <p>Conclusion</p> <p>A comprehensive functional analysis of housekeeping and tissue-specific genes showed that the biological function of housekeeping and tissue-specific genes was consistent with tissue origin. Network analysis revealed that tissue-specific networks have distinct network properties related to each tissue's function. Tissue 'signature networks' promise to be a rich source of targets and biomarkers for disease treatment and diagnosis.</p

Interactions of pharmaceutical companies with world countries, cancers and rare diseases from Wikipedia network analysis

Using English Wikipedia network of more than 5 million articles we analyze interactions and interlinks between 34 largest pharmaceutical companies, 195 world countries, 47 rare renal diseases and 37 types of cancer. The recently developed algorithm of reduced Google matrix (REGOMAX) allows to take into account direct Markov transitions between these articles but also all indirect ones generated by the pathways between these articles via the global Wikipedia network. Thus this approach provides a compact description of interactions between these articles that allows to determine the friendship networks between articles, the PageRank sensitivity of countries to pharmaceutical companies and rare renal diseases. We also show that the top pharmaceutical companies of Wikipedia PageRank are not those of the top list of market capitalization

AsthmaMap: An interactive knowledge repository for mechanisms of asthma.

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