HumanBase is a “one stop shop” for biological and biomedical researchers interested in data-driven predictions of gene expression, function, regulation, and interactions in human, particularly in the context of specific cell types/tissues and human disease.
This resource is not merely a public database of primary genomics data or biological literature. The data-driven integrative analyses (i.e. algorithms that “learn” from large genomic data collections) presented in HumanBase are especially powerful because they separate signal from noise in large biological data collections to reach beyond “existing biological knowledge” represented in the biological literature to identify novel associations that are not biased toward well-studied areas of biomedical research. Carefully designed algorithms can drive the development of experimentally testable hypotheses. Thus, HumanBase is a resource for biomedical researchers to incorporate into their research workflows, which they can use to interpret their experimental results and generate hypotheses for experimental follow-up.
In order to leverage the vast collections of raw, noisy genomic data, they must be integrated, summarized, and presented in a biologically informative manner. We provide a means of mining tens of thousands of whole-genome experiments by way of functional interaction networks. Each interaction network represents a body of data, probabilistically weighted and integrated, focused on a particular biological question. These questions can include, for example, the function of a gene, the relationship between two pathways, or the processes disrupted in a genetic disorder. (Huttenhower, et. al 2008)
The precise actions of genes are frequently dependent on their tissue context, and human diseases result from the disordered interplay of tissue- and cell lineage–specific processes. These factors combine to make the understanding of tissue-specific gene functions, disease pathophysiology and gene-disease associations particularly challenging.
HumanBase builds genome-scale functional maps of human tissues by integrating a collection of data sets covering thousands of experiments contained in more than 14,000 distinct publications. We automatically assess each data set for its relevance to each of 144 tissue- and cell lineage–specific functional contexts. The resulting functional maps provide a detailed portrait of protein function and interactions in specific human tissues and cell lineages ranging from B lymphocytes to the renal glomerulus and the whole brain. This approach allows HumanBase to profile the specialized function of genes in a high-throughput manner, even in tissues and cell lineages for which no or few tissue-specific data exist.
Tissue-specific networks provide a new means to generate hypotheses related to the molecular basis of human disease. In NetWAS, the statistical associations from a standard GWAS guide the analysis of functional networks. NetWAS, in conjunction with tissue-specific networks, effectively reprioritizes statistical associations from GWAS to identify disease-associated genes. This reprioritization method is driven by GWAS discovery and does not depend on prior disease knowledge.
Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA, Himmelstein DS, Zhang R, Hartmann BM, Zaslavsky E, Sealfon SC, Chasman DI, FitzGerald GA, Dolinski K, Grosser T, Troyanskaya OG. (2015). Understanding multicellular function and disease with human tissue-specific networks. Nature Genetics. 10.1038/ng.3259w.
Krishnan A*, Zhang R*, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG.(2016) Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nature Neuroscience.