Biomedical research is at a critical juncture in which vast amounts of information on molecules and molecular interactions have been collected, but methods to integrate and analyze these data are still in their infancy. The sheer number and variety of technologies is staggering. In terms of molecules, global mRNA profiles are obtained using DNA microarrays 178 or next-generation sequencing 179, while changes in protein abundance 180, protein phosphorylation state 181 and metabolite concentrations 182 are quantified with mass spectrometry, NMR and other advanced techniques. In terms of molecular interactions, protein-protein binding is measured using yeast-two-hybrid assays 183,184,185,186,187,188,189,190, LUMIER 191, affinity purification coupled to mass spectrometry 192,193,194, or kinase-substrate arrays 195. Protein-DNA and protein-RNA binding are measured with technologies such as chIP-chip 196,197,198, chIP-PET 199,200, DAM-ID 201, double-stranded DNA arrays 202, yeastone- hybrid 203,204, or RIP-chip 205. There has also been an explosion in techniques for mapping genetic networks, including Synthetic Genetic Arrays 206, dSLAM 207, EMAP 208, high-throughput liquid culture assays 209, and combinatorial RNAi 210, which rapidly identify epistatic relationships such as synthetic lethality or suppression in an automated fashion. Large networks are generated by functional genomic studies, involving panels of gene knock-outs 211,212,213,214,215 or analysis of expression Quantitative Trait Loci (eQTL) 216,217,218,219. Alternatively, networks are being defined using functional inter-relationships, such as linking two proteins that are co-expressed or that are given the same protein functional annotation 220. This enormous collection of measurement types necessitates a bioinformatic framework to integrate, filter, and interpret the resulting data. The Mission of the Network Assembly Core is to provide tools for integration and visualization of network level and other genome-scale data, assembly of these data into biological networks in which functional network modules can be identified, and storage and dissemination of data and resulting models. The director, Dr. Ideker is an established leader in the area of bioinformatics dedicated to developing such methods.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center (P50)
Project #
5P50GM085764-05
Application #
8729593
Study Section
Special Emphasis Panel (ZGM1-CBCB-2)
Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
5
Fiscal Year
2014
Total Cost
$129,393
Indirect Cost
$45,914
Name
University of California San Diego
Department
Type
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Muse, Evan D; Yu, Shan; Edillor, Chantle R et al. (2018) Cell-specific discrimination of desmosterol and desmosterol mimetics confers selective regulation of LXR and SREBP in macrophages. Proc Natl Acad Sci U S A 115:E4680-E4689
Bui, Nam; Huang, Justin K; Bojorquez-Gomez, Ana et al. (2018) Disruption of NSD1 in Head and Neck Cancer Promotes Favorable Chemotherapeutic Responses Linked to Hypomethylation. Mol Cancer Ther 17:1585-1594
Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku et al. (2018) Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 6:484-495.e5
Ozturk, Kivilcim; Dow, Michelle; Carlin, Daniel E et al. (2018) The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. J Mol Biol 430:2875-2899
Yan, Jian; Chen, Shi-An A; Local, Andrea et al. (2018) Histone H3 lysine 4 monomethylation modulates long-range chromatin interactions at enhancers. Cell Res 28:204-220
Antonova-Koch, Yevgeniya; Meister, Stephan; Abraham, Matthew et al. (2018) Open-source discovery of chemical leads for next-generation chemoprotective antimalarials. Science 362:
Zarrinpar, Amir; Chaix, Amandine; Xu, Zhenjiang Z et al. (2018) Antibiotic-induced microbiome depletion alters metabolic homeostasis by affecting gut signaling and colonic metabolism. Nat Commun 9:2872
Cowell, Annie N; Istvan, Eva S; Lukens, Amanda K et al. (2018) Mapping the malaria parasite druggable genome by using in vitro evolution and chemogenomics. Science 359:191-199
Hoeksema, Marten A; Glass, Christopher K (2018) Nature and nurture of tissue-specific macrophage phenotypes. Atherosclerosis :
Preissl, Sebastian; Fang, Rongxin; Huang, Hui et al. (2018) Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat Neurosci 21:432-439

Showing the most recent 10 out of 207 publications