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Institution

Genomics Institute of the Novartis Research Foundation

FacilitySan Diego, California, United States
About: Genomics Institute of the Novartis Research Foundation is a facility organization based out in San Diego, California, United States. It is known for research contribution in the topics: Thermotoga maritima & Gene. The organization has 1106 authors who have published 1126 publications receiving 122552 citations. The organization is also known as: Novartis Institute for Functional Genomics.


Papers
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Journal ArticleDOI
29 Mar 2012-Nature
TL;DR: The results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents and the generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens.
Abstract: The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

6,417 citations

Journal ArticleDOI
TL;DR: A biologist-oriented portal that provides a gene list annotation, enrichment and interactome resource and enables integrated analysis of multi-OMICs datasets, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
Abstract: A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era

6,282 citations

Journal ArticleDOI
TL;DR: In this paper, high-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale, and the authors have designed custom arrays that interrogate the expression of the vast majority of proteinencoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues.
Abstract: The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.

3,513 citations

Journal ArticleDOI
21 Mar 2003-Cell
TL;DR: The characterization of ANKTM1 is described, a cold-activated channel with a lower activation temperature compared to the cold and menthol receptor, TRPM8, which is found in a subset of nociceptive sensory neurons where it is coexpressed with TRPV1/VR1 (the capsaicin/heat receptor) but not TRPM 8.

2,290 citations

Journal ArticleDOI
03 May 2002-Cell
TL;DR: Genetic and genomic analysis suggests that a relatively small number of output genes are directly regulated by core oscillator components, and major processes regulated by the SCN and liver were found to be under circadian regulation.

2,227 citations


Authors

Showing all 1107 results

NameH-indexPapersCitations
Peter G. Schultz15689389716
Chi-Huey Wong129122066349
David A. Brenner12849952756
Nathanael S. Gray11972059748
Carolyn R. Bertozzi11869161006
Steve A. Kay11128447155
J. Andrew McCammon10666955698
Jonathan A. Ellman9947836218
David Lloyd90101737691
David C.S. Huang9026837506
Ruben Abagyan8537731620
Gary Siuzdak8128031641
John B. Hogenesch8022636488
Sebastian Doniach7821719797
Lars Eckmann7820026288
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
202122
202035
201935
201844
201731