MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

Doniger, Scott; Salomonis, Nathan; Dahlquist, Kam D; Vranizan, Karen; Lawlor, Steven C.; Conklin, Bruce R.

doi:10.1186/gb-2003-4-1-r7

Cited by 769 publications

(194 citation statements)

References 16 publications

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“…Several other tools have recently been developed to analyze gene expression by using pathway or ontology information, e.g., (32)(33)(34). Most determine whether a group of differentially expressed genes is enriched for a pathway or ontology term by using overlap statistics such as the cumulative hypergeometric distribution.…”

Section: Discussionmentioning

confidence: 99%

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Subramanian

Tamayo

Mootha

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

42,916

304

38,314

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Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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Section: Discussionmentioning

confidence: 99%

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Subramanian

Tamayo

Mootha

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

42,916

304

38,314

View full text Add to dashboard Cite

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“…GeneOntology.org) with the MAPPFINDER program (20). The raw output was sorted for MAPPFINDER terms that had at least three genes changed with a Z-score Ͼ2 (approximately equivalent to a P Ͻ 0.05).…”

Section: Mappfinder Resultsmentioning

confidence: 99%

Gene expression patterns define key transcriptional events in cell-cycle regulation by cAMP and protein kinase A

Zambon

Zhang

Minovitsky

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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122

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“…The overall trend in altered gene expression levels for caveolin 1 (À2.970.3), Microarray data were also characterized using Gen-MAPP (Gene Microarray Pathway Profiler; Gladstone Institutes), a program designed to view and analyse gene expression data in the context of biological pathways (Dahlquist, 2002;Doniger et al, 2003). The criteria for increased and decreased gene expression levels using this program were set at X1.5 and pÀ1.5-fold change, respectively.…”

Section: Resultsmentioning

confidence: 99%

High-mobility group A1a protein regulates Ras/ERK signaling in MCF-7 human breast cancer cells

et al. 2004

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High-mobility group (HMG) A1 proteins are gene regulatory factors whose overexpression is frequently observed in naturally occurring human cancers. The overexpression of transgenic HMGA1 proteins in cells results in neoplastic transformation and promotes progression to malignant cellular phenotypes. To understand the underlying molecular and biological events involved in these phenomena, we used oligonucleotide microarray analyses to generate an HMGA1a-induced expression profile for approximately 22 000 genes. This gene expression profile was generated using a well-characterized transgenic human MCF-7 mammary adenocarcinoma cell line in which overexpression of transgenic HMGA1 promotes a transition to a more malignant and metastatic phenotype. Microarray expression analyses, together with independent quantitative real-time reverse transcriptase polymerase chain reaction results, indicate that HMGA1a regulates genes involved in the Ras-extracellular signalrelated kinase (Ras/ERK) mitogenic signaling pathway, including KIT ligand and caveolins 1 and 2. We also found that many cholesterol biosynthesis genes were decreased in cells overexpressing HMGA1a. Cholesterol depletion, decreased caveolin, and increased KIT ligand expression, are all independently associated with the activation of Ras/ERK signaling. Upon further analysis, we found that sensitivity to epidermal growth factor activation of ERK phosphorylation was significantly higher, and that cholesterol was significantly depleted, in cells overexpressing HMGA1a. The cumulative evidence indicates that one likely mechanism by which the HMGA1a protein promotes malignant changes in cells is through increased sensitivity to the activation of the Ras/ERK signaling pathway.

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MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

Cited by 769 publications

References 16 publications

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Gene expression patterns define key transcriptional events in cell-cycle regulation by cAMP and protein kinase A

High-mobility group A1a protein regulates Ras/ERK signaling in MCF-7 human breast cancer cells

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