Question

Can Pre-Existing Microarray Datasets Be Re-Used To Make New Discoveries/ Assumptions?

4

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11.4 years ago

Olivier ▴ 440

Hello all,

Grateful if someone could direct me to tutorials/ case studies where microarray datasets from databases have been re-used/ analysed by third parties for making new assumptions. i.e. I'd like to know if someone can draw new results from the datasets left at repositories like GEO datasets, etc. Is it a common practice?

Hope to get several comments/ case studies (if I'm not mistaken).

Thanks. Sorry if the question seems too basic...

microarray database • 3.3k views

ADD COMMENT • link updated 11.4 years ago by Khader Shameer 18k • written 11.4 years ago by Olivier ▴ 440

score 4 · Answer 1 · 2013-06-21

You might need to narrow your parameters. As described, such studies exist in numbers almost beyond counting. GEO contains nearly 1 million samples profiled with one or more expression platforms (primarily microarrays). And, I would guess that there are more papers using these datasets in the manner you describe than the total number of original studies which caused them to be deposited in GEO in the first place. Even a random browsing of key journals such as Genome Research, Genome Biology, PNAS, NAR, Bioinformatics, PLoS journals, etc over the last ten years will find tons. You could also search google or pubmed for "public gene expression data", "Meta-analysis of Microarray Data", etc. Check out the hundreds of papers which cite Oncomine or the >1000 which cite GEO. Even just browsing through the publication list from the Atul Butte lab (no affiliation) will find you dozens. That is just one of dozens/hundreds of prominent researchers who have made careers of doing what you describe.

Can someone draw new results from the datasets left at repositories like GEO datasets, etc. Is it a common practice? YES! I would argue that this activity has predominated (almost defines) the genomics and bioinformatics community for last 10 years or more. Currently, there is more excitement on the development of next-gen sequence approaches. But, given the larger sample sizes and richer clinical follow-up data, the microarray meta-analysis field is still extremely active.

score 3 · Answer 2 · 2013-06-21

Definitely check out this article: http://www.ncbi.nlm.nih.gov/pubmed/21849665

Sci Transl Med. 2011 Aug 17;3(96):96ra77. doi: 10.1126/scitranslmed.3001318.

Discovery and preclinical validation of drug indications using compendia of public gene expression data.

Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ.

Abstract: The application of established drug compounds to new therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning use high-throughput experimental approaches to assess a compound's potential therapeutic qualities. Here, we present a systematic computational approach to predict novel therapeutic indications on the basis of comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds, yielding predicted therapeutic potentials for these drugs. We recovered many known drug and disease relationships using computationally derived therapeutic potentials and also predict many new indications for these 164 drugs. We experimentally validated a prediction for the antiulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate its efficacy both in vitro and in vivo using mouse xenograft models. This computational method provides a systematic approach for repositioning established drugs to treat a wide range of human diseases.

and this article: http://www.ncbi.nlm.nih.gov/pubmed/21849664

Sci Transl Med. 2011 Aug 17;3(96):96ra76. doi: 10.1126/scitranslmed.3002648.

Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, Morgan AA, Sarwal MM, Pasricha PJ, Butte AJ.

Abstract: Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract for which there are few safe and effective therapeutic options for long-term treatment and disease maintenance. Here, we applied a computational approach to discover new drug therapies for IBD in silico, using publicly available molecular data reporting gene expression in IBD samples and 164 small-molecule drug compounds. Among the top compounds predicted to be therapeutic for IBD by our approach were prednisolone, a corticosteroid used to treat IBD, and topiramate, an anticonvulsant drug not previously described to have efficacy for IBD or any related disorders of inflammation or the gastrointestinal tract. Using a trinitrobenzenesulfonic acid (TNBS)-induced rodent model of IBD, we experimentally validated our topiramate prediction in vivo. Oral administration of topiramate significantly reduced gross pathological signs and microscopic damage in primary affected colon tissue in the TNBS-induced rodent model of IBD. These findings suggest that topiramate might serve as a therapeutic option for IBD in humans and support the use of public molecular data and computational approaches to discover new therapeutic options for disease.

Both papers are from Atul Butte's lab, and if you google his name with "high school" you'll get some very nice write-ups on internship projects he's mentored on mining public microarray data...

score 1 · Answer 3 · 2013-06-21

Yes... Here are few studies where we used publicly available microarray data to study plant stress biology

STIF: Identification of stress-upregulated transcription factor binding sites in Arabidopsis thaliana.
STIFDB-Arabidopsis Stress Responsive Transcription Factor DataBase.
STIFDB2: an updated version of plant stress-responsive transcription factor database with additional stress signals, stress-responsive transcription factor binding sites and stress-responsive genes in Arabidopsis and rice.
Comparative analyses of stress-responsive genes in Arabidopsis thaliana: insight from genomic data mining, functional enrichment, pathway analysis and phenomics.

Data is out there, all you need is... rational, relevant and challenging questions.