Input and runtime details for job 5382027 (precomputed example)

Input

	List of genes analyzed		BMP2 BMP4 BMP7 BMPR1A BMPR2 MYC NOG SMAD1 SMAD2 SMAD3 SMAD4 TGFB1 TGFB2 TGFB3 TGFBR1 TGFBR2 TGFBR3 -NF1 -NF2 -PTEN -TP53 -CHD5 -FOXD3
	Minimum number of genes per cluster		5
	Maximum number of genes per cluster		15
	Minimum score to return		1.1

Job ID 5382027 (server version 4.3.0)

	Job Submitted & Queued		@ Mon Aug 08 10:19:27 CEST 2016
	metaMIR Started		@ Mon Aug 08 10:19:39 CEST 2016
	metaMIR Finished & Post-Processing		@ Mon Aug 08 10:20:31 CEST 2016
	Job Completed		@ Mon Aug 08 10:20:32 CEST 2016

DIRECT ACCESS: http://rna.informatik.uni-freiburg.de/RetrieveResults.jsp?jobID=5382027&toolName=metaMIR ( 30 days expiry )

Description of the job

example3

An example of metaMIR in differential analysis mode. The transforming growth factor beta (TGFb) signalling pathway, important during neural development (including neural crest) is often dysregulated in the development or progression of cancers. In many contexts, up-regulation of the activating components of the pathway can be involved in the progression of disease. A potential therapeutic application of miRNAs in this context would be to search a miRNA that targets (down-regulates) these components. At the same time, given the multitudinous targets of miRNAs, one would not want to target tumour suppressor genes.

In this gene list, the TGFb componens are plain, indicating the search should be "positive" to find miRNAs that will target (down-regulate) these genes as in the standard analysis. Genes prefixed with a minus sign (-) are those tumour suppressors one does *not* want to be simultaneously targeted by any miRNA returned for a cluster in the positive search.

Output

Download an archive file containing the complete results produced by the job in [.zip] format.

Results file content

[1] "input= /scratch/rna/bisge001/RNA_results/CARNA-result//metaMIR_5382027//input_genes 5 15 1.1 /scratch/rna/bisge001/RNA_results/CARNA-result//metaMIR_5382027/" [1] "All entered genes ok." [1] "Partitioning positive genelist" [1] "Partitioning negative genelist" [1] "Analyzing 5 lists from 17 pos genes entered." | | | 0% | |============== | 20% | |============================ | 40% | |========================================== | 60% | |======================================================== | 80% | |======================================================================| 100%[1] "Writing results"

When using metaMIR please cite :

Jason A. Davis, Sita J. Saunders, Martin Mann, Rolf Backofen
Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data
Nucleic Acids Research, 2017, 45 (15), 8745–8757.
Martin Raden, Syed M Ali, Omer S Alkhnbashi, Anke Busch, Fabrizio Costa, Jason A Davis, Florian Eggenhofer, Rick Gelhausen, Jens Georg, Steffen Heyne, Michael Hiller, Kousik Kundu, Robert Kleinkauf, Steffen C Lott, Mostafa M Mohamed, Alexander Mattheis, Milad Miladi, Andreas S Richter, Sebastian Will, Joachim Wolff, Patrick R Wright, and Rolf Backofen
Freiburg RNA tools: a central online resource for RNA-focused research and teaching
Nucleic Acids Research, 46(W1), W25-W29, 2018.

Results are computed with metaMIR version 1.0.0 using R 3.2.1

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