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Input and runtime details for job 5275471 (precomputed example)
Input
 List of genes analyzed | *YAP1
*WWTR1
*TP73
*TEAD1
*TEAD2
*AJUBA
MYC
PAX3
PAX7
DLX5
DLX6
FOXI1
GBX2
HES1
HES5
MSX1
MSX2
SMAD2
SMAD3
SMAD4
TFAP2A
ZIC1
ZIC5 | ||
| Minimum number of genes per cluster | 5 | ||
| Maximum number of genes per cluster | 15 | ||
| Minimum score to return | 1.1 |
Job ID 5275471 (server version 4.3.0)
 | Job Submitted & Queued | @ Mon Aug 08 10:15:17 CEST 2016 | |
| metaMIR Started | @ Mon Aug 08 10:15:24 CEST 2016 | |
| metaMIR Finished & Post-Processing | @ Mon Aug 08 10:16:28 CEST 2016 | |
| Job Completed | @ Mon Aug 08 10:16:28 CEST 2016 |
DIRECT ACCESS: https://rna.informatik.uni-freiburg.de/RetrieveResults.jsp?jobID=5275471&toolName=metaMIR ( 30 days expiry )
Description of the job
example2
An example investigating potential miRNA-mediated cross regulation between
two cellular processes: the Hippo/YAP signaling pathway and transcription
factors involved in neural crest (NC) development. Specifically,
co-regulation here is investigated between those factors promoting
Hippo/YAP transcriptional co-activity and transcription factors involved in
the neural plate border specification during NC development.
The Hippo/YAP genes are marked with asterisks, to ensure that any
combination returned in the analysis will have at least one YAP-related
component subject to simultaneous co-regulation with NC genes. Otherwise,
the results could be overwhelmed by the much longer list of NC genes,
returning combinations that contain no Hippo/YAP elements.
Output
download complete results
[zip]
Result table
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all
Sort by selecting a column name.
Rows: 1-10
11-20
21-30
31-40
41-50
51-60
61-70
71-80
81-90
91-100
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111-120
121-130
131-140
141-150
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351-360
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all
| miRNA | Final Score | Positive Combo | Positive Score | Positive Group | Positive List |
|---|---|---|---|---|---|
| hsa-miR-626 | 1.34 | FOXI1,PAX7,SMAD2,SMAD3,TP73,WWTR1 | 1.336 | 6 | L04 |
| hsa-miR-450b-5p | 1.33 | GBX2,MSX2,SMAD4,TP73,WWTR1,YAP1 | 1.334 | 6 | L01 |
| hsa-miR-588 | 1.33 | AJUBA,GBX2,MSX2,PAX7,TEAD1,TFAP2A,ZIC5 | 1.332 | 7 | L05 |
| hsa-miR-6734-5p | 1.33 | GBX2,SMAD3,TEAD2,TP73,YAP1 | 1.331 | 5 | L08 |
| hsa-miR-2052 | 1.29 | AJUBA,SMAD2,SMAD4,TEAD1,TEAD2,TP73 | 1.33 | 6 | L08 |
| hsa-miR-548p | 1.33 | GBX2,MYC,SMAD2,TP73,WWTR1,YAP1 | 1.329 | 6 | L01 |
| hsa-miR-30a-3p | 1.32 | DLX5,MYC,PAX3,SMAD2,TEAD1,YAP1 | 1.324 | 6 | L02 |
| hsa-miR-3692-3p | 1.31 | AJUBA,GBX2,MSX2,MYC,PAX3,YAP1 | 1.32 | 6 | L01 |
| hsa-miR-30b-5p | 1.32 | PAX3,TEAD1,TFAP2A,WWTR1,ZIC1,ZIC5 | 1.318 | 6 | L05 |
| hsa-miR-345-3p | 1.32 | AJUBA,HES5,PAX7,SMAD4,TP73,ZIC1 | 1.315 | 6 | L06 |
Result table - repPos
Sort by selecting a column name.
Rows: 1-10
11-20
21-23
all
| Gene name | Rep |
|---|---|
| AJUBA | 100 |
| GBX2 | 100 |
| MSX2 | 100 |
| PAX3 | 100 |
| PAX7 | 100 |
| SMAD2 | 100 |
| SMAD3 | 100 |
| SMAD4 | 100 |
| TEAD1 | 100 |
| TFAP2A | 100 |
Result table - clusterPos
Sort by selecting a column name.
Rows: 1-10
11-20
21-30
31-33
all
| Gene 1 | Inter | Gene 2 | Norm |
|---|---|---|---|
| AJUBA | pp | GBX2 | 19 |
| MSX2 | pp | MYC | 15 |
| PAX3 | pp | PAX7 | 23 |
| SMAD2 | pp | SMAD3 | 23 |
| SMAD4 | pp | TEAD1 | 23 |
| TFAP2A | pp | TP73 | 21 |
| WWTR1 | pp | YAP1 | 23 |
| ZIC1 | pp | AJUBA | 21 |
| GBX2 | pp | MSX2 | 20 |
| MYC | pp | PAX3 | 15 |
Job resubmission
usability assessment
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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