Input and runtime details for job 4526878 (precomputed example)
Training data
 Class-annotated RNAs in FASTA | [.fa] |
Model parameters
| k-mers of interest | AA,AGA,AGGU,AGU,AGUU,CU,GAA,GAGG,GG,GGG,GU,GUU,UGA,UGU,UU,UUG,UUGU,UUU | ||
| k-mer features reflect | number of occurrences (feature is number >= 0) | ||
| Max. energy ranked stable | -3 | ||
| Machine learning model | Support Vector Classification using RBF kernel |
Candidate data
| Candidate RNAs in FASTA | [.fa] |
Job ID 4526878 (server version trunk)
 | Job Submitted & Queued | @ Wed Feb 10 15:00:26 CET 2021 | |
| BrainDead Started | @ Wed Feb 10 15:16:53 CET 2021 | |
| BrainDead Finished & Post-Processing | @ Wed Feb 10 15:16:53 CET 2021 | |
| Post-Processing Finished | @ Wed Feb 10 15:17:07 CET 2021 | |
| Job Completed | @ Wed Feb 10 15:17:11 CET 2021 |
DIRECT ACCESS: http://rna.informatik.uni-freiburg.de/RetrieveResults.jsp?jobID=4526878&toolName=BrainDead ( 30 days expiry )
Description of the job
miRNAs as ligands for microglia activation
This example summarizes our study that investigates the ability of mature miRNAs to act as immune receptor ligands. The ability of extracellular miRNAs to directly activate receptors is a recently discovered new field of operation of miRNAs beside their classic role in post-transcriptional gene regulation. The small RNAs available within the example's training data were experimentally tested for their potential to activate murine microglia cells in vitro. They are pre-classified as +/-1 when found activating or non-activating, resp., using fold change analyses based TNF-alpha concentration measurements. The example's training data set comprises both the original training data (top group) as well as the experimentally verified candidate sequences (middle and bottom group) from our initial BrainDead main publication (see list of references within Help page). For details on the selected k-mers, please refer to the manuscript. The candidate set covers all mature human miRNAs from mirBase v22.1.
Output
download complete results
[zip]
Downloads
Predictions for candidate RNAs
Predicted class
Show rows: 1-100
101-200
201-300
301-400
401-500
501-600
601-700
701-800
801-900
901-1000
1001-1100
1101-1200
1201-1300
1301-1400
1401-1500
1501-1600
1601-1700
1701-1800
1801-1900
1901-2000
2001-2100
2101-2200
2201-2300
2301-2400
2401-2500
2501-2600
2601-2656
all
Sort by selecting a column name.
Rows: 1-100
101-200
201-300
301-400
401-500
501-600
601-700
701-800
801-900
901-1000
1001-1100
1101-1200
1201-1300
1301-1400
1401-1500
1501-1600
1601-1700
1701-1800
1801-1900
1901-2000
2001-2100
2101-2200
2201-2300
2301-2400
2401-2500
2501-2600
2601-2656
all
| RNA id | class | rank | prob(1) |
|---|---|---|---|
| hsa-miR-4802-5p | 1.000 | 304.000 | 0.762 |
| hsa-miR-4803 | 1.000 | 805.000 | 0.525 |
| hsa-miR-4804-3p | -1.000 | 1259.000 | 0.346 |
| hsa-miR-4804-5p | 1.000 | 656.000 | 0.601 |
| hsa-miR-483-3p | -1.000 | 1196.000 | 0.369 |
| hsa-miR-483-5p | -1.000 | 2523.000 | 0.062 |
| hsa-miR-484 | -1.000 | 1648.000 | 0.232 |
| hsa-miR-485-3p | -1.000 | 1657.000 | 0.229 |
| hsa-miR-485-5p | -1.000 | 2428.000 | 0.075 |
| hsa-miR-486-3p | -1.000 | 2458.000 | 0.072 |
| hsa-miR-486-5p | 1.000 | 552.000 | 0.648 |
| hsa-miR-487a-3p | -1.000 | 1171.000 | 0.379 |
| hsa-miR-487a-5p | 1.000 | 604.000 | 0.626 |
| hsa-miR-487b-3p | -1.000 | 2356.000 | 0.090 |
| hsa-miR-487b-5p | 1.000 | 605.000 | 0.626 |
| hsa-miR-488-3p | -1.000 | 1016.000 | 0.428 |
| hsa-miR-488-5p | -1.000 | 2358.000 | 0.090 |
| hsa-miR-489-3p | -1.000 | 1372.000 | 0.302 |
| hsa-miR-489-5p | 1.000 | 244.000 | 0.788 |
| hsa-miR-490-3p | -1.000 | 2268.000 | 0.104 |
| hsa-miR-490-5p | -1.000 | 1770.000 | 0.196 |
| hsa-miR-491-3p | -1.000 | 2078.000 | 0.142 |
| hsa-miR-491-5p | -1.000 | 1992.000 | 0.159 |
| hsa-miR-492 | -1.000 | 2569.000 | 0.057 |
| hsa-miR-493-3p | -1.000 | 1539.000 | 0.259 |
| hsa-miR-493-5p | 1.000 | 311.000 | 0.753 |
| hsa-miR-494-3p | -1.000 | 2063.000 | 0.144 |
| hsa-miR-494-5p | 1.000 | 275.000 | 0.775 |
| hsa-miR-495-3p | -1.000 | 2399.000 | 0.083 |
| hsa-miR-495-5p | 1.000 | 218.000 | 0.801 |
| hsa-miR-496 | -1.000 | 2161.000 | 0.124 |
| hsa-miR-497-3p | -1.000 | 974.000 | 0.443 |
| hsa-miR-497-5p | 1.000 | 41.000 | 0.920 |
| hsa-miR-498-3p | -1.000 | 1897.000 | 0.174 |
| hsa-miR-498-5p | -1.000 | 1186.000 | 0.374 |
| hsa-miR-4999-3p | -1.000 | 1635.000 | 0.234 |
| hsa-miR-4999-5p | 1.000 | 220.000 | 0.801 |
| hsa-miR-499a-3p | -1.000 | 1712.000 | 0.215 |
| hsa-miR-499a-5p | 1.000 | 229.000 | 0.797 |
| hsa-miR-499b-3p | -1.000 | 1652.000 | 0.231 |
| hsa-miR-499b-5p | 1.000 | 396.000 | 0.712 |
| hsa-miR-5000-3p | -1.000 | 916.000 | 0.471 |
| hsa-miR-5000-5p | 1.000 | 618.000 | 0.623 |
| hsa-miR-5001-3p | -1.000 | 865.000 | 0.500 |
| hsa-miR-5001-5p | -1.000 | 1573.000 | 0.248 |
| hsa-miR-5002-3p | 1.000 | 537.000 | 0.650 |
| hsa-miR-5002-5p | 1.000 | 382.000 | 0.718 |
| hsa-miR-5003-3p | 1.000 | 524.000 | 0.656 |
| hsa-miR-5003-5p | -1.000 | 2463.000 | 0.072 |
| hsa-miR-5004-3p | -1.000 | 1204.000 | 0.366 |
| hsa-miR-5004-5p | -1.000 | 1697.000 | 0.218 |
| hsa-miR-5006-3p | 1.000 | 430.000 | 0.700 |
| hsa-miR-5006-5p | -1.000 | 1920.000 | 0.171 |
| hsa-miR-5007-3p | -1.000 | 2083.000 | 0.141 |
| hsa-miR-5007-5p | 1.000 | 434.000 | 0.699 |
| hsa-miR-5008-3p | 1.000 | 800.000 | 0.527 |
| hsa-miR-5008-5p | -1.000 | 1170.000 | 0.379 |
| hsa-miR-5009-3p | -1.000 | 2248.000 | 0.107 |
| hsa-miR-5009-5p | -1.000 | 1610.000 | 0.240 |
| hsa-miR-500a-3p | -1.000 | 2492.000 | 0.068 |
| hsa-miR-500a-5p | -1.000 | 1591.000 | 0.246 |
| hsa-miR-500b-3p | -1.000 | 2301.000 | 0.098 |
| hsa-miR-500b-5p | -1.000 | 1377.000 | 0.299 |
| hsa-miR-501-3p | -1.000 | 2493.000 | 0.068 |
| hsa-miR-501-5p | 1.000 | 787.000 | 0.531 |
| hsa-miR-5010-3p | 1.000 | 180.000 | 0.821 |
| hsa-miR-5010-5p | -1.000 | 2433.000 | 0.075 |
| hsa-miR-5011-3p | -1.000 | 1608.000 | 0.240 |
| hsa-miR-5011-5p | -1.000 | 1418.000 | 0.289 |
| hsa-miR-502-3p | -1.000 | 2392.000 | 0.083 |
| hsa-miR-502-5p | -1.000 | 2016.000 | 0.153 |
| hsa-miR-503-3p | 1.000 | 454.000 | 0.687 |
| hsa-miR-503-5p | -1.000 | 1262.000 | 0.345 |
| hsa-miR-504-3p | -1.000 | 1245.000 | 0.351 |
| hsa-miR-504-5p | -1.000 | 1616.000 | 0.238 |
| hsa-miR-5047 | 1.000 | 368.000 | 0.730 |
| hsa-miR-505-3p | 1.000 | 162.000 | 0.832 |
| hsa-miR-505-5p | -1.000 | 1529.000 | 0.264 |
| hsa-miR-506-3p | -1.000 | 1713.000 | 0.215 |
| hsa-miR-506-5p | 1.000 | 784.000 | 0.532 |
| hsa-miR-507 | -1.000 | 907.000 | 0.475 |
| hsa-miR-508-3p | 1.000 | 513.000 | 0.660 |
| hsa-miR-508-5p | -1.000 | 2286.000 | 0.102 |
| hsa-miR-5087 | 1.000 | 678.000 | 0.587 |
| hsa-miR-5088-3p | -1.000 | 900.000 | 0.481 |
| hsa-miR-5088-5p | -1.000 | 1710.000 | 0.215 |
| hsa-miR-5089-3p | -1.000 | 1700.000 | 0.218 |
| hsa-miR-5089-5p | -1.000 | 1605.000 | 0.241 |
| hsa-miR-509-3-5p | -1.000 | 2575.000 | 0.056 |
| hsa-miR-509-3p | -1.000 | 1384.000 | 0.296 |
| hsa-miR-509-5p | -1.000 | 2303.000 | 0.098 |
| hsa-miR-5090 | -1.000 | 1719.000 | 0.213 |
| hsa-miR-5091 | -1.000 | 2346.000 | 0.093 |
| hsa-miR-5092 | -1.000 | 1359.000 | 0.309 |
| hsa-miR-5093 | -1.000 | 2191.000 | 0.117 |
| hsa-miR-5094 | -1.000 | 1289.000 | 0.337 |
| hsa-miR-510-3p | -1.000 | 2320.000 | 0.094 |
| hsa-miR-510-5p | -1.000 | 2243.000 | 0.108 |
| hsa-miR-5100 | -1.000 | 1855.000 | 0.183 |
| hsa-miR-511-3p | -1.000 | 2275.000 | 0.104 |
Job resubmission
Use the following button if you want to resubmit the job with
altered input or parameterization:
usability assessment
When using BrainDead please cite :
- Martin Raden, Thomas Wallach, Milad Miladi, Yuanyuan Zhai, Christina Krueger, Zoe J. Mossmann, Paul Dembny, Rolf Backofen, Seija Lehnardt
Structure-aware machine learning classification of oligonucleotide-induced immune response identifies microRNAs operating as Toll-like receptor 7/8 ligands
RNA Biology, 18:sup1, 268-277,2021. - 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 BrainDead version 1.0.1 using IntaRNA 3.1.5 and Vienna RNA package 2.4.14
About this web server
|
version 5.0.10
|
Copyright © 2012 - 2024 Bioinformatics Group Freiburg
|
|
Imprint and Disclaimer
|
Imprint and Disclaimer