Freiburg RNA Tools
BrainDead - Results
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BrainDead 4526878

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: https://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

Sort by selecting a column name.
RNA id class rank prob(1)
hsa-miR-639 1.000 357.000 0.737
hsa-miR-640 -1.000 2283.000 0.103
hsa-miR-641 -1.000 2619.000 0.043
hsa-miR-642a-3p -1.000 1619.000 0.238
hsa-miR-642a-5p 1.000 652.000 0.605
hsa-miR-642b-3p -1.000 1527.000 0.264
hsa-miR-642b-5p 1.000 726.000 0.560
hsa-miR-643 1.000 419.000 0.705
hsa-miR-644a 1.000 693.000 0.575
hsa-miR-645 -1.000 1246.000 0.351
hsa-miR-646 -1.000 1714.000 0.214
hsa-miR-647 -1.000 961.000 0.448
hsa-miR-648 -1.000 2104.000 0.136
hsa-miR-649 1.000 280.000 0.772
hsa-miR-6499-3p 1.000 127.000 0.855
hsa-miR-6499-5p -1.000 2379.000 0.085
hsa-miR-650 -1.000 1792.000 0.191
hsa-miR-6500-3p 1.000 704.000 0.572
hsa-miR-6500-5p -1.000 919.000 0.469
hsa-miR-6501-3p -1.000 2580.000 0.056
hsa-miR-6501-5p 1.000 204.000 0.811
hsa-miR-6502-3p -1.000 1903.000 0.173
hsa-miR-6502-5p 1.000 358.000 0.736
hsa-miR-6503-3p -1.000 2242.000 0.109
hsa-miR-6503-5p -1.000 1907.000 0.172
hsa-miR-6504-3p -1.000 1053.000 0.419
hsa-miR-6504-5p -1.000 1609.000 0.240
hsa-miR-6505-3p -1.000 1111.000 0.399
hsa-miR-6505-5p -1.000 2526.000 0.062
hsa-miR-6506-3p -1.000 2086.000 0.140
hsa-miR-6506-5p -1.000 1787.000 0.192
hsa-miR-6507-3p 1.000 729.000 0.559
hsa-miR-6507-5p -1.000 1508.000 0.268
hsa-miR-6508-3p -1.000 2529.000 0.061
hsa-miR-6508-5p -1.000 2441.000 0.074
hsa-miR-6509-3p -1.000 869.000 0.500
hsa-miR-6509-5p -1.000 2339.000 0.093
hsa-miR-651-3p -1.000 1870.000 0.179
hsa-miR-651-5p -1.000 1074.000 0.418
hsa-miR-6510-3p 1.000 701.000 0.572
hsa-miR-6510-5p -1.000 2407.000 0.081
hsa-miR-6511a-3p -1.000 1054.000 0.419
hsa-miR-6511a-5p -1.000 2607.000 0.048
hsa-miR-6511b-3p -1.000 1056.000 0.419
hsa-miR-6511b-5p -1.000 2588.000 0.053
hsa-miR-6512-3p -1.000 2121.000 0.132
hsa-miR-6512-5p -1.000 2229.000 0.110
hsa-miR-6513-3p -1.000 1347.000 0.312
hsa-miR-6513-5p 1.000 530.000 0.653
hsa-miR-6514-3p 1.000 354.000 0.737
hsa-miR-6514-5p -1.000 1691.000 0.220
hsa-miR-6515-3p -1.000 1055.000 0.419
hsa-miR-6515-5p -1.000 2151.000 0.127
hsa-miR-6516-3p -1.000 1095.000 0.409
hsa-miR-6516-5p 1.000 435.000 0.699
hsa-miR-652-3p 1.000 491.000 0.669
hsa-miR-652-5p -1.000 1718.000 0.213
hsa-miR-6529-3p 1.000 585.000 0.638
hsa-miR-6529-5p -1.000 2317.000 0.096
hsa-miR-653-3p 1.000 84.000 0.877
hsa-miR-653-5p 1.000 256.000 0.783
hsa-miR-654-3p 1.000 166.000 0.826
hsa-miR-654-5p -1.000 2447.000 0.073
hsa-miR-655-3p 1.000 754.000 0.552
hsa-miR-655-5p -1.000 1936.000 0.169
hsa-miR-656-3p -1.000 1354.000 0.311
hsa-miR-656-5p 1.000 238.000 0.792
hsa-miR-657 1.000 567.000 0.646
hsa-miR-658 -1.000 1121.000 0.395
hsa-miR-659-3p -1.000 842.000 0.500
hsa-miR-659-5p -1.000 1774.000 0.195
hsa-miR-660-3p -1.000 955.000 0.449
hsa-miR-660-5p 1.000 260.000 0.782
hsa-miR-661 -1.000 1532.000 0.261
hsa-miR-662 1.000 111.000 0.861
hsa-miR-663a -1.000 1781.000 0.194
hsa-miR-663b -1.000 1229.000 0.358
hsa-miR-664a-3p 1.000 503.000 0.661
hsa-miR-664a-5p -1.000 2111.000 0.134
hsa-miR-664b-3p 1.000 392.000 0.713
hsa-miR-664b-5p -1.000 1997.000 0.157
hsa-miR-665 -1.000 1118.000 0.398
hsa-miR-668-3p -1.000 1075.000 0.417
hsa-miR-668-5p -1.000 1528.000 0.264
hsa-miR-670-3p 1.000 564.000 0.646
hsa-miR-670-5p -1.000 898.000 0.483
hsa-miR-671-3p -1.000 1165.000 0.380
hsa-miR-671-5p -1.000 2164.000 0.124
hsa-miR-6715a-3p -1.000 1761.000 0.198
hsa-miR-6715b-3p -1.000 1919.000 0.171
hsa-miR-6715b-5p 1.000 190.000 0.817
hsa-miR-6716-3p -1.000 2055.000 0.145
hsa-miR-6716-5p -1.000 2633.000 0.037
hsa-miR-6717-5p -1.000 2585.000 0.054
hsa-miR-6718-5p -1.000 1842.000 0.185
hsa-miR-6719-3p -1.000 1523.000 0.266
hsa-miR-6720-3p -1.000 2640.000 0.033
hsa-miR-6720-5p -1.000 1818.000 0.188
hsa-miR-6721-5p -1.000 1172.000 0.379
hsa-miR-6722-3p -1.000 2249.000 0.107
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

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When using BrainDead please cite :

Results are computed with BrainDead version 1.0.1 using IntaRNA 3.1.5 and Vienna RNA package 2.4.14