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: 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

Sort by selecting a column name.
RNA id class rank prob(1)
hsa-miR-3657 -1.000 857.000 0.500
hsa-miR-3658 -1.000 2406.000 0.082
hsa-miR-3659 1.000 278.000 0.773
hsa-miR-365a-3p -1.000 1585.000 0.247
hsa-miR-365a-5p -1.000 1689.000 0.221
hsa-miR-365b-3p -1.000 1586.000 0.247
hsa-miR-365b-5p 1.000 660.000 0.599
hsa-miR-3660 1.000 822.000 0.513
hsa-miR-3661 -1.000 1366.000 0.305
hsa-miR-3662 -1.000 2538.000 0.060
hsa-miR-3663-3p -1.000 1485.000 0.274
hsa-miR-3663-5p -1.000 2031.000 0.149
hsa-miR-3664-3p 1.000 740.000 0.554
hsa-miR-3664-5p 1.000 830.000 0.509
hsa-miR-3665 -1.000 1760.000 0.198
hsa-miR-3666 -1.000 1894.000 0.175
hsa-miR-3667-3p 1.000 707.000 0.570
hsa-miR-3667-5p -1.000 1918.000 0.171
hsa-miR-3668 -1.000 904.000 0.478
hsa-miR-367-3p 1.000 698.000 0.574
hsa-miR-367-5p 1.000 526.000 0.655
hsa-miR-3670 -1.000 1275.000 0.341
hsa-miR-3671 -1.000 2582.000 0.055
hsa-miR-3672 1.000 820.000 0.514
hsa-miR-3674 -1.000 996.000 0.434
hsa-miR-3675-3p -1.000 2233.000 0.109
hsa-miR-3675-5p -1.000 1250.000 0.350
hsa-miR-3677-3p -1.000 1860.000 0.182
hsa-miR-3677-5p -1.000 2012.000 0.153
hsa-miR-3678-3p 1.000 74.000 0.885
hsa-miR-3678-5p -1.000 1489.000 0.273
hsa-miR-3679-3p -1.000 1355.000 0.311
hsa-miR-3679-5p -1.000 2266.000 0.105
hsa-miR-3680-3p -1.000 1303.000 0.332
hsa-miR-3680-5p 1.000 327.000 0.748
hsa-miR-3681-3p -1.000 1209.000 0.365
hsa-miR-3681-5p 1.000 779.000 0.535
hsa-miR-3682-3p -1.000 988.000 0.436
hsa-miR-3682-5p 1.000 353.000 0.737
hsa-miR-3683 -1.000 2369.000 0.088
hsa-miR-3684 -1.000 1351.000 0.311
hsa-miR-3685 -1.000 1479.000 0.276
hsa-miR-3686 -1.000 1869.000 0.179
hsa-miR-3688-3p -1.000 2140.000 0.129
hsa-miR-3688-5p -1.000 2168.000 0.122
hsa-miR-3689a-3p 1.000 578.000 0.642
hsa-miR-3689a-5p 1.000 186.000 0.819
hsa-miR-3689b-3p 1.000 422.000 0.704
hsa-miR-3689b-5p 1.000 187.000 0.819
hsa-miR-3689c 1.000 423.000 0.704
hsa-miR-3689d 1.000 763.000 0.547
hsa-miR-3689e 1.000 188.000 0.819
hsa-miR-3689f 1.000 757.000 0.549
hsa-miR-369-3p 1.000 251.000 0.786
hsa-miR-369-5p 1.000 467.000 0.683
hsa-miR-3690 -1.000 2545.000 0.060
hsa-miR-3691-3p -1.000 1955.000 0.166
hsa-miR-3691-5p -1.000 1821.000 0.188
hsa-miR-3692-3p -1.000 1335.000 0.317
hsa-miR-3692-5p -1.000 1778.000 0.194
hsa-miR-370-3p -1.000 2454.000 0.073
hsa-miR-370-5p 1.000 343.000 0.740
hsa-miR-3713 1.000 722.000 0.565
hsa-miR-3714 -1.000 1998.000 0.157
hsa-miR-371a-3p 1.000 318.000 0.752
hsa-miR-371a-5p -1.000 1278.000 0.341
hsa-miR-371b-3p 1.000 93.000 0.871
hsa-miR-371b-5p -1.000 1453.000 0.283
hsa-miR-372-3p -1.000 1221.000 0.360
hsa-miR-372-5p -1.000 954.000 0.450
hsa-miR-373-3p -1.000 1216.000 0.363
hsa-miR-373-5p -1.000 1438.000 0.287
hsa-miR-374a-3p 1.000 492.000 0.667
hsa-miR-374a-5p -1.000 1155.000 0.384
hsa-miR-374b-3p 1.000 47.000 0.904
hsa-miR-374b-5p -1.000 2172.000 0.121
hsa-miR-374c-3p 1.000 63.000 0.893
hsa-miR-374c-5p -1.000 1956.000 0.166
hsa-miR-375-3p 1.000 26.000 0.930
hsa-miR-375-5p -1.000 2288.000 0.101
hsa-miR-376a-2-5p -1.000 1239.000 0.354
hsa-miR-376a-3p -1.000 2627.000 0.039
hsa-miR-376a-5p -1.000 1122.000 0.394
hsa-miR-376b-3p -1.000 1942.000 0.167
hsa-miR-376b-5p 1.000 136.000 0.849
hsa-miR-376c-3p -1.000 2561.000 0.058
hsa-miR-376c-5p 1.000 603.000 0.626
hsa-miR-377-3p 1.000 681.000 0.584
hsa-miR-377-5p 1.000 444.000 0.693
hsa-miR-378a-3p -1.000 2541.000 0.060
hsa-miR-378a-5p 1.000 331.000 0.746
hsa-miR-378b -1.000 2274.000 0.104
hsa-miR-378c -1.000 2543.000 0.060
hsa-miR-378d -1.000 2542.000 0.060
hsa-miR-378e -1.000 1703.000 0.217
hsa-miR-378f -1.000 2188.000 0.117
hsa-miR-378g -1.000 2624.000 0.042
hsa-miR-378h -1.000 1175.000 0.378
hsa-miR-378i -1.000 2645.000 0.030
hsa-miR-378j -1.000 1693.000 0.220
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