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-1272 -1.000 2194.000 0.116
hsa-miR-1273c -1.000 2345.000 0.093
hsa-miR-1273h-3p -1.000 1504.000 0.269
hsa-miR-1273h-5p -1.000 2198.000 0.115
hsa-miR-1275 -1.000 2163.000 0.124
hsa-miR-1276 -1.000 2344.000 0.093
hsa-miR-1277-3p 1.000 850.000 0.500
hsa-miR-1277-5p -1.000 1536.000 0.260
hsa-miR-1278 -1.000 1345.000 0.314
hsa-miR-1279 1.000 389.000 0.713
hsa-miR-128-1-5p -1.000 1021.000 0.425
hsa-miR-128-2-5p -1.000 1687.000 0.222
hsa-miR-128-3p -1.000 2410.000 0.081
hsa-miR-1281 -1.000 1407.000 0.289
hsa-miR-1282 1.000 99.000 0.869
hsa-miR-1283 -1.000 1727.000 0.211
hsa-miR-1284 1.000 647.000 0.607
hsa-miR-1285-3p -1.000 2253.000 0.107
hsa-miR-1285-5p 1.000 195.000 0.814
hsa-miR-1286 -1.000 1825.000 0.187
hsa-miR-1287-3p -1.000 1357.000 0.310
hsa-miR-1287-5p -1.000 1161.000 0.382
hsa-miR-1288-3p -1.000 1500.000 0.270
hsa-miR-1288-5p -1.000 1950.000 0.167
hsa-miR-1289 -1.000 1921.000 0.170
hsa-miR-129-1-3p -1.000 1353.000 0.311
hsa-miR-129-2-3p -1.000 1582.000 0.247
hsa-miR-129-5p -1.000 1475.000 0.278
hsa-miR-1290 -1.000 1568.000 0.249
hsa-miR-1291 -1.000 2397.000 0.083
hsa-miR-1292-3p 1.000 773.000 0.538
hsa-miR-1292-5p -1.000 2052.000 0.146
hsa-miR-1293 -1.000 847.000 0.500
hsa-miR-1294 1.000 65.000 0.892
hsa-miR-1295a -1.000 1827.000 0.187
hsa-miR-1295b-3p -1.000 2128.000 0.131
hsa-miR-1295b-5p -1.000 2495.000 0.067
hsa-miR-1296-3p -1.000 1725.000 0.211
hsa-miR-1296-5p -1.000 1704.000 0.216
hsa-miR-1297 -1.000 1338.000 0.316
hsa-miR-1298-3p -1.000 2064.000 0.144
hsa-miR-1298-5p -1.000 1341.000 0.315
hsa-miR-1299 -1.000 1771.000 0.196
hsa-miR-1301-3p -1.000 1631.000 0.235
hsa-miR-1301-5p -1.000 1887.000 0.175
hsa-miR-1302 -1.000 1633.000 0.234
hsa-miR-1303 -1.000 1222.000 0.360
hsa-miR-1304-3p -1.000 1698.000 0.218
hsa-miR-1304-5p -1.000 915.000 0.471
hsa-miR-1305 -1.000 1575.000 0.248
hsa-miR-1306-3p 1.000 463.000 0.684
hsa-miR-1306-5p -1.000 2183.000 0.118
hsa-miR-1307-3p -1.000 1966.000 0.164
hsa-miR-1307-5p -1.000 1880.000 0.176
hsa-miR-130a-3p -1.000 1100.000 0.404
hsa-miR-130a-5p 1.000 135.000 0.849
hsa-miR-130b-3p -1.000 2511.000 0.065
hsa-miR-130b-5p 1.000 3.000 0.953
hsa-miR-132-3p -1.000 2581.000 0.055
hsa-miR-132-5p 1.000 39.000 0.922
hsa-miR-1321 -1.000 1773.000 0.195
hsa-miR-1322 1.000 709.000 0.568
hsa-miR-1323 -1.000 1101.000 0.404
hsa-miR-1324 -1.000 2046.000 0.147
hsa-miR-133a-3p 1.000 676.000 0.587
hsa-miR-133a-5p -1.000 2348.000 0.092
hsa-miR-133b 1.000 677.000 0.587
hsa-miR-134-3p -1.000 2017.000 0.152
hsa-miR-134-5p 1.000 265.000 0.780
hsa-miR-1343-3p -1.000 2107.000 0.135
hsa-miR-1343-5p -1.000 1830.000 0.187
hsa-miR-135a-2-3p -1.000 2304.000 0.098
hsa-miR-135a-3p -1.000 1977.000 0.162
hsa-miR-135a-5p 1.000 349.000 0.738
hsa-miR-135b-3p -1.000 1783.000 0.193
hsa-miR-135b-5p 1.000 241.000 0.790
hsa-miR-136-3p -1.000 2171.000 0.122
hsa-miR-136-5p 1.000 156.000 0.835
hsa-miR-137-3p -1.000 2457.000 0.072
hsa-miR-137-5p -1.000 1795.000 0.191
hsa-miR-138-1-3p -1.000 1604.000 0.241
hsa-miR-138-2-3p 1.000 198.000 0.814
hsa-miR-138-5p 1.000 262.000 0.780
hsa-miR-139-3p 1.000 362.000 0.733
hsa-miR-139-5p -1.000 1344.000 0.314
hsa-miR-140-3p -1.000 2646.000 0.028
hsa-miR-140-5p 1.000 380.000 0.718
hsa-miR-141-3p -1.000 2200.000 0.115
hsa-miR-141-5p 1.000 205.000 0.810
hsa-miR-142-3p 1.000 215.000 0.804
hsa-miR-142-5p -1.000 2439.000 0.074
hsa-miR-143-3p -1.000 2129.000 0.131
hsa-miR-143-5p -1.000 2174.000 0.120
hsa-miR-144-3p -1.000 1046.000 0.419
hsa-miR-144-5p -1.000 1509.000 0.268
hsa-miR-145-3p -1.000 1144.000 0.387
hsa-miR-145-5p -1.000 888.000 0.488
hsa-miR-1468-3p -1.000 2500.000 0.067
hsa-miR-1468-5p 1.000 116.000 0.860
hsa-miR-1469 -1.000 1750.000 0.200
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