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-3126-5p -1.000 2411.000 0.081
hsa-miR-3127-3p -1.000 1766.000 0.196
hsa-miR-3127-5p -1.000 1740.000 0.204
hsa-miR-3128 -1.000 1988.000 0.159
hsa-miR-3129-3p -1.000 2073.000 0.142
hsa-miR-3129-5p 1.000 781.000 0.534
hsa-miR-3130-3p -1.000 2409.000 0.081
hsa-miR-3130-5p -1.000 1649.000 0.232
hsa-miR-3131 -1.000 2026.000 0.150
hsa-miR-3132 -1.000 2631.000 0.038
hsa-miR-3133 -1.000 2590.000 0.053
hsa-miR-3134 -1.000 1499.000 0.271
hsa-miR-3135a -1.000 1820.000 0.188
hsa-miR-3135b -1.000 2131.000 0.131
hsa-miR-3136-3p 1.000 224.000 0.800
hsa-miR-3136-5p -1.000 1935.000 0.169
hsa-miR-3137 -1.000 1390.000 0.294
hsa-miR-3138 -1.000 1552.000 0.254
hsa-miR-3139 -1.000 1141.000 0.388
hsa-miR-3140-3p -1.000 1331.000 0.319
hsa-miR-3140-5p -1.000 1363.000 0.306
hsa-miR-3141 -1.000 2351.000 0.092
hsa-miR-3142 -1.000 2122.000 0.132
hsa-miR-3143 1.000 472.000 0.681
hsa-miR-3144-3p 1.000 274.000 0.775
hsa-miR-3144-5p -1.000 2589.000 0.053
hsa-miR-3145-3p 1.000 667.000 0.595
hsa-miR-3145-5p -1.000 2074.000 0.142
hsa-miR-3146 -1.000 2617.000 0.044
hsa-miR-3147 1.000 821.000 0.513
hsa-miR-3148 -1.000 2159.000 0.125
hsa-miR-3149 1.000 351.000 0.738
hsa-miR-3150a-3p 1.000 381.000 0.718
hsa-miR-3150a-5p -1.000 2362.000 0.088
hsa-miR-3150b-3p 1.000 201.000 0.813
hsa-miR-3150b-5p -1.000 2269.000 0.104
hsa-miR-3151-3p 1.000 710.000 0.568
hsa-miR-3151-5p -1.000 2197.000 0.115
hsa-miR-3152-3p -1.000 2265.000 0.105
hsa-miR-3152-5p 1.000 105.000 0.865
hsa-miR-3153 -1.000 1879.000 0.176
hsa-miR-3154 -1.000 1126.000 0.393
hsa-miR-3155a -1.000 2643.000 0.031
hsa-miR-3155b -1.000 2461.000 0.072
hsa-miR-3156-3p 1.000 819.000 0.517
hsa-miR-3156-5p -1.000 2648.000 0.028
hsa-miR-3157-3p -1.000 2522.000 0.062
hsa-miR-3157-5p -1.000 2185.000 0.118
hsa-miR-3158-3p -1.000 1986.000 0.160
hsa-miR-3158-5p -1.000 1893.000 0.175
hsa-miR-3159 -1.000 1362.000 0.308
hsa-miR-3160-3p -1.000 2548.000 0.060
hsa-miR-3160-5p 1.000 731.000 0.559
hsa-miR-3161 -1.000 2323.000 0.094
hsa-miR-3162-3p -1.000 1409.000 0.289
hsa-miR-3162-5p -1.000 2464.000 0.072
hsa-miR-3163 -1.000 2361.000 0.089
hsa-miR-3164 -1.000 1620.000 0.238
hsa-miR-3165 1.000 728.000 0.560
hsa-miR-3166 -1.000 2373.000 0.086
hsa-miR-3167 -1.000 2090.000 0.139
hsa-miR-3168 1.000 191.000 0.816
hsa-miR-3169 -1.000 909.000 0.474
hsa-miR-3170 -1.000 1117.000 0.398
hsa-miR-3171 -1.000 1862.000 0.182
hsa-miR-3173-3p -1.000 2414.000 0.080
hsa-miR-3173-5p 1.000 129.000 0.853
hsa-miR-3174 1.000 489.000 0.670
hsa-miR-3175 -1.000 2609.000 0.046
hsa-miR-3176 -1.000 1817.000 0.188
hsa-miR-3177-3p -1.000 2237.000 0.109
hsa-miR-3177-5p -1.000 1315.000 0.323
hsa-miR-3178 -1.000 2132.000 0.131
hsa-miR-3179 -1.000 2434.000 0.075
hsa-miR-3180 -1.000 2006.000 0.156
hsa-miR-3180-3p -1.000 1593.000 0.246
hsa-miR-3180-5p -1.000 1282.000 0.338
hsa-miR-3181 -1.000 1207.000 0.365
hsa-miR-3182 1.000 706.000 0.570
hsa-miR-3183 -1.000 2138.000 0.130
hsa-miR-3184-3p -1.000 1954.000 0.166
hsa-miR-3184-5p -1.000 1013.000 0.429
hsa-miR-3185 -1.000 2623.000 0.042
hsa-miR-3186-3p -1.000 1279.000 0.340
hsa-miR-3186-5p 1.000 816.000 0.519
hsa-miR-3187-3p -1.000 1466.000 0.281
hsa-miR-3187-5p -1.000 1723.000 0.211
hsa-miR-3188 1.000 786.000 0.531
hsa-miR-3189-3p -1.000 1306.000 0.331
hsa-miR-3189-5p -1.000 1333.000 0.318
hsa-miR-3190-3p -1.000 2440.000 0.074
hsa-miR-3190-5p -1.000 2029.000 0.149
hsa-miR-3191-3p -1.000 2380.000 0.085
hsa-miR-3191-5p -1.000 962.000 0.448
hsa-miR-3192-3p 1.000 711.000 0.568
hsa-miR-3192-5p 1.000 657.000 0.601
hsa-miR-3193 -1.000 1716.000 0.213
hsa-miR-3194-3p -1.000 1476.000 0.277
hsa-miR-3194-5p -1.000 1680.000 0.224
hsa-miR-3195 -1.000 975.000 0.443
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