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-2278 -1.000 1019.000 0.427
hsa-miR-2355-3p 1.000 79.000 0.881
hsa-miR-2355-5p -1.000 2383.000 0.085
hsa-miR-2392 -1.000 2043.000 0.147
hsa-miR-23a-3p -1.000 1567.000 0.249
hsa-miR-23a-5p -1.000 965.000 0.448
hsa-miR-23b-3p -1.000 2013.000 0.153
hsa-miR-23b-5p 1.000 443.000 0.694
hsa-miR-23c 1.000 798.000 0.528
hsa-miR-24-1-5p -1.000 1009.000 0.430
hsa-miR-24-2-5p -1.000 2082.000 0.141
hsa-miR-24-3p -1.000 1548.000 0.255
hsa-miR-2467-3p -1.000 2142.000 0.129
hsa-miR-2467-5p 1.000 287.000 0.769
hsa-miR-25-3p 1.000 243.000 0.789
hsa-miR-25-5p -1.000 2241.000 0.109
hsa-miR-2681-3p 1.000 234.000 0.795
hsa-miR-2681-5p 1.000 650.000 0.606
hsa-miR-2682-3p -1.000 936.000 0.460
hsa-miR-2682-5p 1.000 309.000 0.756
hsa-miR-26a-1-3p 1.000 473.000 0.680
hsa-miR-26a-2-3p 1.000 82.000 0.878
hsa-miR-26a-5p -1.000 2537.000 0.060
hsa-miR-26b-3p 1.000 532.000 0.653
hsa-miR-26b-5p -1.000 1337.000 0.316
hsa-miR-27a-3p 1.000 239.000 0.791
hsa-miR-27a-5p 1.000 659.000 0.600
hsa-miR-27b-3p -1.000 973.000 0.443
hsa-miR-27b-5p -1.000 2250.000 0.107
hsa-miR-28-3p 1.000 437.000 0.698
hsa-miR-28-5p -1.000 1653.000 0.231
hsa-miR-2861 -1.000 1991.000 0.159
hsa-miR-2909 1.000 801.000 0.526
hsa-miR-296-3p -1.000 1027.000 0.421
hsa-miR-296-5p -1.000 1383.000 0.296
hsa-miR-297 -1.000 1380.000 0.296
hsa-miR-298 -1.000 1169.000 0.379
hsa-miR-299-3p -1.000 1672.000 0.226
hsa-miR-299-5p 1.000 319.000 0.752
hsa-miR-29a-3p -1.000 1444.000 0.284
hsa-miR-29a-5p 1.000 69.000 0.888
hsa-miR-29b-1-5p 1.000 649.000 0.606
hsa-miR-29b-2-5p 1.000 397.000 0.712
hsa-miR-29b-3p 1.000 386.000 0.715
hsa-miR-29c-3p 1.000 742.000 0.554
hsa-miR-29c-5p 1.000 165.000 0.829
hsa-miR-300 -1.000 2558.000 0.059
hsa-miR-301a-3p -1.000 1131.000 0.391
hsa-miR-301a-5p 1.000 247.000 0.787
hsa-miR-301b-3p 1.000 495.000 0.665
hsa-miR-301b-5p 1.000 153.000 0.838
hsa-miR-302a-3p 1.000 370.000 0.729
hsa-miR-302a-5p -1.000 1151.000 0.385
hsa-miR-302b-3p 1.000 184.000 0.821
hsa-miR-302b-5p -1.000 2448.000 0.073
hsa-miR-302c-3p 1.000 475.000 0.679
hsa-miR-302c-5p -1.000 2225.000 0.111
hsa-miR-302d-3p 1.000 54.000 0.899
hsa-miR-302d-5p 1.000 646.000 0.607
hsa-miR-302e -1.000 1634.000 0.234
hsa-miR-302f 1.000 125.000 0.857
hsa-miR-3059-3p -1.000 1576.000 0.248
hsa-miR-3059-5p 1.000 237.000 0.792
hsa-miR-3064-3p -1.000 982.000 0.437
hsa-miR-3064-5p 1.000 252.000 0.785
hsa-miR-3065-3p 1.000 112.000 0.861
hsa-miR-3065-5p -1.000 1744.000 0.202
hsa-miR-3074-3p -1.000 2298.000 0.098
hsa-miR-3074-5p -1.000 941.000 0.457
hsa-miR-3085-3p -1.000 1287.000 0.337
hsa-miR-3085-5p -1.000 1145.000 0.387
hsa-miR-30a-3p 1.000 8.000 0.945
hsa-miR-30a-5p -1.000 2092.000 0.138
hsa-miR-30b-3p 1.000 36.000 0.926
hsa-miR-30b-5p -1.000 1488.000 0.273
hsa-miR-30c-1-3p -1.000 927.000 0.464
hsa-miR-30c-2-3p -1.000 882.000 0.490
hsa-miR-30c-5p -1.000 1487.000 0.273
hsa-miR-30d-3p 1.000 157.000 0.835
hsa-miR-30d-5p -1.000 2093.000 0.138
hsa-miR-30e-3p 1.000 23.000 0.930
hsa-miR-30e-5p -1.000 1029.000 0.421
hsa-miR-31-3p -1.000 981.000 0.437
hsa-miR-31-5p -1.000 2494.000 0.067
hsa-miR-3115 1.000 623.000 0.619
hsa-miR-3116 -1.000 2264.000 0.105
hsa-miR-3117-3p -1.000 2297.000 0.098
hsa-miR-3117-5p -1.000 2307.000 0.097
hsa-miR-3118 -1.000 967.000 0.447
hsa-miR-3119 1.000 614.000 0.624
hsa-miR-3120-3p -1.000 1931.000 0.170
hsa-miR-3120-5p -1.000 1031.000 0.420
hsa-miR-3121-3p -1.000 2450.000 0.073
hsa-miR-3121-5p 1.000 547.000 0.648
hsa-miR-3122 -1.000 1502.000 0.269
hsa-miR-3123 1.000 372.000 0.728
hsa-miR-3124-3p -1.000 1234.000 0.356
hsa-miR-3124-5p -1.000 2513.000 0.065
hsa-miR-3125 -1.000 2308.000 0.097
hsa-miR-3126-3p -1.000 2259.000 0.106
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