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-1-5p 1.000 501.000 0.661
hsa-miR-556-3p 1.000 502.000 0.661
hsa-miR-664a-3p 1.000 503.000 0.661
hsa-miR-4290 1.000 504.000 0.661
hsa-miR-6832-3p 1.000 505.000 0.661
hsa-miR-6833-3p 1.000 506.000 0.661
hsa-miR-6865-3p 1.000 507.000 0.661
hsa-miR-6867-3p 1.000 508.000 0.661
hsa-miR-6870-3p 1.000 509.000 0.661
hsa-miR-6887-3p 1.000 510.000 0.661
hsa-miR-6891-3p 1.000 511.000 0.661
hsa-miR-7162-5p 1.000 512.000 0.661
hsa-miR-508-3p 1.000 513.000 0.660
hsa-miR-561-5p 1.000 514.000 0.659
hsa-miR-4525 1.000 515.000 0.659
hsa-miR-6742-5p 1.000 516.000 0.659
hsa-miR-4760-5p 1.000 517.000 0.658
hsa-miR-1538 1.000 518.000 0.658
hsa-miR-451a 1.000 519.000 0.658
hsa-miR-208a-5p 1.000 520.000 0.657
hsa-miR-188-3p 1.000 521.000 0.657
hsa-miR-4650-5p 1.000 522.000 0.657
hsa-miR-4724-3p 1.000 523.000 0.656
hsa-miR-5003-3p 1.000 524.000 0.656
hsa-miR-1251-3p 1.000 525.000 0.655
hsa-miR-367-5p 1.000 526.000 0.655
hsa-miR-769-5p 1.000 527.000 0.654
hsa-miR-3927-3p 1.000 528.000 0.653
hsa-miR-203a-3p 1.000 529.000 0.653
hsa-miR-6513-5p 1.000 530.000 0.653
hsa-miR-1225-3p 1.000 531.000 0.653
hsa-miR-26b-3p 1.000 532.000 0.653
hsa-miR-4761-3p 1.000 533.000 0.651
hsa-miR-92a-3p 1.000 534.000 0.651
hsa-miR-4473 1.000 535.000 0.651
hsa-miR-1200 1.000 536.000 0.650
hsa-miR-5002-3p 1.000 537.000 0.650
hsa-miR-6778-3p 1.000 538.000 0.650
hsa-miR-6786-3p 1.000 539.000 0.650
hsa-miR-6812-3p 1.000 540.000 0.650
hsa-miR-217-3p 1.000 541.000 0.650
hsa-miR-4664-5p 1.000 542.000 0.650
hsa-miR-603 1.000 543.000 0.648
hsa-miR-548c-3p 1.000 544.000 0.648
hsa-miR-548j-3p 1.000 545.000 0.648
hsa-miR-1255b-2-3p 1.000 546.000 0.648
hsa-miR-3121-5p 1.000 547.000 0.648
hsa-miR-548aq-3p 1.000 548.000 0.648
hsa-miR-548az-3p 1.000 549.000 0.648
hsa-miR-591 1.000 550.000 0.648
hsa-miR-3934-3p 1.000 551.000 0.648
hsa-miR-486-5p 1.000 552.000 0.648
hsa-miR-597-5p 1.000 553.000 0.648
hsa-miR-3924 1.000 554.000 0.648
hsa-miR-4420 1.000 555.000 0.648
hsa-miR-10525-3p 1.000 556.000 0.648
hsa-miR-4762-3p 1.000 557.000 0.647
hsa-miR-548c-5p 1.000 558.000 0.647
hsa-miR-548o-5p 1.000 559.000 0.647
hsa-miR-548am-5p 1.000 560.000 0.647
hsa-miR-548au-5p 1.000 561.000 0.647
hsa-miR-4711-3p 1.000 562.000 0.646
hsa-miR-32-5p 1.000 563.000 0.646
hsa-miR-670-3p 1.000 564.000 0.646
hsa-miR-4686 1.000 565.000 0.646
hsa-miR-598-5p 1.000 566.000 0.646
hsa-miR-657 1.000 567.000 0.646
hsa-miR-3200-3p 1.000 568.000 0.645
hsa-miR-454-5p 1.000 569.000 0.645
hsa-miR-6129 1.000 570.000 0.645
hsa-miR-587 1.000 571.000 0.645
hsa-miR-4798-5p 1.000 572.000 0.645
hsa-miR-548w 1.000 573.000 0.644
hsa-miR-548bb-5p 1.000 574.000 0.644
hsa-miR-103a-3p 1.000 575.000 0.644
hsa-miR-6881-3p 1.000 576.000 0.643
hsa-miR-6884-3p 1.000 577.000 0.643
hsa-miR-3689a-3p 1.000 578.000 0.642
hsa-miR-7160-5p 1.000 579.000 0.642
hsa-miR-7154-3p 1.000 580.000 0.640
hsa-miR-4748 1.000 581.000 0.640
hsa-let-7a-3p 1.000 582.000 0.638
hsa-miR-16-2-3p 1.000 583.000 0.638
hsa-miR-6835-3p 1.000 584.000 0.638
hsa-miR-6529-3p 1.000 585.000 0.638
hsa-miR-6841-5p 1.000 586.000 0.638
hsa-miR-5697 1.000 587.000 0.638
hsa-miR-556-5p 1.000 588.000 0.637
hsa-miR-4722-5p 1.000 589.000 0.635
hsa-miR-2277-3p 1.000 590.000 0.635
hsa-miR-330-5p 1.000 591.000 0.634
hsa-miR-3913-5p 1.000 592.000 0.634
hsa-miR-616-3p 1.000 593.000 0.631
hsa-miR-181c-3p 1.000 594.000 0.631
hsa-miR-569 1.000 595.000 0.630
hsa-miR-10397-5p 1.000 596.000 0.629
hsa-miR-548t-3p 1.000 597.000 0.629
hsa-miR-548aa 1.000 598.000 0.629
hsa-miR-4726-3p 1.000 599.000 0.628
hsa-miR-1911-3p 1.000 600.000 0.628
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