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-1243 -1.000 1501.000 0.270
hsa-miR-3122 -1.000 1502.000 0.269
hsa-miR-331-5p -1.000 1503.000 0.269
hsa-miR-1273h-3p -1.000 1504.000 0.269
hsa-miR-4518 -1.000 1505.000 0.269
hsa-miR-4481 -1.000 1506.000 0.269
hsa-miR-937-5p -1.000 1507.000 0.268
hsa-miR-6507-5p -1.000 1508.000 0.268
hsa-miR-144-5p -1.000 1509.000 0.268
hsa-miR-429 -1.000 1510.000 0.268
hsa-miR-411-3p -1.000 1511.000 0.268
hsa-miR-630 -1.000 1512.000 0.268
hsa-miR-12119 -1.000 1513.000 0.268
hsa-miR-6860 -1.000 1514.000 0.267
hsa-miR-513a-3p -1.000 1515.000 0.267
hsa-miR-1825 -1.000 1516.000 0.267
hsa-miR-4274 -1.000 1517.000 0.267
hsa-miR-5193 -1.000 1518.000 0.267
hsa-miR-6794-3p -1.000 1519.000 0.267
hsa-miR-4491 -1.000 1520.000 0.267
hsa-miR-4299 -1.000 1521.000 0.267
hsa-miR-4451 -1.000 1522.000 0.267
hsa-miR-6719-3p -1.000 1523.000 0.266
hsa-miR-4520-2-3p -1.000 1524.000 0.266
hsa-miR-1539 -1.000 1525.000 0.266
hsa-miR-7155-5p -1.000 1526.000 0.266
hsa-miR-642b-3p -1.000 1527.000 0.264
hsa-miR-668-5p -1.000 1528.000 0.264
hsa-miR-505-5p -1.000 1529.000 0.264
hsa-miR-1178-5p -1.000 1530.000 0.262
hsa-miR-5196-5p -1.000 1531.000 0.262
hsa-miR-661 -1.000 1532.000 0.261
hsa-miR-4746-3p -1.000 1533.000 0.261
hsa-miR-6736-5p -1.000 1534.000 0.261
hsa-miR-621 -1.000 1535.000 0.261
hsa-miR-1277-5p -1.000 1536.000 0.260
hsa-miR-3649 -1.000 1537.000 0.260
hsa-miR-5688 -1.000 1538.000 0.260
hsa-miR-493-3p -1.000 1539.000 0.259
hsa-miR-7843-3p -1.000 1540.000 0.259
hsa-miR-6759-5p -1.000 1541.000 0.258
hsa-miR-8078 -1.000 1542.000 0.257
hsa-miR-4777-5p -1.000 1543.000 0.256
hsa-miR-548i -1.000 1544.000 0.256
hsa-miR-450a-1-3p -1.000 1545.000 0.256
hsa-miR-4772-5p -1.000 1546.000 0.256
hsa-miR-4490 -1.000 1547.000 0.256
hsa-miR-24-3p -1.000 1548.000 0.255
hsa-miR-6816-5p -1.000 1549.000 0.255
hsa-miR-1257 -1.000 1550.000 0.255
hsa-miR-6803-5p -1.000 1551.000 0.255
hsa-miR-3138 -1.000 1552.000 0.254
hsa-miR-186-5p -1.000 1553.000 0.253
hsa-miR-891b -1.000 1554.000 0.253
hsa-miR-4430 -1.000 1555.000 0.253
hsa-miR-5572 -1.000 1556.000 0.253
hsa-miR-4784 -1.000 1557.000 0.252
hsa-miR-147b-3p -1.000 1558.000 0.251
hsa-miR-218-1-3p -1.000 1559.000 0.251
hsa-miR-11181-3p -1.000 1560.000 0.251
hsa-miR-1207-5p -1.000 1561.000 0.250
hsa-miR-6775-5p -1.000 1562.000 0.250
hsa-miR-320e -1.000 1563.000 0.250
hsa-miR-7973 -1.000 1564.000 0.250
hsa-miR-6083 -1.000 1565.000 0.250
hsa-miR-95-3p -1.000 1566.000 0.250
hsa-miR-23a-3p -1.000 1567.000 0.249
hsa-miR-1290 -1.000 1568.000 0.249
hsa-miR-6836-3p -1.000 1569.000 0.249
hsa-miR-6893-5p -1.000 1570.000 0.249
hsa-miR-516b-5p -1.000 1571.000 0.249
hsa-miR-6757-5p -1.000 1572.000 0.249
hsa-miR-5001-5p -1.000 1573.000 0.248
hsa-miR-10396a-3p -1.000 1574.000 0.248
hsa-miR-1305 -1.000 1575.000 0.248
hsa-miR-3059-3p -1.000 1576.000 0.248
hsa-miR-4262 -1.000 1577.000 0.248
hsa-miR-7157-5p -1.000 1578.000 0.248
hsa-miR-5689 -1.000 1579.000 0.248
hsa-let-7b-3p -1.000 1580.000 0.247
hsa-miR-122-3p -1.000 1581.000 0.247
hsa-miR-129-2-3p -1.000 1582.000 0.247
hsa-miR-190a-3p -1.000 1583.000 0.247
hsa-miR-155-3p -1.000 1584.000 0.247
hsa-miR-365a-3p -1.000 1585.000 0.247
hsa-miR-365b-3p -1.000 1586.000 0.247
hsa-miR-3613-3p -1.000 1587.000 0.247
hsa-miR-5583-5p -1.000 1588.000 0.247
hsa-miR-6802-3p -1.000 1589.000 0.247
hsa-miR-7109-3p -1.000 1590.000 0.247
hsa-miR-500a-5p -1.000 1591.000 0.246
hsa-miR-182-5p -1.000 1592.000 0.246
hsa-miR-3180-3p -1.000 1593.000 0.246
hsa-miR-1255b-5p -1.000 1594.000 0.245
hsa-miR-625-3p -1.000 1595.000 0.245
hsa-miR-4675 -1.000 1596.000 0.245
hsa-miR-103b -1.000 1597.000 0.245
hsa-miR-718 -1.000 1598.000 0.244
hsa-miR-1229-5p -1.000 1599.000 0.244
hsa-miR-3912-3p -1.000 1600.000 0.244
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