Input and runtime details for job 4526878 (precomputed example)

Training data

Class-annotated RNAs in FASTA

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

Training RNAs: k-mer feature counts [csv] and positions [txt]
Candidate RNAs: k-mer feature counts [csv] and positions [txt] and class prediction [csv]
Model: training statistics [txt] and model data [pkl]

Predictions for candidate RNAs

Predicted class

Sort by selecting a column name.

RNA id	class	rank	prob(1)
hsa-miR-891b	-1.000	1554.000	0.253
hsa-miR-892a	1.000	146.000	0.840
hsa-miR-892b	-1.000	1699.000	0.218
hsa-miR-892c-3p	1.000	117.000	0.860
hsa-miR-892c-5p	-1.000	2385.000	0.085
hsa-miR-9-3p	-1.000	2521.000	0.062
hsa-miR-9-5p	1.000	1.000	0.957
hsa-miR-920	-1.000	2412.000	0.081
hsa-miR-921	-1.000	2101.000	0.137
hsa-miR-922	-1.000	2650.000	0.028
hsa-miR-924	1.000	470.000	0.681
hsa-miR-92a-1-5p	1.000	767.000	0.542
hsa-miR-92a-2-5p	1.000	193.000	0.816
hsa-miR-92a-3p	1.000	534.000	0.651
hsa-miR-92b-3p	-1.000	841.000	0.500
hsa-miR-92b-5p	-1.000	2333.000	0.093
hsa-miR-93-3p	-1.000	1194.000	0.371
hsa-miR-93-5p	1.000	768.000	0.542
hsa-miR-933	-1.000	1308.000	0.330
hsa-miR-934	-1.000	1030.000	0.420
hsa-miR-935	1.000	103.000	0.865
hsa-miR-936	-1.000	2527.000	0.062
hsa-miR-937-3p	-1.000	846.000	0.500
hsa-miR-937-5p	-1.000	1507.000	0.268
hsa-miR-938	-1.000	1947.000	0.167
hsa-miR-939-3p	-1.000	1206.000	0.365
hsa-miR-939-5p	-1.000	1181.000	0.377
hsa-miR-940	-1.000	2208.000	0.114
hsa-miR-941	-1.000	1076.000	0.417
hsa-miR-942-3p	-1.000	2616.000	0.044
hsa-miR-942-5p	1.000	40.000	0.920
hsa-miR-943	1.000	35.000	0.926
hsa-miR-944	1.000	474.000	0.679
hsa-miR-95-3p	-1.000	1566.000	0.250
hsa-miR-95-5p	1.000	433.000	0.699
hsa-miR-9500	-1.000	2540.000	0.060
hsa-miR-96-3p	-1.000	1690.000	0.221
hsa-miR-96-5p	1.000	478.000	0.678
hsa-miR-9718	-1.000	1736.000	0.206
hsa-miR-98-3p	-1.000	839.000	0.500
hsa-miR-98-5p	1.000	18.000	0.930
hsa-miR-9851-3p	-1.000	1097.000	0.406
hsa-miR-9851-5p	1.000	316.000	0.753
hsa-miR-9898	1.000	794.000	0.531
hsa-miR-9899	-1.000	1756.000	0.200
hsa-miR-9900	-1.000	2212.000	0.113
hsa-miR-9901	-1.000	929.000	0.463
hsa-miR-9902	-1.000	2388.000	0.084
hsa-miR-9903	-1.000	2309.000	0.097
hsa-miR-9983-3p	-1.000	1252.000	0.349
hsa-miR-9985	-1.000	2260.000	0.106
hsa-miR-9986	1.000	306.000	0.760
hsa-miR-99a-3p	-1.000	2186.000	0.118
hsa-miR-99a-5p	-1.000	1081.000	0.417
hsa-miR-99b-3p	-1.000	2061.000	0.144
hsa-miR-99b-5p	-1.000	1684.000	0.223

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 :

Martin Raden, Thomas Wallach, Milad Miladi, Yuanyuan Zhai, Christina Krueger, Zoe J. Mossmann, Paul Dembny, Rolf Backofen, Seija Lehnardt
Structure-aware machine learning classification of oligonucleotide-induced immune response identifies microRNAs operating as Toll-like receptor 7/8 ligands
RNA Biology, 18:sup1, 268-277,2021.
Martin Raden, Syed M Ali, Omer S Alkhnbashi, Anke Busch, Fabrizio Costa, Jason A Davis, Florian Eggenhofer, Rick Gelhausen, Jens Georg, Steffen Heyne, Michael Hiller, Kousik Kundu, Robert Kleinkauf, Steffen C Lott, Mostafa M Mohamed, Alexander Mattheis, Milad Miladi, Andreas S Richter, Sebastian Will, Joachim Wolff, Patrick R Wright, and Rolf Backofen
Freiburg RNA tools: a central online resource for RNA-focused research and teaching
Nucleic Acids Research, 46(W1), W25-W29, 2018.

Results are computed with BrainDead version 1.0.1 using IntaRNA 3.1.5 and Vienna RNA package 2.4.14

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