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: 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

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-744-5p	-1.000	2201.000	0.115
hsa-miR-224-3p	-1.000	2202.000	0.115
hsa-miR-1245a	-1.000	2203.000	0.115
hsa-miR-4655-5p	-1.000	2204.000	0.115
hsa-miR-4637	-1.000	2205.000	0.115
hsa-miR-4758-5p	-1.000	2206.000	0.114
hsa-miR-1909-3p	-1.000	2207.000	0.114
hsa-miR-940	-1.000	2208.000	0.114
hsa-miR-6885-5p	-1.000	2209.000	0.113
hsa-miR-1227-5p	-1.000	2210.000	0.113
hsa-miR-4466	-1.000	2211.000	0.113
hsa-miR-9900	-1.000	2212.000	0.113
hsa-miR-383-3p	-1.000	2213.000	0.113
hsa-miR-6787-5p	-1.000	2214.000	0.113
hsa-miR-4786-3p	-1.000	2215.000	0.113
hsa-miR-6081	-1.000	2216.000	0.113
hsa-miR-12117	-1.000	2217.000	0.112
hsa-miR-4520-3p	-1.000	2218.000	0.112
hsa-miR-6829-5p	-1.000	2219.000	0.112
hsa-miR-7151-3p	-1.000	2220.000	0.111
hsa-miR-3936	-1.000	2221.000	0.111
hsa-miR-6810-5p	-1.000	2222.000	0.111
hsa-miR-3621	-1.000	2223.000	0.111
hsa-miR-6845-5p	-1.000	2224.000	0.111
hsa-miR-302c-5p	-1.000	2225.000	0.111
hsa-miR-363-5p	-1.000	2226.000	0.110
hsa-miR-3922-5p	-1.000	2227.000	0.110
hsa-miR-4452	-1.000	2228.000	0.110
hsa-miR-6512-5p	-1.000	2229.000	0.110
hsa-miR-6068	-1.000	2230.000	0.110
hsa-miR-583	-1.000	2231.000	0.110
hsa-miR-4695-5p	-1.000	2232.000	0.109
hsa-miR-3675-3p	-1.000	2233.000	0.109
hsa-miR-6850-3p	-1.000	2234.000	0.109
hsa-miR-222-3p	-1.000	2235.000	0.109
hsa-miR-150-3p	-1.000	2236.000	0.109
hsa-miR-3177-3p	-1.000	2237.000	0.109
hsa-miR-4535	-1.000	2238.000	0.109
hsa-miR-6732-5p	-1.000	2239.000	0.109
hsa-miR-4463	-1.000	2240.000	0.109
hsa-miR-25-5p	-1.000	2241.000	0.109
hsa-miR-6503-3p	-1.000	2242.000	0.109
hsa-miR-510-5p	-1.000	2243.000	0.108
hsa-miR-3944-5p	-1.000	2244.000	0.108
hsa-miR-5706	-1.000	2245.000	0.108
hsa-miR-12120	-1.000	2246.000	0.108
hsa-miR-7150	-1.000	2247.000	0.108
hsa-miR-5009-3p	-1.000	2248.000	0.107
hsa-miR-6722-3p	-1.000	2249.000	0.107
hsa-miR-27b-5p	-1.000	2250.000	0.107
hsa-miR-6770-5p	-1.000	2251.000	0.107
hsa-miR-4423-3p	-1.000	2252.000	0.107
hsa-miR-1285-3p	-1.000	2253.000	0.107
hsa-miR-3200-5p	-1.000	2254.000	0.107
hsa-miR-4432	-1.000	2255.000	0.106
hsa-miR-7153-5p	-1.000	2256.000	0.106
hsa-miR-766-5p	-1.000	2257.000	0.106
hsa-miR-589-5p	-1.000	2258.000	0.106
hsa-miR-3126-3p	-1.000	2259.000	0.106
hsa-miR-9985	-1.000	2260.000	0.106
hsa-miR-5693	-1.000	2261.000	0.106
hsa-miR-615-3p	-1.000	2262.000	0.105
hsa-miR-4498	-1.000	2263.000	0.105
hsa-miR-3116	-1.000	2264.000	0.105
hsa-miR-3152-3p	-1.000	2265.000	0.105
hsa-miR-3679-5p	-1.000	2266.000	0.105
hsa-miR-887-3p	-1.000	2267.000	0.105
hsa-miR-490-3p	-1.000	2268.000	0.104
hsa-miR-3150b-5p	-1.000	2269.000	0.104
hsa-miR-6872-5p	-1.000	2270.000	0.104
hsa-miR-151a-5p	-1.000	2271.000	0.104
hsa-miR-151b	-1.000	2272.000	0.104
hsa-miR-3646	-1.000	2273.000	0.104
hsa-miR-378b	-1.000	2274.000	0.104
hsa-miR-511-3p	-1.000	2275.000	0.104
hsa-miR-1180-5p	-1.000	2276.000	0.103
hsa-miR-425-3p	-1.000	2277.000	0.103
hsa-miR-3978	-1.000	2278.000	0.103
hsa-miR-4521	-1.000	2279.000	0.103
hsa-miR-466	-1.000	2280.000	0.103
hsa-miR-18a-5p	-1.000	2281.000	0.103
hsa-miR-4458	-1.000	2282.000	0.103
hsa-miR-640	-1.000	2283.000	0.103
hsa-miR-4293	-1.000	2284.000	0.103
hsa-miR-4715-3p	-1.000	2285.000	0.102
hsa-miR-508-5p	-1.000	2286.000	0.102
hsa-miR-5681a	-1.000	2287.000	0.101
hsa-miR-375-5p	-1.000	2288.000	0.101
hsa-miR-4431	-1.000	2289.000	0.101
hsa-miR-1233-5p	-1.000	2290.000	0.101
hsa-miR-6848-5p	-1.000	2291.000	0.100
hsa-miR-106b-5p	-1.000	2292.000	0.100
hsa-miR-4781-5p	-1.000	2293.000	0.100
hsa-miR-7107-5p	-1.000	2294.000	0.099
hsa-miR-6846-5p	-1.000	2295.000	0.099
hsa-miR-4251	-1.000	2296.000	0.099
hsa-miR-3117-3p	-1.000	2297.000	0.098
hsa-miR-3074-3p	-1.000	2298.000	0.098
hsa-miR-4285	-1.000	2299.000	0.098
hsa-miR-34c-3p	-1.000	2300.000	0.098

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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