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 an archive file containing the complete results produced by the job in [.zip] format.

Results file content

BrainDead is lazy..

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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