BrainDead - RNA classification via accessible k-mers

BrainDead learns and predicts a two-class model for short RNA sequences based on accessibility-enhanced k-mer features. That is, the model considers whether or not k-mers are covered by intra-molecular or homo-dimer base pairs of stable structures or RNA-RNA interactions.

For articles describing the tool and webserver refer to the reference section below. Please cite us when using our tools.
For more information check the help page.

Try BrainDead with this example: miRNAs as ligands for microglia activation (input|result)

Training data

$Class-annotated RNAs in FASTA: The input has to be in valid FASTA format. No multiline input is allowed (sequence etc. in one line each). The number of sequences has to be at least 20 and at most 1000. Sequence lengths have to be in the range 10-40. The allowed sequence alphabet is 'ACGUTacgut'. Each FASTA sequence header/name has to match against the regular expression '\S+\s+[-+]?1'. Click for further information!$

Class-annotated RNAs in FASTA:

[clear]

[+] Model parameters [-] Model parameters

k-mers of interest:

k-mer features reflect:

number of occurrences (feature is number >= 0)
existence (feature is 0 or 1)

Max. energy ranked stable:

Machine learning model:

Support Vector Classification using RBF kernel
Support Vector Classification using linear kernel

Candidate data

Candidate RNAs in FASTA:
(optional)

[clear]

Description:

Your Email:

(optional)
(optional)

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 3.2.0 and Vienna RNA package 2.4.18

Main Menu

Freiburg RNA Tools

Interaction Prediction

Seq-Str Alignment

CRISPR

Sequence Design

Splicing

SNP & Mutation

Classification

Gene set extension

MoDPepInt Server

CPSP-Tools Server

Galaxy-FR

CMV server

Teaching

BrainDead - RNA classification via accessible k-mers

Training data

[+] Model parameters [-] Model parameters

Candidate data

When using BrainDead please cite :