Submission

Paste in training examples,

or upload training examples

Paste in evaluation examples,

or, upload evaluation examples
Valid format:
column format. Example: Training set,
(data from SYFPEITHI and MHCpep. Ref, see the Abstract) 		Valid formats:
column format, Example: Evaluation set (Geluk et al., Diabetes 47 1594-1600 (1998))
or fasta format, Example: gp120.
Instructions: Paste in or upload training examples to train a prediction method. To evaluate the performance of the method Paste in or upload evaluation examples as well.

Please read the CBS access policies for information about limitations on the daily number of submissions.

Advanced options

Motif parameters

Motif length (min 1, max 51).

Matrix parameters

Clustering method.
Henikoff & Henikoff 1/nr method
Cluster at 62% identity
No clustering

Weight on prior.

General parameters


Sorting of output
Sort output on predicted values
Don't sort output

Sampling parameters

Start temperature


End temperature


Number of temperature steps (default 10, max 1000)


Random seed (-1: default, 0: seed on time, \>0: choose seed)


Number of iterations per training example (default 20, max 1000)



Background model
Flat (all amino acids have probability0.05)
Swissprot
From training data

Cutoff for counting an example as a positive example.

Position specific weighting

Weights on the different positions (format example: "1,3,1,1,1,1,1,1,3". Leave blank to choose default: equal weight on all positions)

Load saved prediction method

Paste in parameters,

or upload parameter file

CITATIONS

For publication of results, please cite:

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, Brunak S, Lund O. Bioinformatics. 2004 20:1388-97

EasyGibbs. To be published