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NNAlign-1.4 Server

Discovering sequence motifs in quantitative peptide data

View the version history of this server. All the previous versions are available online, for comparison and reference.

Instructions Output format Article abstract

1. TRAINING data

Paste peptides in PEPTIDE format

or submit a file directly from your local disk:


Alternatively, upload a trained MODEL

locally saved model:


Sample training data can be found HERE

Use this option if you previously trained a NNAlign model and
wish to use it for predictions on evaluation data

2. EVALUATION data (optional)

Paste in evaluation examples

or upload evaluation examples:


Sample evaluation data in FASTA or PEPTIDE format

3. Set advanced options (optional)

Customize the run by setting the parameters in the section below.

4. SUBMIT job

NOTE, depending on the size of your datasets and selected parameters it might take up to a few hours to complete the query.
Please be patient.
Instructions: Paste in or upload training examples to train the artificial neural networks. If you previously trained a NNAlign model, you may upload it in the right hand box, and use it for predictions on the evaluation set.

The evaluation set sequences can be either in FASTA format (sequences are automatically broken down into peptides) or as a list of peptides. For details refer to the instructions & guidelines page


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

Default options

Motif length (also as interval e.g. 7-9)





NOTE, depending on the size of your datasets and selected parameters it might take up to a few hours to complete the query.
Please be patient.

Confidentiality:
The sequences are kept confidential and will be deleted after processing.

Run locally:
A stand-alone version of the program tested on several architectures is available: DOWNLOAD


CITATIONS

For publication of results, please cite:

Andreatta M., Schafer-Nielsen C., Lund O., Buus S. and Nielsen M. (2011) "NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data". PLoS ONE 6(11): e26781. doi:10.1371/journal.pone.0026781 LINK


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