DLocalMotif is a discriminitive motif discovery web service specifically designed to discover local motifs in protein sequences that are aligned relative to a defined sequence landmark. It uses three discriminitive scoring features, motif spatial confinement (MSC), motif over-representation (MOR) and motif relative entropy (MRE). These features establish if a motif is positioned in a constrained sequence interval in positive data set and absent in negative data set.

- A standalone software of our method is available here.

If you use DLocalMotif in your research, please cite the following paper:

Ahmed M. Mehdi, Muhammad Shoaib B. Sehgal, Bostjan Kobe, Timothy L. Bailey and Mikael Bodén "DLocalMotif: A discriminative approach for discovering local motifs in protein sequences" (2013), Bioinformatics, 29 (1):39-46 [Abstract and Full Text]

Data Submission Form

Note: Please do not discard this page, the results will be updated on this page.

Please enter the positive and/or negative protein sequences which you believe containing local motifs. The sequences may contain no more than 6000 amino acids and should be in fasta format. If you have larger sequences, please download our standalone software.
Enter the name of file containing the positive sequences here:

Enter the name of file containing the negative sequences (if any) here:

the positive data set here (Sample Protein Input Sequences):

the negative data set here:

Which objective function you would like to use?
Sum of three scores
Product of three scores

DLocalMotif will find the optimum width of each motif within the limits you specify here:

Minimum width of a motif ( ≥ 3)
Maximum width of a motif ( ≤ length of (≤ length aligned sequences)

Maximum number of motifs

Tolerance in local motif shift (|r2-r1|)

p-value threshold