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HH-MOtif-TreeFinder HH-MOTiF


Combines hidden Markov model (HHM) comparisons with a hierarchical representation of identified SLiMs in motif trees. HH-MOTiF is a web-server that can find remotely conserved motifs in data sets with low-complexity regions or high redundancy. It makes use of evolutionary information by creating HMMs for each input sequence and its orthologs. Finally, this method can detect several independent motif trees that occur in independent, possibly overlapping subsets of the provided input sequences.

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HH-MOTiF classification

HH-MOTiF specifications

Web user interface
Input data:
The input set must contain full protein sequences.
Output format:
Computer skills:
Restrictions to use:
Input format:
Programming languages:
C++, Python

HH-MOTiF support


  • Bianca Habermann <>


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Computational Biology Group, Max Planck Institute of Biochemistry, Martinsried, Germany; Research Group Quantitative Biology and Bioinformatics, Max Planck Institute for Biophysical Chemistry, Gottingen, Germany; Computational Biology Group, Developmental Biology Institute of Marseille (IBDM) UMR 7288, CNRS, Aix Marseille Universite, Marseille, France

Funding source(s)

This work was supported by the Max Planck Society and the CNRS.

Link to literature

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