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Related Experiment Video

Updated: Jul 2, 2026

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

MotifCluster: an interactive online tool for clustering and visualizing sequences using shared motifs.

Micah Hamady¹, Jeremy Widmann, Shelley D Copley

¹Department of Computer Science, University of Colorado, Boulder, CO 80309, USA.

|August 19, 2008

Summary

MotifCluster identifies related protein motifs and clusters sequences into families. This tool accurately assigns protein families to superfamilies with very low error rates, aiding in biological relationship discovery.

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Last Updated: Jul 2, 2026

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

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Published on: May 31, 2011

Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Identifying relationships between biological sequences is crucial for understanding protein function and evolution.
Conserved motifs within sequences are key indicators of functional or structural relatedness.
Existing tools may lack comprehensive capabilities for motif discovery, sequence clustering, and integrated visualization.

Purpose of the Study:

To introduce MotifCluster, a novel computational tool for identifying related motifs and clustering sequences.
To enable users to test protein relatedness and visualize motif distribution.
To assess the accuracy and performance of MotifCluster in classifying protein families.

Main Methods:

Motif discovery algorithms to identify conserved patterns within sequence datasets.
Sequence clustering based on the presence and distribution of identified motifs.
Visualization of motif mapping onto phylogenetic trees, sequence alignments, and 3D protein structures.
Validation using gold-standard protein superfamilies to quantify classification accuracy.

Main Results:

MotifCluster successfully identifies related motifs and clusters sequences into distinct families.
The tool provides integrated visualization of motif-sequence relationships.
Demonstrated high accuracy in assigning protein families to correct superfamilies using gold-standard datasets.
Achieved 0.17% false positive and 0% false negative assignments with recommended settings.

Conclusions:

MotifCluster is an accurate and effective tool for discovering related motifs and clustering biological sequences.
The tool facilitates the exploration of protein relationships and evolutionary patterns.
MotifCluster offers valuable insights for researchers in bioinformatics and related fields.