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Offers both speed and accuracy in finding multiple motifs in large datasets. THiCweed achieves its combination of speed and accuracy without resorting to heuristics in scoring and without resorting to training on a small subset of the sequences. It does not require prior information on the number of motifs or the lengths of the motif, since its approach is based on clustering rather than traditional motif-finding, and the clustering is based on stringent statistical criteria.

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THiCweed classification

THiCweed specifications

Software type:
Restrictions to use:
Input format:
BSD 3-clause “New” or “Revised” License
Command line interface
Input data:
Takes as input a single fasta-formatted file containing multiple sequences to be clustered on similarity. The sequences may be of different lengths.
Operating system:
Computer skills:
Julia, ArgParse module

THiCweed distribution


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THiCweed support


  • Rahul Siddharthan <>


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The Institute of Mathematical Sciences (HBNI), Chennai, India; CSIR National Chemical Laboratory, Pune, India

Funding source(s)

This work was supported by a Wellcome Trust-DBT India Alliance Early Career fellowship and the PRISM 12th plan project under the Department of Atomic Energy, Government of India.

Link to literature

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