MITSU is an algorithm for discovery of transcription factor binding site (TFBS) motifs. It is based on the stochastic EM (sEM) algorithm, which overcomes some of the limitations of deterministic EM-based algorithms for motif discovery. Unlike previous sEM algorithms for motif discovery, MITSU is unconstrained with regard to the distribution of motif occurrences within the input dataset. MITSU also has the ability to automatically determine the most likely motif width by incorporating an information-based heuristic known as MCOIN. In tests on realistic synthetic and previously characterised prokaryotic data, MITSU has been shown to outperform an EM-based algorithm and previous sEM-based implementations.

MITSU is described in more detail in our paper:
A. M. Kilpatrick, B. Ward & S. Aitken, Stochastic EM-based TFBS motif discovery with MITSU
Bioinformatics, 30(12):i310-i318, 2014

Project Samples

Project Activity

See All Activity >

Follow MITSU

MITSU Web Site

Other Useful Business Software
Gen AI apps are built with MongoDB Atlas Icon
Gen AI apps are built with MongoDB Atlas

Build gen AI apps with an all-in-one modern database: MongoDB Atlas

MongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
Start Free
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of MITSU!

Additional Project Details

Registered

2014-03-14