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Updated: Jul 2, 2026
11:22
Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
Published on: January 30, 2018
Efficient representation and P-value computation for high-order Markov motifs.
Paulo G S da Fonseca1, Katia S Guimarães, Marie-France Sagot
1Centro de Informática, Universidade Federal de Pernambuco, 50732-970 Recife, Brazil. paguso@cin.ufpe.br
Bioinformatics (Oxford, England)
|August 12, 2008
Summary
Higher order Markov models improve biological sequence motif analysis by enabling efficient representation and P-value computation. This overcomes limitations of standard position weight matrices (PWMs).
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Position weight matrices (PWMs) are standard for biological sequence motifs, enabling efficient algorithms.
- Markov chain models generalize PWMs by considering interposition dependencies, but incur computational overhead.
Purpose of the Study:
- To address the representation and P-value computation challenges of higher order Markov models for biological sequence motifs.
- To develop efficient methods for analyzing complex sequence patterns beyond simple PWMs.
Main Methods:
- Proposed an efficient motif representation using tries for computing empirical position-specific conditional base probabilities.
- Extended existing PWM-based algorithms to compute exact P-values for high-order Markov motif models.
Main Results:
- Developed a novel, efficient trie-based representation for higher order Markov models.
- Enabled exact P-value computation for these complex motif models, enhancing statistical significance evaluation.
- The software is available as a Java library.
Conclusions:
- The proposed methods enhance the applicability of higher order Markov models in bioinformatics.
- This work provides efficient tools for motif identification and statistical significance evaluation with complex dependencies.
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