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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

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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).

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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.