Protein Homology Detection Through Alignment of Markov Random Fields: Using MRFalign (SpringerBriefs in Computer Science)

Protein Homology Detection Through Alignment of Markov Random Fields: Using MRFalign (SpringerBriefs in Computer Science)

Language: English

Pages: 51

ISBN: 331914913X

Format: PDF / Kindle (mobi) / ePub


This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.

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. . . . . . . . . . . . . . . 2.7 Algorithms for Aligning Two Markov Random Fields. References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Software. . . . . . . . . . . . . . . . . . . . . . 3.1 Overview of Program . . . . . . . . . 3.2 Software Download . . . . . . . . . . 3.3 Feature Files . . . . . . . . . . . . . . . 3.4 MRFsearch Ranking File . . . . . . . 3.5 Interpreting P-Value . . . . . . . . . . 3.6 Interpreting a Pairwise Alignment. References. . . . . . . .

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respectively. Mutual Table 4.15 Contribution of the edge alignment potential and mutual information (MI), measured by alignment recall improvement on two benchmarks Set3.6K and Set2.6K Set3.6K Exact match (%) Only node potential 44.7 Node + edge potential, no 48.1 MI Node + edge potential with 49.2 MI The structure alignments generated by DeepAlign 4-offset (%) Set2.6K Exact match (%) 4-offset (%) 48.6 52.2 68.6 72.3 71.8 75.2 53.5 74.2 77.8 are used as reference alignments 4.6

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