Protein Homology Detection Through Alignment of Markov Random Fields

Using MRFalign

  • Jinbo Xu
  • Sheng Wang
  • Jianzhu Ma

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Jinbo Xu, Sheng Wang, Jianzhu Ma
    Pages 1-16
  3. Jinbo Xu, Sheng Wang, Jianzhu Ma
    Pages 17-30
  4. Jinbo Xu, Sheng Wang, Jianzhu Ma
    Pages 31-36
  5. Jinbo Xu, Sheng Wang, Jianzhu Ma
    Pages 37-48
  6. Back Matter
    Pages 49-51

About this book

Introduction

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.

Keywords

Hidden Markov Model Long-Range Residue Interaction Markov Random Fields Position-Specific Scoring Matrix Protein Homology Detection

Authors and affiliations

  • Jinbo Xu
    • 1
  • Sheng Wang
    • 2
  • Jianzhu Ma
    • 3
  1. 1.Toyota Technological InstituteChicagoUSA
  2. 2.Toyota Technological InstituteChicagoUSA
  3. 3.Toyota Technological InstituteChicagoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-14914-1
  • Copyright Information The Author(s) 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-14913-4
  • Online ISBN 978-3-319-14914-1
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • About this book