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Statistics for bioinformatics : methods for multiple sequence alignment /

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Bibliographic Details
Main Author: Thompson, Julie D. (Author)
Corporate Author: EBSCOhost
Format: eBook
Language:English
Published: London : ISTE Press : Elsevier, 2016.
Series:Statistics for bioinformatics set.
Subjects:
Bioinformatics > Statistical methods.
Computational Biology.
Sequence Alignment.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Statistics for Bioinformatics: Methods for Multiple Sequence Alignment
  • Copyright
  • Contents
  • Preface
  • PART 1 Fundamental Concepts
  • 1 Introduction
  • 1.1. Biological sequences: DNA/RNA/proteins
  • 1.2. From DNA to RNA and proteins
  • 1.3. RNA sequence, structure and function
  • 1.4. Protein sequence, structure and function
  • 1.5. Sequence evolution
  • 1.6. MSA: basic concepts
  • 1.7. Multiple sequence alignment applications
  • PART 2 Traditional Multiple Sequence Alignment Methods
  • 2 Heuristic Sequence Alignment Methods
  • 2.1. Optimal sequence alignment
  • 2.2. Progressive multiple alignment
  • 2.3. Iterative alignment
  • 2.4. Consistency-based alignment
  • 2.5. Cooperative alignment strategies
  • 3 Statistical Alignment Approaches
  • 3.1. Probabilistic models of sequence evolution
  • 3.2. Profile HMM-based alignment
  • 3.3. Simulated annealing
  • 3.4. Genetic algorithms
  • 4 Multiple Alignment Quality Control
  • 4.1. Objective scoring functions
  • 4.2. Determination of reliable regions
  • 4.3. Estimation of homology
  • 5 Benchmarking
  • 5.1. Criteria for benchmark construction
  • 5.2. Multiple alignment benchmarks
  • 5.3. Comparison of multiple alignment benchmarks
  • PART 3 Large-scale Multiple Sequence Alignment Methods
  • 6 Whole Genome Alignment
  • 6.1. Pairwise genome alignment
  • 6.2. Progressive methods for multiple genome alignment
  • 6.3. Graph-based methods for multiple genome alignment
  • 6.4. Meta-aligners for multiple genome alignment
  • 6.5. Accuracy measures for genome alignment methods
  • 6.6. Benchmarking genome alignment
  • 7 Multiple Alignment of Thousands of Sequences
  • 7.1. Extension of the progressive alignment approach
  • 7.2. Meta-aligners for large numbers of sequences
  • 7.3. Extending "seed" alignments
  • 7.4. Benchmarking large numbers of sequences.
  • 8 Future Perspectives: High-Performance Computing
  • 8.1. Coarse-grain parallelism: grid computing
  • 8.2. Fine-grain parallelism: GPGPU
  • 8.3. MSA in the cloud
  • Bibliography
  • Index
  • Back Cover.