Statistics for Bioinformatics ：Methods for Multiple Sequence Alignment

Publication subTitle ：Methods for Multiple Sequence Alignment

Author： Thompson   Julie  

Publisher： Elsevier Science‎

Publication year： 2016

E-ISBN: 9780081019610

P-ISBN(Paperback): 9781785482168

Subject： Q811.4 biological information theory;TP3 Computers

Keyword： 分子生物学,遗传学,基础医学

Language： ENG

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Description

Statistics for Bioinformatics: Methods for Multiple Sequence Alignment provides an in-depth introduction to the most widely used methods and software in the bioinformatics field. With the ever increasing flood of sequence information from genome sequencing projects, multiple sequence alignment has become one of the cornerstones of bioinformatics. Multiple sequence alignments are crucial for genome annotation, as well as the subsequent structural, functional, and evolutionary studies of genes and gene products. Consequently, there has been renewed interest in the development of novel multiple sequence alignment algorithms and more efficient programs.

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Chapter

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

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