Statistics for bioinformatics : methods for multiple sequence alignment /

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 b...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Thompson, Julie D. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : ISTE Press : Elsevier, 2016.
Colección:Statistics for bioinformatics set.
Temas:
Bioinformatics > Statistical methods.
Sequence alignment (Bioinformatics)
Computational Biology
Sequence Alignment
Bio-informatique > M�ethodes statistiques.
Bio-informatique.
Homologie de s�equence (Bio-informatique)
NATURE > Reference.
SCIENCE > Life Sciences > Biology.
SCIENCE > Life Sciences > General.
Electronic books.
Internet Resources.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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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