生物序列分析Biological Sequence Analysis azw3 lrf 下载 百度云 夸克云 snb pdf kindle

Probabilistic methods are assuming greater significance in the analysis of nucleotide sequence data. This book provides the first unified, up-to-date and self-contained account of such methods, and more generally of probabilistic methods of sequence analysis, presented in a Bayesian framework.

Preface

1 Introduction

1.1 Sequence similarity, homology, and alignment

1.2 Overview of the book

1.3 Probabilities and probabilistic models

1.4 Further reading

2 Palrwise alignment

2.1 Introduction

2.2 The scoring model

2.3 Alignment algorithms

2.4 Dynamic programming with more complex models

2.5 Heuristic alignment algorithms

2.6 Linear space alignments

2.7 Significance of scores

2.8 Deriving score parameters from alignment data

2.9 Further reading

3 Markov chains and hidden Markov models

3.1 Markov chains

3.2 Hidden Markov models

3.3 Parameter estimation for HMMs

3.4 HMM model structure

3.5 More complex Markov chains

3.6 Numerical stability of HMM algorithms

3.7 Further reading

4 Pairwise alignment using HMMs

4.1 Pair HMMs

4.2 The full probability of x and y, summing over all paths

4.3 Suboptimal alignment

4.4 The posterior probability that xi is aligned to yi

4.5 Pair HMMs versus FgAs for searching

4.6 Further reading

5 Profile HMMs for sequence families

5.1 Ungapped score matrices

5.2 Adding insert and delete states to obtain profile HMMs

5.3 Deriving profile HMMs from multiple alignments

5.4 Searching with profile HMMs

5.5 Profile HMM variants for non-global alignments

5.6 More on estimation of probabilities

5.7 Optimal model construction

5.8 Weighting training sequences

5.9 Further reading

6 Multiple sequence alignment methods

6.1 What a multiple alignment means

6.2 Scoring a multiple alignment

6.3 Multidimensional dynamic programming

6.4 Progressive alignment methods

6.5 Multiple alignment by profile HMM training

6.6 Further reading

7 Building phylogenetic trees

7.1 The tree of life

7.2 Background on trees

7.3 Making a tree from pairwise distances

7.4 Parsimony

7.5 Assessing the trees: the bootstrap

7.6 Simultaneous alignment and phylogeny

7.7 Further reading

7.8 Appendix: proof of neighbour-joining theorem

8 Probabilistic approaches to phylogeny

8.1 Introduction

8.2 Probabilistic models of evolution

8.3 Calculating the likelihood for ungapped alignments

8.4 Using the likelihood for inference

8.5 Towards more realistic evolutionary models

8.6 Comparison of probabilistic and non-probabilistic methods

8.7 Further reading

9 Transformational grammars

9.1 Transformational grammars

9.2 Regular grammars

9.3 Context-free grammars

……

10　RNA structure analysis

11 Background on probability

Bibliography

Author index

Subject index

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Richard Durbin is Head of the Informatics Division at the Sanger Centre in Cambridge，England.

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书籍介绍

Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.