Protein Bioinformatics - Eidhammer, Ingvar

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Résumé :
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Biographie:
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Sommaire:
Preface.

Acknowledgements.

Part I: SEQUENCE ANALYSIS.

1. Pairwise Global Alignment of Sequences.

1.1 Alignment and Evolution.

1.2 What is an Alignment?

1.3 A Scoring Scheme for the Model.

1.4 Finding Highest-Scoring Alignments with Dynamic Programming.

1.4.1 Determine Hi,j.

1.4.2 Use of matrices.

1.4.3 Finding the alignments that give the highest score.

1.4.4 Gaps.

1.5 Scoring Matrices.

1.6 Scoring Gaps: Gap Penalties.

1.7 Dynamic Programming for General Gap Penalty.

1.8 Dynamic Programming for Affine Gap Penalty.

1.9 Alignment Score and Sequence Distance.

1.10 Exercises.

1.11 Bibliographic notes.

2 Pairwise Local Alignment and Database Search.

2.1 The Basic Operation: Comparing Two Sequences.

2.2 Dot Matrices.

2.2.1 Filtering.

2.2.2 Repeating segments.

2.3 Dynamic Programming.

2.3.1 Initialization.

2.3.2 Finding the best local alignments.

2.3.3 Algorithms.

2.3.4 Scoring matrices and gap penalties.

2.4 Database Search: BLAST.

2.4.1 The procedure.

2.4.2 Preprocess the query: make the word list.

2.4.3 Scanning the database sequences.

2.4.4 Extending to HSP.

2.4.5 Introducing gaps.

2.4.6 Algorithm.

2.5 Exercises.

2.6 Bibliographic notes.

3. Statistical Analysis.

3.1 Hypothesis Testing for Sequence Homology.

3.1.1 Random generation of sequences.

3.1.2 Use of Z values for estimating the statistical significance.

3.2 Statistical Distributions.

3.2.1 Poisson probability distribution.

3.2.2 Extreme value distributions.

3.3 Theoretical Analysis of Statistical Significance.

3.3.1 The P value has an extreme value distribution.

3.3.2 Theoretical analysis for database search.

3.4 Probability Distributions for Gapped Alignments.

3.5 Assessing and Comparing Programs for Database Search.

3.5.1 Sensitivity and specificity.

3.5.2 Discrimination power.

3.5.3 Using more sequences as queries.

3.6 Exercises.

3.7 Bibliographic notes.

4 .Multiple Global Alignment and Phylogenetic Trees.

4.1 Dynamic Programming.

4.1.1 SP score of multiple alignments.

4.1.2 A pruning algorithm for the DP solution.

4.2 Multiple Alignments and Phylogenetic Trees.

4.3 Phylogeny.

4.3.1 The number of different tree topologies.

4.3.2 Molecular clock theory.

4.3.3 Additive and ultrametric trees.

4.3.4 Different approaches for reconstructing phylogenetic trees.

4.3.5 Distance-based construction.

4.3.6 Rooting of trees.

4.3.7 Statistical test: bootstrapping.

4.4 Progressive Alignment.

4.4.1 Aligning two subset alignments.

4.4.2 Clustering.

4.4.3 Sequence weights.

4.4.4 CLUSTAL.

4.5 Other Approaches.

4.6 Exercises.

4.7 Bibliographic notes.

5. Scoring Matrices.

5.1 Scoring Matrices Based on Physio-Chemical Properties.

5.2 PAM Scoring Matrices.

5.2.1 The evolutionary model.

5.2.2 Calculate substitution matrix.

5.2.3 Matrices for general evolutionary time.

5.2.4 Measuring sequence similarity by use of M?.

5.2.5 Odds matrices.

5.2.6 Scoring matrices (log-odds matrices).

5.2.7 Estimating the evolutionary distance.

5.3 BLOSUM Scoring Matrices.

5.3.1 Log-odds matrix.

5.3.2 Developing scoring matrices for different evolutionary distances.

5.4 Comparing BLOSUM and PAM Matrices.