This book is basically used for a bioinformatics class in France and translated in English. I've been working on developing tools for ncRNA gene predictions and this book clarifies of the mystery in genomics what I could not reach by biology books. Using pictures and figures, it's a pretty much comprehensive book. If you are a novice in bioinformatics field, I would recommend reading this book. However, if you expect to learn something in more specific such as machine learning or etc., then you may be disappointed.
Author(s): Frédéric Dardel, François Képès, Noah Hardy
Edition: 1
Publisher: Wiley
Year: 2006
Language: English
Pages: 253
Cover......Page 1
Contents......Page 7
Preface to the French edition......Page 9
Preface to the English edition......Page 11
1.1 Automatic sequencing......Page 13
1.2 Sequencing strategies......Page 16
1.3 Fragmentation strategies......Page 20
1.4 Sequence assembly......Page 24
1.5 Filling gaps......Page 26
1.6 Obstacles to reconstruction......Page 28
1.7 Utilizing a complementary ‘large’1 clone library......Page 30
1.8 The first large-scale sequencing project: The Haemophilus influenzae genome......Page 31
1.9 cDNA and EST......Page 32
2.1 Introduction: Comparison as a sequence prediction method......Page 37
2.2 A sample molecule: the human androsterone receptor......Page 38
2.3 Sequence homologies – functional homologies......Page 39
2.4 Comparison matrices......Page 40
2.5 The problem of insertions and deletions......Page 45
2.6 Optimal alignment: the dynamic programming method......Page 46
2.7 Fast heuristic methods......Page 50
2.8 Sensitivity, specificity, and confidence level......Page 58
2.9 Multiple alignments......Page 62
3.1 General properties of genomes......Page 73
3.2 Genome comparisons......Page 79
3.3 Gene evolution and phylogeny: applications to annotation......Page 87
4.2 Genes and the genetic code......Page 97
4.3 Expression signals......Page 99
4.5 Sites located on DNA......Page 103
4.7 Pattern detection methods......Page 108
5.2 Nucleotide base and amino acid distribution......Page 119
5.3 The biological basis of codon bias......Page 124
5.4 Using statistical bias for prediction......Page 125
5.5 Modeling DNA sequences......Page 128
5.6 Complex models......Page 132
5.7 Sequencing errors and hidden Markov models......Page 135
5.9 The search for genes – a difficult art......Page 139
6.1 The structure of RNA......Page 143
6.2 Properties of the RNA molecule......Page 144
6.3 Secondary RNA structures......Page 146
6.4 Thermodynamic stability of RNA structures......Page 150
6.5 Finding the most stable structure......Page 156
6.6 Validation of predicted secondary structures......Page 161
6.7 Using chemical and enzymatic probing to analyze folding......Page 162
6.8 Long-distance interactions and three-dimensional structure prediction......Page 164
6.9 Protein structure......Page 167
6.10 Secondary structure prediction......Page 170
6.11 Three-dimensional modeling based on homologous protein structure......Page 173
6.12 Predicting folding......Page 178
7.1 Introduction......Page 181
7.2 Post-genomic methods......Page 182
7.3 Macromolecular networks......Page 194
7.4 Topology of macromolecular networks......Page 205
7.5 Modularity and dynamics of macromolecular networks......Page 211
7.6 Inference of regulatory networks......Page 218
8: Simulation of biological processes in the genome context......Page 223
8.2 Prediction and explanation......Page 225
8.3 Simulation of molecular networks......Page 227
8.4 Generic post-genomic simulators......Page 238
Index......Page 245
