Computational methods for understanding bacterial and archaeal genomes

CONTENTS......Page 12
Preface......Page 6
List of Contributors......Page 14
Acknowledgments......Page 20
1.1. The Replicon Concept and Classification of Replicons......Page 22
1.2. Physical Organization of Replicons in the Cell......Page 25
2.1. Size and Gene Content......Page 26
2.2. Why Are Prokaryotic Chromosomes Small?......Page 28
2.3. G+C Content......Page 30
2.4. Oligonucleotide Composition and Genome Signature......Page 33
3.1. Intrachromosomal Variance of Nucleotide and Oligonucleotide Composition......Page 36
3.2. Synonymous Codon Usage......Page 37
3.4. G–C Skew......Page 42
4.1. Large Repeats and Duplications......Page 44
4.2. Transposons and Insertion Sequences......Page 46
4.3. Integrons......Page 48
4.4. Chimeric Mobile Elements: Conjugative Transposons, ICEs, Plasmid-Prophages, Transposon-Prophages, Genomic Islands, and Genetic Litter......Page 49
4.6. Short Dispersed Repeats......Page 50
4.7. Simple Sequence Repeats......Page 53
4.8. CRISPR Repeats......Page 56
Acknowledgments......Page 58
1. Introduction......Page 60
2.1. The GeneMark Program......Page 62
3.1. The Glimmer Program......Page 64
3.2. Using Deleted Interpolation in Gene Prediction......Page 66
4. Hidden Markov Models......Page 68
4.2. The Viterbi Algorithm......Page 69
4.3. HMM Training......Page 70
4.4. The ECOPARSE Program......Page 71
4.5. The GeneHacker Program......Page 72
4.6. HMM Versions of the GeneMark Program......Page 73
5.1. The GeneScan Program......Page 74
5.2. The Lengthen-Shu.e Program......Page 75
6.1. The RescueNet Program......Page 76
8.1. The ZCURVE Program......Page 77
9.1. The GeneMark-Genesis Program......Page 78
9.3. The MED Program......Page 79
10.1. The ORPHEUS Program......Page 80
10.3. The BDGF Program......Page 81
10.4. The EasyGene Program......Page 82
11. Gene Start Prediction......Page 83
12. Resolving Overlapping Genes......Page 86
13. Non-coding RNA Gene Prediction......Page 87
14. Assessing Gene Prediction Programs......Page 89
15. Discussion......Page 92
Acknowledgments......Page 95
1.1. The Amino Acids......Page 96
1.2. Codon Designations......Page 97
1.3. Transfer RNA......Page 98
1.4. Aminoacyl-tRNA Synthetases......Page 100
2.1. Non-canonical Codes......Page 102
2.2. Selenocysteine......Page 103
2.3. Pyrrolysine......Page 105
2.5. Asparagine and Glutamine......Page 107
2.6. Evolutionary Considerations......Page 108
2.7. Nanoarchaeal tRNA......Page 110
3.1. Overview......Page 111
3.2. Physiochemical Models......Page 112
3.3. Historical Models......Page 114
4. Summary......Page 117
6. Mentioned Computer Programs......Page 118
1.2. Assessing Evolution Using Gene Samplings......Page 120
2.1. Reciprocal Best BLAST Hits......Page 121
2.2. Sequential Genome Samplings......Page 122
2.3. Gene Frequency Histograms......Page 123
3. The Shrinking Core......Page 124
4. The Pan-Genome......Page 126
4.1. Classification of Proteins Based on Their Frequency of Occurrence: The Extended Core, Character Genes and Accessory Pool......Page 127
5. How Are Biological Innovations Made?......Page 130
6. Summary and Concluding Remarks......Page 131
Acknowledgments......Page 132
2.1. Genomic Islands......Page 134
2.2. Prophage......Page 137
2.3. Integrons......Page 138
2.4. Transposons and IS Elements......Page 140
2.5. Other Mobile Elements......Page 142
3.1.1. Sequence Composition-based Approaches......Page 143
3.1.2. SIGI and SIGI-HMM......Page 145
3.1.5. Z-Curve (GC Profile)......Page 146
3.1.8. Comparative Genomics-based Approaches......Page 147
3.2. Detection of Prophages......Page 148
3.3. Detection of Integrons......Page 149
3.4. Detection of Transposons and IS Elements......Page 150
4. Resources......Page 151
5. Discussion......Page 152
7. Further Reading......Page 154
Glossary......Page 155
1.2. Towards a Natural Taxonomy......Page 158
1.3. Ribosomal RNA versus Other Molecular Markers......Page 159
1.5. Patterns Created Through Convergent Evolution......Page 160
1.6. Other Artifacts That Can Lead to Misleading Genome Phylogenies......Page 161
2.2. What Is the Unit of Selection in Microbial Evolution?......Page 162
3.1. Surrogate Methods......Page 164
3.2. Unusual Phyletic Patterns......Page 166
3.3. Phylogenetic Incongruence......Page 167
5. Further Reading......Page 171
Acknowledgments......Page 172
1. Introduction......Page 174
2. Genome Reduction as a General Phenomenon......Page 178
3. Reconstruction of Ancestral Genomes......Page 180
3.1. Production of a Table of Orthologous Gene Groups in the Compared Genomes......Page 183
3.2. Determination of Gene Content of One or Several Ancestors......Page 185
3.3. Gene Order Reconstruction in Ancestral Genomes......Page 190
4. Evolutionary Process of Genome Reduction......Page 191
5. Minimal Gene Sets......Page 196
5.1. Minimal Natural Genomes: Nature’s Four Most Remarkable Experiments on Genome Minimization......Page 197
5.2. Extreme Genome Reduction: Cell Versus Organelle......Page 200
5.3. Defining the Minimal Gene Complement of a Living Cell by Comparative Genomics......Page 201
6. Summary......Page 204
Acknowledgments......Page 205
1. Introduction......Page 206
2.1. Operons and Transcription Units......Page 207
3.1. The Basal Transcription Machinery in Archaea......Page 209
3.2. The Basal Transcription Machinery in Bacteria......Page 211
3.4. Cis-Elements in σ70-Depending Promoters......Page 212
3.5. Bacterial σ54-Dependent Promoters......Page 213
4.3. Transcription Termination in Archaea......Page 215
5.1. Transcriptional Signal Sensing......Page 216
5.2. Exogenous and Endogenous Signal Sensing by TFs......Page 217
5.3. Additional Mechanisms Regulating the TF Activity......Page 218
6.3. Transcriptional Activation in Bacterial σ54 Promoters......Page 219
7.1. Transcriptional Regulation by Nucleoid-Structure in Bacteria......Page 220
7.2. Transcriptional Regulation by Nucleoid-Structure in Archaea......Page 221
7.3. Nucleoid-Associated Proteins in Bacteria......Page 222
8.2. Attenuators......Page 224
8.4. mRNA Half-Life......Page 225
9.2. Noisy Gene Transcription......Page 226
9.4. Transcriptional Regulatory Network......Page 227
10. Summary......Page 228
Acknowledgments......Page 229
1. Introduction......Page 230
2. Basics of Orthologs, Inparalogs and Outparalogs, and Xenologs......Page 231
3.1. Sequence Similarity as Basis for Ortholog Selection Methods......Page 235
3.2. Existing Databases of Protein Families......Page 236
4. Gene Family and Superfamily Classification......Page 238
4.1. Attempt to Unify Di.erent Family-Superfamily Classifications......Page 239
5.1. Reciprocal Best BLAST Hit (RBH) Method......Page 241
5.2. Reciprocal Smallest Distance (RSD) Method......Page 243
6.1. Tree Reconciliation Method......Page 244
6.2. Phylogenetic Clustering Method: BranchClust......Page 245
7. Summary......Page 251
8. Further Reading......Page 252
1. Introduction......Page 254
2.2. Transcriptional Regulation of Genes in an Operon......Page 256
2.4. Uber-operons as Conserved Groups of Operons......Page 258
3.2. RT-PCR......Page 260
4.1. Feature Selection......Page 261
4.2. Prediction Methods......Page 262
4.3. Prediction Evaluation......Page 264
5.2. Prediction of Orthologous Genes......Page 266
6.1. Origins of Gene Clusters......Page 267
6.2.2. Gene Order Is Not Conserved During Bacterial Evolution......Page 268
6.2.3. Adaptive Evolution of Bacterial Operons......Page 269
7. Computational Prediction of Uber-operons......Page 270
8.1. Internet Resources for Known Operons......Page 271
8.2. Internet Resources for Operon Prediction......Page 272
8.3. Internet Resources for Uber-operon Prediction......Page 274
9. Challenges Ahead......Page 275
10. Summary......Page 276
11. Further Reading......Page 277
Acknowledgments......Page 278
1. Introduction......Page 280
2. Structure of Prokaryotic Regulons......Page 282
4. Representation of cis-Regulatory Elements......Page 284
5. Prediction of Regulons......Page 285
5.2. Regulon Prediction for a Known TF Through Comparative Genome Analyses......Page 286
5.3.2. Prediction of cis-Regulatory Elements and Possible σ-Factor
Binding Sites......Page 287
5.3.3. Prediction of Additional Members of a Regulon by Scanning the Target Genome......Page 288
5.3.4. An Application: Prediction of NtcA Regulon in Cyanobacteria......Page 291
5.4.1. Prediction of Orthologous Groups (OGs) in Closely Related Genomes......Page 293
5.4.4. An Application: Prediction of Regulons in Staphylococcus Aureus......Page 295
6. Discussion......Page 296
Acknowledgments......Page 300
1.1. Metabolic Pathways......Page 302
1.2. Regulatory Pathways......Page 303
1.3. Signaling Pathways......Page 304
2.2. Transcriptomic Data......Page 305
2.5.1. Protein–Protein Interactions......Page 306
2.5.2. Protein-DNA Interactions......Page 307
3.1. Functional Assignment of Genes......Page 308
3.2.2. Gene Fusion and Phylogenetic Pro.le Analysis......Page 309
3.3. Prediction of Functional Modules......Page 310
4. Mining Omics Data......Page 311
4.1.2. Identi.cation of Co-regulatory Genes through Data Clustering......Page 312
4.2.1. Prediction of Protein–Protein Interaction Clusters......Page 313
4.2.4. Analysis of Protein–Protein Interaction Networks......Page 314
5. Pathway Prediction through Pathway Mapping......Page 315
5.2. Pathway Construction by PathoLogic Based on Gene Annotation......Page 317
5.4. Pathway Construction Using SEED......Page 318
6. Pathway Inference through Information Fusion......Page 319
6.1. Identi.cation of Missing Reactions and Gene Functional Assignment......Page 320
6.3. Integration of Multiple Partial Pathway Models......Page 321
6.4.1. Prediction of the Nitrogen Assimilation in Synechococcus sp. WH8102......Page 322
7. Estimation of Parameters for Metabolic Models......Page 323
7.1. Forward or Bottom-Up Modeling......Page 325
7.3. Reverse Network Engineering......Page 326
7.4. Reducing the Complexity of the Inference Task......Page 328
7.5. Solving Differential Equations......Page 329
7.6. Efficient Algorithms for Determining Optimal Estimates......Page 330
8. Major Resources of Relevant Tools on the Internet......Page 331
9. Concluding Remarks......Page 333
10. Further Reading......Page 334
Acknowledgments......Page 335
1. Introduction......Page 336
2. Why Do We Need Models to Understand Pathway Systems?......Page 339
3. The Modeling Process......Page 341
4. Case Study: Sugar Metabolism in Lactococcus lactis......Page 342
5. Defining the Problem......Page 343
6. Developing Hypotheses Regarding Network Structure......Page 344
7. Choosing a Modeling Approach......Page 345
7.1. Stoichiometric Models......Page 346
7.3. Canonical Models......Page 348
7.3.1. Power-Law Approximation......Page 349
7.3.2. Lin-Log Approximation......Page 351
7.3.3. Summary: Canonical Models......Page 354
8. Information from Experimental Data......Page 355
9.1. Steady-State, Stability, and Sensitivity Analysis......Page 358
9.3. External Consistency......Page 359
9.4. Monte-Carlo Simulations......Page 360
10.1. Typical Uses......Page 361
10.3. Optimization......Page 363
11. Online Resources for Modeling......Page 364
Acknowledgments......Page 365
1.1. Introduction......Page 366
1.2. Definitions......Page 367
1.3. The Promises......Page 368
1.4. The Challenges......Page 369
3.1. The Predominate form of Life and Its Population and Community Characteristics......Page 370
3.2. The Complexity of Genomes and Biology......Page 371
4. Exploring Novel Cultivation and Growth Conditions to Extend Metagenomics......Page 372
5. Landmark Advances Toward Metagenomics......Page 373
6.1. High Population Complexity: Soil Metagenomics......Page 374
6.2. Low Population Complexity: The Acid Mine-Drainage (AMD) Project......Page 376
6.3. The Sargasso Sea Survey — An Assay of Marine Metagenomics......Page 377
6.4. Marine Metagenomic Horizontal Sampling: A Global Ocean Sampling (GOS) Project......Page 378
6.5. Marine Metagenomic Vertical Sampling: A Time-Dependent Alternative Sampling Strategy......Page 380
6.6. Marine Bacteriophages — Viral Metagenomics and Global Diversity......Page 381
6.7. Microbiomes: Microbiota within Biological Hosts and the Human Microbiome Project (HMP)......Page 382
6.8. The Higher Termite Hindgut (3rd Proctodeal Segment, P3) Microbiome......Page 391
7.1. 16S rRNA-PCR......Page 392
7.4. Environmental Shotgun Sequencing (ESS)......Page 393
8.1. Challenges in Sequencing Technologies......Page 394
8.3. Single-Cell Sequencing......Page 395
8.4.1. Probing Large or Genome Scale Expression in Metagenomic Context......Page 396
8.4.3. Functional Gene Array (FGA)......Page 397
9. Computational Challenges......Page 398
9.2. Comparative Metagenomics......Page 401
9.3. Binning ESS Data......Page 402
10. The Interconnection of Microbiology Research with Metagenomics......Page 404
11.1.2. Habitat Selection......Page 405
11.2. Study of Natural Habitats Constructed in the Laboratory......Page 406
12. Considerations on the Future Implications of Metagenomics......Page 407
13.3. ICoMM: International Census of Marine Microbes......Page 410
13.6. SEED: The Subsystems Approach to Genome Annotation......Page 411
14. Online Resources......Page 415
15. Further Reading......Page 416
Acknowledgments......Page 417
References......Page 418
Index......Page 488