Computational methods for understanding bacterial and archaeal genomes /
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Imprint: | London : Imperial College Press ; Hackensack, NJ : Distributed by World Scientific Publishing, ©2008. |
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Description: | 1 online resource (xix, 473 pages) : illustrations (some color) |
Language: | English |
Series: | Series on advances in bioinformatics and computational biology, 1751-6404 ; v. 7 Series on advances in bioinformatics and computational biology ; v. 7. |
Subject: | |
Format: | E-Resource Book |
URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11173789 |
Table of Contents:
- Preface; CONTENTS; List of Contributors; Acknowledgments; 1. General Characteristics of Prokaryotic Genomes Jan Mr ́azek and Anne O. Summers; 1. Introduction; 1.1. The Replicon Concept and Classification of Replicons; 1.2. Physical Organization of Replicons in the Cell; 2. Overall Properties of Prokaryotic Chromosomes; 2.1. Size and Gene Content; 2.2. Why Are Prokaryotic Chromosomes Small?; 2.3. G+C Content; 2.4. Oligonucleotide Composition and Genome Signature; 2.5. Amino Acid Composition and Adaptation to Growth at High Temperatures; 3. Heterogeneity of Prokaryotic Chromosomes.
- 3.1. Intrachromosomal Variance of Nucleotide and Oligonucleotide Composition3.2. Synonymous Codon Usage; 3.3. Identification of Genomic Islands and Lateral Gene Transfer Events; 3.4. G-C Skew; 4. Repeats in Prokaryotic Genomes; 4.1. Large Repeats and Duplications; 4.2. Transposons and Insertion Sequences; 4.3. Integrons; 4.4. Chimeric Mobile Elements: Conjugative Transposons, ICEs, Plasmid-Prophages, Transposon-Prophages, Genomic Islands, and Genetic Litter; 4.5. Retrons; 4.6. Short Dispersed Repeats; 4.7. Simple Sequence Repeats; 4.8. CRISPR Repeats; 5. Further Reading; Acknowledgments.
- 2. Genes in Prokaryotic Genomes and Their Computational Prediction Rajeev K. Azad1. Introduction; 2. Inhomogeneous Markov Models; 2.1. The GeneMark Program; 3. Interpolated Markov Models; 3.1. The Glimmer Program; 3.2. Using Deleted Interpolation in Gene Prediction; 4. Hidden Markov Models; 4.1. The Forward-Backward Algorithm; 4.2. The Viterbi Algorithm; 4.3. HMM Training; 4.4. The ECOPARSE Program; 4.5. The GeneHacker Program; 4.6. HMM Versions of the GeneMark Program; 5. Fourier Transform Methods; 5.1. The GeneScan Program; 5.2. The Lengthen-Shu.e Program; 6. Self-Organizing Maps.
- 6.1. The RescueNet Program7. Directed Acyclic Graphs; 7.1. The FrameD Program; 8. Linear Discriminant Function; 8.1. The ZCURVE Program; 9. Unsupervised Model Training: The Self-Learning Algorithms; 9.1. The GeneMark-Genesis Program; 9.2. The GeneMarkS Program; 9.3. The MED Program; 10. Using Similarity Search in Gene Prediction; 10.1. The ORPHEUS Program; 10.2. The CRITICA Program; 10.3. The BDGF Program; 10.4. The EasyGene Program; 10.5. The GISMO Program; 11. Gene Start Prediction; 12. Resolving Overlapping Genes; 13. Non-coding RNA Gene Prediction; 14. Assessing Gene Prediction Programs.
- 15. Discussion16. Further Reading; Acknowledgments; 3. Evolution of the Genetic Code: Computational Methods and Inferences Greg Fournier; 1. Introduction; 1.1. The Amino Acids; 1.2. Codon Designations; 1.3. Transfer RNA; 1.4. Aminoacyl-tRNA Synthetases; 2. Major Methods and Algorithms: Variations of the Genetic Code; 2.1. Non-canonical Codes; 2.2. Selenocysteine; 2.3. Pyrrolysine; 2.4. The Sep System; 2.5. Asparagine and Glutamine; 2.6. Evolutionary Considerations; 2.7. Nanoarchaeal tRNA; 3. Major Methods and Algorithms: Models of Genetic Code Evolution; 3.1. Overview.