Statistics for Bioinformatics: Methods for Multiple Sequence Alignment provides an in-depth introduction to the most widely used methods and software in the bioinformatics field. With the ever increasing flood of sequence information from genome sequencing projects, multiple sequence alignment has become one of the cornerstones of bioinformatics. Multiple sequence alignments are crucial for genome annotation, as well as the subsequent structural, functional, and evolutionary studies of genes and gene products. Consequently, there has been renewed interest in the development of novel multiple sequence alignment algorithms and more efficient programs.
1. 1: Introduction - p. 3 2. Part 2: Traditional Multiple Sequence Alignment Methods - p. 25 3. Introduction - p. 27 4. 2: Heuristic Sequence Alignment Methods - p. 29 5. 3: Statistical Alignment Approaches - p. 43 6. 4: Multiple Alignment Quality Control - p. 53 7. 5: Benchmarking - p. 63 8. Part 3: Large-scale Multiple Sequence Alignment Methods - p. 72 9. Introduction - p. 73 10. 6: Whole Genome Alignment - p. 75 11. 7: Multiple Alignment of Thousands of Sequences - p. 87 12. 8: Future Perspectives: High-Performance Computing - p. 97 13. Bibliography - p. 109 14. Index - p. 133
Statistics for Bioinformatics: Methods for Multiple Sequence Alignment provides an in-depth introduction to the most widely used methods and software in the bioinformatics field. With the ever increasing flood of sequence information from genome sequencing projects, multiple sequence alignment has become one of the cornerstones of bioinformatics. Multiple sequence alignments are crucial for genome annotation, as well as the subsequent structural, functional, and evolutionary studies of genes and gene products. Consequently, there has been renewed interest in the development of novel multiple sequence alignment algorithms and more efficient programs.
1. 1: Introduction - p. 3 2. Part 2: Traditional Multiple Sequence Alignment Methods - p. 25 3. Introduction - p. 27 4. 2: Heuristic Sequence Alignment Methods - p. 29 5. 3: Statistical Alignment Approaches - p. 43 6. 4: Multiple Alignment Quality Control - p. 53 7. 5: Benchmarking - p. 63 8. Part 3: Large-scale Multiple Sequence Alignment Methods - p. 72 9. Introduction - p. 73 10. 6: Whole Genome Alignment - p. 75 11. 7: Multiple Alignment of Thousands of Sequences - p. 87 12. 8: Future Perspectives: High-Performance Computing - p. 97 13. Bibliography - p. 109 14. Index - p. 133
