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Statistics for bioinformatics : methods for multiple sequence alignment /
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 b...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
ISTE Press : Elsevier,
2016.
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| Colección: | Statistics for bioinformatics set.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Thompson, Julie D., |e author. | |
| 245 | 1 | 0 | |a Statistics for bioinformatics : |b methods for multiple sequence alignment / |c Julie Dawn Thompson. |
| 264 | 1 | |a London : |b ISTE Press : |b Elsevier, |c 2016. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Statistics for bioinformatics set | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed February 23, 2017). | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a Front Cover -- Statistics for Bioinformatics: Methods for Multiple Sequence Alignment -- Copyright -- Contents -- Preface -- PART 1 Fundamental Concepts -- 1 Introduction -- 1.1. Biological sequences: DNA/RNA/proteins -- 1.2. From DNA to RNA and proteins -- 1.3. RNA sequence, structure and function -- 1.4. Protein sequence, structure and function -- 1.5. Sequence evolution -- 1.6. MSA: basic concepts -- 1.7. Multiple sequence alignment applications -- PART 2 Traditional Multiple Sequence Alignment Methods -- 2 Heuristic Sequence Alignment Methods -- 2.1. Optimal sequence alignment -- 2.2. Progressive multiple alignment -- 2.3. Iterative alignment -- 2.4. Consistency-based alignment -- 2.5. Cooperative alignment strategies -- 3 Statistical Alignment Approaches -- 3.1. Probabilistic models of sequence evolution -- 3.2. Profile HMM-based alignment -- 3.3. Simulated annealing -- 3.4. Genetic algorithms -- 4 Multiple Alignment Quality Control -- 4.1. Objective scoring functions -- 4.2. Determination of reliable regions -- 4.3. Estimation of homology -- 5 Benchmarking -- 5.1. Criteria for benchmark construction -- 5.2. Multiple alignment benchmarks -- 5.3. Comparison of multiple alignment benchmarks -- PART 3 Large-scale Multiple Sequence Alignment Methods -- 6 Whole Genome Alignment -- 6.1. Pairwise genome alignment -- 6.2. Progressive methods for multiple genome alignment -- 6.3. Graph-based methods for multiple genome alignment -- 6.4. Meta-aligners for multiple genome alignment -- 6.5. Accuracy measures for genome alignment methods -- 6.6. Benchmarking genome alignment -- 7 Multiple Alignment of Thousands of Sequences -- 7.1. Extension of the progressive alignment approach -- 7.2. Meta-aligners for large numbers of sequences -- 7.3. Extending "seed" alignments -- 7.4. Benchmarking large numbers of sequences. | |
| 505 | 8 | |a 8 Future Perspectives: High-Performance Computing -- 8.1. Coarse-grain parallelism: grid computing -- 8.2. Fine-grain parallelism: GPGPU -- 8.3. MSA in the cloud -- Bibliography -- Index -- Back Cover. | |
| 650 | 0 | |a Bioinformatics |x Statistical methods. | |
| 650 | 0 | |a Sequence alignment (Bioinformatics) | |
| 650 | 1 | 2 | |a Computational Biology |0 (DNLM)D019295 |
| 650 | 1 | 2 | |a Sequence Alignment |0 (DNLM)D016415 |
| 650 | 6 | |a Bio-informatique |0 (CaQQLa)201-0313075 |x M�ethodes statistiques. |0 (CaQQLa)201-0373903 | |
| 650 | 6 | |a Bio-informatique. |0 (CaQQLa)201-0313075 | |
| 650 | 6 | |a Homologie de s�equence (Bio-informatique) |0 (CaQQLa)000265156 | |
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| 650 | 7 | |a SCIENCE |x Life Sciences |x General. |2 bisacsh | |
| 650 | 7 | |a Sequence alignment (Bioinformatics) |2 fast |0 (OCoLC)fst01747147 | |
| 655 | 0 | |a Electronic books. | |
| 655 | 4 | |a Internet Resources. | |
| 776 | 0 | 8 | |i Print version: |z 9780081019610 |
| 830 | 0 | |a Statistics for bioinformatics set. | |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9781785482168 |z Texto completo |