| Chapter title |
Estimating Fitness of Viral Quasispecies from Next-Generation Sequencing Data
|
|---|---|
| Chapter number | 462 |
| Book title |
Quasispecies: From Theory to Experimental Systems
|
| Published in |
Current topics in microbiology and immunology, January 2015
|
| DOI | 10.1007/82_2015_462 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-923897-5, 978-3-31-923898-2
|
| Authors |
David Seifert, Niko Beerenwinkel, Seifert, David, Beerenwinkel, Niko |
| Abstract |
The quasispecies model is ubiquitous in the study of viruses. While having lead to a number of insights that have stood the test of time, the quasispecies model has mostly been discussed in a theoretical fashion with little support of data. With next-generation sequencing (NGS), this situation is changing and a wealth of data can now be produced in a time- and cost-efficient manner. NGS can, after removal of technical errors, yield an exceedingly detailed picture of the viral population structure. The widespread availability of cross-sectional data can be used to study fitness landscapes of viral populations in the quasispecies model. This chapter highlights methods that estimate the strength of selection in selective sweeps, assesses marginal fitness effects of quasispecies, and finally infers the fitness landscape of a viral quasispecies, all on the basis of NGS data. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 18% |
| Professor > Associate Professor | 6 | 18% |
| Researcher | 6 | 18% |
| Student > Ph. D. Student | 5 | 15% |
| Student > Doctoral Student | 3 | 9% |
| Other | 3 | 9% |
| Unknown | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 13 | 39% |
| Agricultural and Biological Sciences | 5 | 15% |
| Computer Science | 2 | 6% |
| Immunology and Microbiology | 2 | 6% |
| Business, Management and Accounting | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 7 | 21% |