| Chapter title |
Diversity Analysis in Viral Metagenomes
|
|---|---|
| Chapter number | 15 |
| Book title |
The Human Virome
|
| Published in |
Methods in molecular biology, August 2018
|
| DOI | 10.1007/978-1-4939-8682-8_15 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-8681-1, 978-1-4939-8682-8
|
| Authors |
Jorge Francisco Vázquez-Castellanos, Vázquez-Castellanos, Jorge Francisco |
| Abstract |
Viruses are the most abundant and diverse biological entity in the earth. Nowadays, there are several viral metagenomes from different ecological niches which have been used to characterize new viral particles and to determine their diversity. However, viral metagenomic data have the disadvantage to be high-dimensional compositional and sparse. This type of data renders many of the conventional multivariate statistical analyses inoperative. Fortunately, different libraries and statistical packages have been developed to deal with this problem and perform the different ecological and statistical analyses. In the present chapter, it is analyzed simulated viral metagenomes, based on real human gut-associated viral metagenomes, using different R and python packages. The example presented here includes the estimation and comparison of different indexes of diversity, evenness, and richness; perform different ordination and statistical analysis using different dissimilarity metrics; determine the optimal cluster configuration and perform biomarker discovery. The scripts and the simulated datasets are in https://github.com/jorgevazcast/Viromic-diversity. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 19% |
| Student > Master | 3 | 12% |
| Student > Bachelor | 3 | 12% |
| Lecturer | 2 | 8% |
| Student > Doctoral Student | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 9 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 27% |
| Agricultural and Biological Sciences | 4 | 15% |
| Immunology and Microbiology | 2 | 8% |
| Computer Science | 1 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 9 | 35% |