| Chapter title |
Reconstructing the Ancestral Relationships Between Bacterial Pathogen Genomes.
|
|---|---|
| Chapter number | 8 |
| Book title |
Bacterial Pathogenesis
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-6673-8_8 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6671-4, 978-1-4939-6673-8
|
| Authors |
Caitlin Collins, Xavier Didelot, Collins, Caitlin, Didelot, Xavier |
| Editors |
Pontus Nordenfelt, Mattias Collin |
| Abstract |
Following recent developments in DNA sequencing technology, it is now possible to sequence hundreds of whole genomes from bacterial isolates at relatively low cost. Analyzing this growing wealth of genomic data in terms of ancestral relationships can reveal many interesting aspects of the evolution, ecology, and epidemiology of bacterial pathogens. However, reconstructing the ancestry of a sample of bacteria remains challenging, especially for the majority of species where recombination is frequent. Here, we review and describe the computational techniques currently available to infer ancestral relationships, including phylogenetic methods that either ignore or account for the effect of recombination, as well as model-based and model-free phylogeny-independent approaches. |
X Demographics
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| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 33% |
| France | 2 | 33% |
| Australia | 1 | 17% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 83% |
| Members of the public | 1 | 17% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 43% |
| Student > Ph. D. Student | 3 | 13% |
| Researcher | 2 | 9% |
| Other | 2 | 9% |
| Lecturer | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 2 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 35% |
| Biochemistry, Genetics and Molecular Biology | 7 | 30% |
| Computer Science | 2 | 9% |
| Linguistics | 1 | 4% |
| Immunology and Microbiology | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 3 | 13% |