Title |
De novo assembly of highly diverse viral populations
|
---|---|
Published in |
BMC Genomics, September 2012
|
DOI | 10.1186/1471-2164-13-475 |
Pubmed ID | |
Authors |
Xiao Yang, Patrick Charlebois, Sante Gnerre, Matthew G Coole, Niall J Lennon, Joshua Z Levin, James Qu, Elizabeth M Ryan, Michael C Zody, Matthew R Henn |
Abstract |
Extensive genetic diversity in viral populations within infected hosts and the divergence of variants from existing reference genomes impede the analysis of deep viral sequencing data. A de novo population consensus assembly is valuable both as a single linear representation of the population and as a backbone on which intra-host variants can be accurately mapped. The availability of consensus assemblies and robustly mapped variants are crucial to the genetic study of viral disease progression, transmission dynamics, and viral evolution. Existing de novo assembly techniques fail to robustly assemble ultra-deep sequence data from genetically heterogeneous populations such as viruses into full-length genomes due to the presence of extensive genetic variability, contaminants, and variable sequence coverage. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 23% |
Switzerland | 1 | 8% |
United Kingdom | 1 | 8% |
Chile | 1 | 8% |
Unknown | 7 | 54% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 8 | 62% |
Members of the public | 5 | 38% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 5 | 2% |
United States | 5 | 2% |
United Kingdom | 5 | 2% |
Germany | 4 | 1% |
Italy | 2 | <1% |
Canada | 2 | <1% |
Belgium | 2 | <1% |
France | 1 | <1% |
Portugal | 1 | <1% |
Other | 7 | 3% |
Unknown | 234 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 74 | 28% |
Researcher | 65 | 24% |
Student > Master | 35 | 13% |
Student > Bachelor | 22 | 8% |
Student > Doctoral Student | 12 | 4% |
Other | 38 | 14% |
Unknown | 22 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 151 | 56% |
Biochemistry, Genetics and Molecular Biology | 46 | 17% |
Computer Science | 13 | 5% |
Immunology and Microbiology | 7 | 3% |
Medicine and Dentistry | 6 | 2% |
Other | 18 | 7% |
Unknown | 27 | 10% |