Name
Abstract | ||
|---|---|---|
Phylogeography is a field that focuses on the geographical lineages of species such as vertebrates or viruses. Here, geographical data, such as location of a species or viral host is as important as the sequence information extracted from the species. Together, this information can help illustrate the migration of the species over time within a geographical area, the impact of geography over the evolutionary history, or the expected population of the species within the area. Molecular sequence data from NCBI, specifically GenBank, provide an abundance of available sequence data for phylogeography. However, geographical data is inconsistently represented and sparse across GenBank entries. This can impede analysis and in situations where the geographical information is inferred, and potentially lead to erroneous results. In this paper, we describe the current state of geographical data in GenBank, and illustrate how automated processing techniques such as named entity recognition, can enhance the geographical data available for phylogeographic studies. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.jbi.2011.06.005 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
nucleic acid,geographical information,automated processing technique,geographical area,bioinformatics,molecular sequence data,sequence information,genbank entry,databases,geographical lineage,available sequence data,geographic locations,enhancing phylogeography,phylogeography,current state,geographical data,phylogeny | Phylogeography,Population,Information retrieval,Computer science,Data sequences,Bioinformatics,Phylogenetics,Named-entity recognition,Evolutionary biology,GenBank | Journal |
Volume | Issue | ISSN |
44 Suppl 1 | S1 | 1532-0480 |
Citations | PageRank | References |
6 | 0.86 | 7 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Scotch | 1 | 123 | 11.56 |
| Indra Neil Sarkar | 2 | 233 | 37.20 |
| Changjiang Mei | 3 | 6 | 0.86 |
| Robert Leaman | 4 | 914 | 39.98 |
| Kei-hoi Cheung | 5 | 664 | 60.65 |
| Pierina Ortiz | 6 | 8 | 1.22 |
| Ashutosh Singraur | 7 | 6 | 0.86 |
| Graciela Gonzalez | 8 | 624 | 39.60 |