| Title |
Live neighbor-joining
|
|---|---|
| Published in |
BMC Bioinformatics, May 2018
|
| DOI | 10.1186/s12859-018-2162-x |
| Pubmed ID | |
| Authors |
Guilherme P. Telles, Graziela S. Araújo, Maria E. M. T. Walter, Marcelo M. Brigido, Nalvo F. Almeida |
| Abstract |
In phylogenetic reconstruction the result is a tree where all taxa are leaves and internal nodes are hypothetical ancestors. In a live phylogeny, both ancestral and living taxa may coexist, leading to a tree where internal nodes may be living taxa. The well-known Neighbor-Joining heuristic is largely used for phylogenetic reconstruction. We present Live Neighbor-Joining, a heuristic for building a live phylogeny. We have investigated Live Neighbor-Joining on datasets of viral genomes, a plausible scenario for its application, which allowed the construction of alternative hypothesis for the relationships among virus that embrace both ancestral and descending taxa. We also applied Live Neighbor-Joining on a set of bacterial genomes and to sets of images and texts. Non-biological data may be better explored visually when their relationship in terms of content similarity is represented by means of a phylogeny. Our experiments have shown interesting alternative phylogenetic hypothesis for RNA virus genomes, bacterial genomes and alternative relationships among images and texts, illustrating a wide range of scenarios where Live Neighbor-Joining may be used. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 6 | 18% |
| Student > Bachelor | 5 | 15% |
| Student > Master | 3 | 9% |
| Student > Ph. D. Student | 3 | 9% |
| Professor > Associate Professor | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 10 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 39% |
| Biochemistry, Genetics and Molecular Biology | 5 | 15% |
| Computer Science | 2 | 6% |
| Arts and Humanities | 1 | 3% |
| Mathematics | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 May 2018.
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#20,512,427
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Outputs of similar age from BMC Bioinformatics
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