| Title |
PhySortR: a fast, flexible tool for sorting phylogenetic trees in R
|
|---|---|
| Published in |
PeerJ, May 2016
|
| DOI | 10.7717/peerj.2038 |
| Pubmed ID | |
| Authors | |
| Abstract |
A frequent bottleneck in interpreting phylogenomic output is the need to screen often thousands of trees for features of interest, particularly robust clades of specific taxa, as evidence of monophyletic relationship and/or reticulated evolution. Here we present PhySortR, a fast, flexible R package for classifying phylogenetic trees. Unlike existing utilities, PhySortR allows for identification of both exclusive and non-exclusive clades uniting the target taxa based on tip labels (i.e., leaves) on a tree, with customisable options to assess clades within the context of the whole tree. Using simulated and empirical datasets, we demonstrate the potential and scalability of PhySortR in analysis of thousands of phylogenetic trees without a priori assumption of tree-rooting, and in yielding readily interpretable trees that unambiguously satisfy the query. PhySortR is a command-line tool that is freely available and easily automatable. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 14 | 28% |
| Australia | 6 | 12% |
| Germany | 3 | 6% |
| Ireland | 2 | 4% |
| United Kingdom | 2 | 4% |
| Finland | 1 | 2% |
| India | 1 | 2% |
| Italy | 1 | 2% |
| Trinidad and Tobago | 1 | 2% |
| Other | 5 | 10% |
| Unknown | 14 | 28% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 34 | 68% |
| Members of the public | 14 | 28% |
| Science communicators (journalists, bloggers, editors) | 2 | 4% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 3% |
| Czechia | 2 | 3% |
| United States | 1 | 1% |
| Sweden | 1 | 1% |
| France | 1 | 1% |
| Germany | 1 | 1% |
| Canada | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 60 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 23 | 33% |
| Student > Ph. D. Student | 16 | 23% |
| Student > Bachelor | 6 | 9% |
| Student > Master | 5 | 7% |
| Professor > Associate Professor | 3 | 4% |
| Other | 5 | 7% |
| Unknown | 12 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 37 | 53% |
| Environmental Science | 5 | 7% |
| Biochemistry, Genetics and Molecular Biology | 5 | 7% |
| Engineering | 3 | 4% |
| Computer Science | 2 | 3% |
| Other | 3 | 4% |
| Unknown | 15 | 21% |