PhyloPars statistics

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Citations per year

Number of citations per year for the bioinformatics software tool PhyloPars
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Tool usage distribution map

This map represents all the scientific publications referring to PhyloPars per scientific context
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Protocols

PhyloPars specifications

Information


Unique identifier OMICS_13168
Name PhyloPars
Interface Web user interface
Restrictions to use None
Input data Phylogeny file
Computer skills Basic
Stability Stable
Maintained Yes

Documentation


Maintainer


  • person_outline Jorn Bruggerman

Publication for PhyloPars

PhyloPars citations

 (3)
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The Most Developmentally Truncated Fishes Show Extensive Hox Gene Loss and Miniaturized Genomes

2018
Genome Biol Evol
PMCID: 5906920
PMID: 29684203
DOI: 10.1093/gbe/evy058
call_split See protocol

[…] the directionality of changes in repeat content and genome sizes, the time-calibrated phylogeny () was used to reconstruct ancestral states at each internal node. This was done with the ML algorithm “phylopars” implemented in the R package Rphylopars (), assuming a Brownian Motion model of trait evolution. Ancestral state reconstruction was performed separately for numbers of DNA transposons, LINE […]

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Rapid maximum likelihood ancestral state reconstruction of continuous characters: A rerooting‐free algorithm

2017
Ecol Evol
PMCID: 5395464
PMID: 28428869
DOI: 10.1002/ece3.2837

[…] rsively traversing the tree in preorder (root to tips) to compute quantities of interest at each internal node. The algorithm is implemented in the R package Rphylopars in the functions anc.recon and phylopars (Goolsby, Bruggeman, & Ané, ). […]

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Predicting climate change effects on wetland ecosystem services using species distribution modeling and plant functional traits

2015
PMCID: 4288999
PMID: 25576286
DOI: 10.1007/s13280-014-0593-9
call_split See protocol

[…] e LEDA traitbase (Kleyer et al. ; Electronic Supplementary Material, Table S3). Gaps in trait data were filled with estimates based on phylogenetic relationships (generated with phyloGenerator) using PhyloPars (Bruggeman et al. ). This approach resulted in complete datasets for the continuous traits: specific leaf area (SLA), canopy height (CH), and root depth (RD) (Table ). SLA is a good positive […]


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PhyloPars institution(s)
Department of Theoretical Biology, Faculty of Earth & Life Sciences, Amsterdam, Netherlands; Centre for Integrative Bioinformatics, VU University Amsterdam, Amsterdam, Netherlands
PhyloPars funding source(s)
This work was supported by the Computational Life Sciences program of the Netherlands, Organisation for Scientific Research (635.100.009) and by the European Commission FP6 Programme.

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