Weighted Statistical Binning statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.

Subscribe
info

Citations per year

Citations chart
info

Popular tool citations

chevron_left Phylogenetic inference Ancestral genome reconstruction chevron_right
Popular tools chart
info

Tool usage distribution map

Tool usage distribution map
info

Associated diseases

Associated diseases

Weighted Statistical Binning specifications

Information


Unique identifier OMICS_25342
Name Weighted Statistical Binning
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Java, Python
Computer skills Advanced
Stability Stable
Requirements
Dendropy
Maintained Yes

Download


Versioning


Add your version

Maintainer


  • person_outline Tandy Warnow <>

Publication for Weighted Statistical Binning

Weighted Statistical Binning in publications

 (2)
PMCID: 4971187
PMID: 27481787
DOI: 10.1098/rstb.2015.0335

[…] and supertree methods that try to improve the noise/signal ratio for each supergene without assuming that gene and species trees are topologically equivalent. the latest of these methods—weighted statistical binning []—has shown interesting improvements on the accuracy of different species tree reconstruction methods., full probabilistic species tree reconstruction methods stand […]

PMCID: 4779606
PMID: 26733575
DOI: 10.1093/gbe/evv261

[…] tree for each bin. astral version 4.7.6 was run on both sets of inputs: the 11,169 unbinned gene trees, and the 2,513 supergene trees, weighting each supergene tree by size of the corresponding bin (weighted statistical binning; ; ). to test for the number of gene trees that supported each hypothesis with support above 50% or 75% threshold, we first restricted each gene tree to branches […]


To access a full list of publications, you will need to upgrade to our premium service.

Weighted Statistical Binning institution(s)
Department of Computer Science, University of Texas at Austin, Austin, TX, USA; Laboratoire de Biométrie et Biologie Évolutive, Université de Lyon, France; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Weighted Statistical Binning funding source(s)
Supported by National Science Foundation (NSF) grant numbers 1461364, 0733029, and 1062335; and by Howard Hughes Medical Institutes (HHMI) graduate international student fellowship.

Weighted Statistical Binning reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review Weighted Statistical Binning