TreefixDTL statistics

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TreefixDTL specifications


Unique identifier OMICS_31847
Name TreefixDTL
Alternative name Treefix-DTL
Software type Application/Script
Interface Command line interface
Restrictions to use None
Input data A single maximum likelihood (ML) gene tree.
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Version 1.0.2
Stability Stable
Numpy, Scipy
Maintained Yes



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  • person_outline Mukul S. Bansal <>
  • person_outline Manolis Kellis <>

Additional information

Publication for TreefixDTL

TreefixDTL in publications

PMCID: 4571573
PMID: 26323765
DOI: 10.1098/rstb.2014.0335

[…] be slow, which motivated other approaches based on the consideration of a set of candidate gene trees obtained using faster approaches that do not consider a species tree. these approaches include treefixdtl [], ale [,] and tera []. the latter two approaches are extensions of an idea initially proposed in [] and formalized in [] and are particularly fast and accurate. they are based […]

PMCID: 4393519
PMID: 25481006
DOI: 10.1093/bioinformatics/btu806

[…] and demonstrate the large impact of gene tree accuracy on downstream evolutionary analyses., availability and implementation: an implementation of our method is available at, contact: [email protected] or [email protected], supplementary information: supplementary data are available at bioinformatics online., gene trees and species trees are the two […]

PMCID: 4380024
PMID: 25380957
DOI: 10.1093/bioinformatics/btu728

[…] aware methods, including tera, we used the dtl costs δ = 2, τ = 3, λ = 1 obtained by using a criteria based on minimizing the change in ancestral genome sizes on a large biological dataset. we ran treefix-dtl with default parameters, jtt/gtr with a gamma distribution as models of evolution, and as a starting tree the phyml tree. mowglinni was run with default parameters, a threshold of 50 […]

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TreefixDTL institution(s)
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Cambridge, MA, USA; Broad Institute, Cambridge, MA, USA
TreefixDTL funding source(s)
Supported by a National Science Foundation [CAREER award 0644282], National Institutes of Health [RC2 HG005639], National Science Foundation AToL [0936234], and startup funds from the University of Connecticut.

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