Supertree building software tools | Phylogenomics data analysis
Phylogenetic tree-building methods use molecular data to represent the evolutionary history of genes and taxa. A recurrent problem is to reconcile the various phylogenies built from different genomic sequences into a single one. This task is generally conducted by a two-step approach whereby a binary representation of the initial trees is first inferred and then a maximum parsimony (MP) analysis is performed on it. This binary representation uses a decomposition of all source trees that is usually based on clades, but that can also be based on triplets or quartets.
Allows users to reconstruct phylogenies for very large protein families. QuickTree is a program simplifying activities such as bootstrapping and the investigation of more sophisticated distance measures for the phylogenetic research community. It makes it feasible to construct trees for large databases of sequence alignments such as Pfam when only limited resources are available.
Constructs supertrees and explore the underlying phylogenomic information from partially overlapping datasets. Clann has been developed to provide implementations of several supertree methods. The methods implemented all allow the investigation of data in a phylogenomic context. There are four supertree methods implemented in Clann: Matrix Representation using Parsimony (MRP); Most Similar Supertree (MSSA); Maximum Quartet Fit (QFIT) and Maximum Splits Fit (SFIT). It is important for the user to know that the software is designed to perform a number of different tasks, however the interpretation of the results is left entirely to the user.
Aims to construct trees in an agglomerative way from a distance matrix representation of sequences. GIGA is an algorithm that assists users with phylogenetic reconstruction of large gene families and determination of orthologs on a large scale. This method makes use of a conceptualization of gene trees as being composed of orthologous subtrees which are joined by other evolutionary events such as gene duplication or horizontal gene transfer.
A method based on a dynamic programming method developed to find an exact solution to the Robinson-Foulds Supertree problem within a constrained search space. FastRFS has excellent accuracy in terms of criterion scores and topological accuracy of the resultant trees, substantially improving on competing methods on a large collection of biological and simulated data. In addition, FastRFS is extremely fast, finishing in minutes on even very large datasets, and in under an hour on a biological dataset with 2228 species.
Aims at inferring supertrees that satisfy the same appealing theoretical properties as with PhySIC, while being as informative as possible under this constraint. The informativeness of a supertree is estimated using a variation of the CIC (cladistic information content) criterion, that takes into account both the presence of multifurcations and the absence of some taxa.
A python framework which construct split-based supertrees with the computation of three majority-rule (MR)supertree variants and input trees (MR(-), MR(+) and MR(+)g). PluMiST searches tree space by NNI (nearest-neighbor interchange) and TDR (taxa-deletion-reinsertion). Only fully resolved input and supertrees are considered, multifurcating trees may be returned as the strict consensus of equally best scoring trees.
Enables the user to quickly summarize consensual information of a set of trees and localize groups of taxa for which the data require consolidation. For k input trees spanning a set of n taxa, this method produces a supertree that satisfies the above-mentioned properties in O(kn3 + n4) computing time. The polytomies of the produced supertree are also tagged by labels indicating areas of conflict as well as those with insufficient overlap.