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Python Phylo: Part of Biopython, this module provides classes, functions and I/O support for working with phylogenetic trees [38] Bio::Phylo Perl A collection of Perl modules for manipulating and visualizing phylogenetic data. Bio::Phylo is one part of a comprehensive suite of Perl biology tools [21] CGRphylo: R
A user with a full genome sequence of a sample of SARS-CoV-2 can use the tool to submit that sequence, which is then compared with other genome sequences, and assigned the most likely lineage (Pango lineage). [5] Single or multiple runs are possible, and the tool can return further information regarding the known history of the assigned lineage ...
Unix/Linux command line phylogenetic tools: Explore, manipulate, analyze, and simulate phylogenetic objects (alignments, trees, and MCMC logs) J.W. Brown, J.F. Walker, and S.A. Smith POY A phylogenetic analysis program that supports multiple kinds of data and can perform alignment and phylogeny inference.
Phylogenetic trees generated by computational phylogenetics can be either rooted or unrooted depending on the input data and the algorithm used. A rooted tree is a directed graph that explicitly identifies a most recent common ancestor (MRCA), [citation needed] usually an inputed sequence that is not represented in the input.
Dbt enables analytics engineers to transform data in their warehouses by writing select statements, and turns these select statements into tables and views. Dbt does the transformation (T) in extract, load, transform (ELT) processes – it does not extract or load data, but is designed to be performant at transforming data already inside of a ...
This makes it practical for analyzing large data sets (hundreds or thousands of taxa) and for bootstrapping, for which purposes other means of analysis (e.g. maximum parsimony, maximum likelihood) may be computationally prohibitive. Neighbor joining has the property that if the input distance matrix is correct, then the output tree will be correct.
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License. It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques". [1]
Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.