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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

SUMAC: Constructing Phylogenetic Supermatrices and Assessing Partially Decisive Taxon Coverage.

William A Freyman¹

¹Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA.

Evolutionary Bioinformatics Online

|December 10, 2015

Summary

This summary is machine-generated.

Supermatrix Constructor (SUMAC) is a new tool for building phylogenetic matrices from GenBank data. It assesses missing data impact using Missing Sequence Decisiveness Scores (MSDS) to guide data acquisition.

Keywords:

GenBank data-mining decisiveness partial taxon coverage phylogenetics supermatrix

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A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Area of Science:

Bioinformatics
Computational Biology
Phylogenetics

Background:

GenBank contains rapidly growing phylogenetic sequence data.
Large phylogenetic trees are crucial for modern research.
Existing tools lack efficient methods for constructing and evaluating phylogenetic matrices with missing data.

Purpose of the Study:

To introduce Supermatrix Constructor (SUMAC), a novel tool for data-mining GenBank.
To construct phylogenetic supermatrices and evaluate the impact of missing data.
To introduce a new metric, Missing Sequence Decisiveness Scores (MSDS), for assessing matrix decisiveness.

Main Methods:

SUMAC data-mines GenBank to construct phylogenetic supermatrices.
It employs exploratory clustering or guide sequences for homologous cluster building.
SUMAC calculates MSDS to quantify the contribution of missing sequences to matrix decisiveness.

Main Results:

SUMAC effectively constructs supermatrices for any taxonomic group in GenBank.
The tool quantifies the impact of missing data using MSDS.
MSDS aids in comparing supermatrices and prioritizing new sequence data acquisition.

Conclusions:

SUMAC provides an efficient solution for constructing and evaluating phylogenetic supermatrices.
The MSDS metric offers valuable insights for improving phylogenetic analyses.
SUMAC is an open-source Python package optimized for multicore systems, enhancing accessibility and usability.