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ASTRID: Accurate Species TRees from Internode Distances.
BMC Genomics
|October 10, 2015
Summary
A new method, ASTRID, improves species tree estimation by addressing challenges from incomplete lineage sorting (ILS). It offers accuracy competitive with ASTRAL-2 but is significantly faster, making it suitable for large datasets.
Area of Science:
- Phylogenetics and evolutionary biology
- Computational biology and bioinformatics
Background:
- Incomplete lineage sorting (ILS) causes gene tree and species tree discordance, complicating accurate species tree estimation.
- Existing methods like ASTRAL-2 and NJst offer solutions, but NJst has limitations in speed and dataset compatibility.
Purpose of the Study:
- To redesign NJst for broader dataset applicability and improved performance.
- To develop a statistically consistent and accurate species tree estimation method under the multi-species coalescent (MSC) model.
Main Methods:
- Redesigned the NJst algorithm to enhance dataset compatibility.
- Expanded the design space to integrate with various distance-based tree estimation techniques.
- Developed ASTRID, a novel coalescent-based species tree estimation method.
Main Results:
- ASTRID demonstrates statistical consistency under the MSC model.
- Achieved accuracy competitive with ASTRAL-2 on large datasets.
- Significantly outperforms ASTRAL-2 in speed, reducing computation time from hours to minutes for some datasets.
Conclusions:
- ASTRID is a highly accurate and substantially faster alternative for species tree estimation.
- The new method addresses limitations of previous approaches, offering improved performance and broader applicability.
- ASTRID is available as open-source software.
