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SCAMPP+FastTree: improving scalability for likelihood-based phylogenetic placement
1Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Bioinformatics Advances
|February 23, 2023
Summary
We improved phylogenetic placement by combining SCAMPP and FastTree with pplacer. This new method, pplacer-SCAMPP-FastTree, scales to larger datasets and offers better accuracy than previous approaches.
Area of Science:
- Computational Biology
- Bioinformatics
- Phylogenetics
Background:
- Phylogenetic placement is crucial for understanding evolutionary relationships.
- Current methods like pplacer, using RAxML, face scalability limitations with large datasets.
- Existing scalable methods include APPLES-2 and pplacer-SCAMPP.
Purpose of the Study:
- To enhance the scalability and accuracy of phylogenetic placement methods.
- To investigate the impact of different parameter estimation techniques on pplacer's performance.
- To evaluate a novel combination of SCAMPP and FastTree for phylogenetic placement.
Main Methods:
- Utilized pplacer, a maximum likelihood-based phylogenetic placement tool.
- Investigated the use of FastTree as an alternative to RAxML for parameter estimation.
- Developed and evaluated the pplacer-SCAMPP-FastTree approach, combining SCAMPP with FastTree.
Main Results:
- Using FastTree with pplacer significantly improves scalability for large backbone trees.
- The combined pplacer-SCAMPP-FastTree method achieves scalability comparable to APPLES-2.
- pplacer-SCAMPP-FastTree demonstrates improved accuracy over similarly scalable methods.
Conclusions:
- The combination of SCAMPP and FastTree offers a highly scalable and accurate solution for phylogenetic placement.
- This approach overcomes the limitations of traditional pplacer implementations on large datasets.
- pplacer-SCAMPP-FastTree represents a significant advancement in phylogenetic analysis tools.
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