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Prediction of missing sequences and branch lengths in phylogenomic data
Diego Darriba1, Michael Weiß2, Alexandros Stamatakis3
1Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, Heidelberg 69118, Germany.
Bioinformatics (Oxford, England)
|January 7, 2016
Summary
Predicting missing sequence data in phylogenomics improves branch length accuracy. This method enhances phylogenetic inference by statistically imputing missing sequences, leading to more reliable evolutionary trees.
Area of Science:
- Phylogenomics
- Bioinformatics
- Computational Biology
Background:
- Large-scale phylogenomic datasets often contain missing data, which can negatively impact phylogenetic inference.
- Missing data, particularly in per-gene or per-partition sequences, can lead to excessively long branch lengths in inferred phylogenies.
- This phenomenon can distort evolutionary interpretations and reduce the accuracy of phylogenetic analyses.
Purpose of the Study:
- To investigate whether statistically predicting missing sequences can alleviate problems caused by missing data in phylogenomic datasets.
- To determine if sequence imputation improves the accuracy of phylogenetic inference, specifically addressing long branch lengths.
- To evaluate the effectiveness of novel algorithms for correcting long branch lengths and imputing missing sequence data.
Main Methods:
- Development and implementation of algorithms for correcting excessively long branch lengths.
- Development and implementation of methods for predicting/imputing missing sequence data.
- Systematic evaluation using three empirical and 100 simulated alignments with data removal, followed by Maximum Likelihood tree inference and comparison to complete datasets.
Main Results:
- The datasets with predicted sequences exhibited one to two orders of magnitude more accurate branch lengths compared to those with missing data.
- Sequence prediction significantly improved the accuracy of branch length estimations in phylogenetic trees.
- While branch lengths were improved, sequence prediction did not significantly affect the Robinson-Foulds (RF) distances between the inferred trees.
Conclusions:
- Statistical prediction and imputation of missing sequence data is an effective strategy to improve branch length accuracy in phylogenomic analyses.
- The developed algorithms offer a viable solution for mitigating the detrimental effects of missing data on phylogenetic inference.
- Further research may explore the impact of imputation on other phylogenetic metrics beyond RF distance.
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