Multiple sequence alignment: a major challenge to large-scale phylogenetics.

Area of Science:

• Computational Biology
• Phylogenetics
• Bioinformatics

Background:

• Advances in computational power enable large-scale phylogenetic analyses.
• Automated multiple sequence alignment methods have improved accuracy.
• Current alignment methods are not adequately tested on very large datasets.

Purpose of the Study:

• To compare the performance of multiple sequence alignment methods on large nucleotide datasets.
• To evaluate the impact of alignment accuracy on phylogenetic inference for large datasets.

Main Methods:

• Utilized a benchmark collection of nucleotide sequence datasets (up to 78,132 sequences).
• Assessed various automated multiple sequence alignment methods.
• Computed maximum likelihood phylogenies using the generated alignments.

Main Results:

• Fewer alignment methods can process larger datasets as sequence count increases.
• The most accurate alignment methods fail on the largest datasets, necessitating moderately accurate ones.
• Large-dataset alignments exhibit significant error rates, leading to high error rates in subsequent phylogenetic estimations.

Conclusions:

• Accurate multiple sequence alignment is a critical bottleneck for large-scale phylogenetic projects like Tree of Life.
• Current alignment tools face scalability challenges, impacting the reliability of large-scale evolutionary studies.
• Further development of scalable and accurate alignment methods is essential for advancing systematics.