Muscle5: High-accuracy alignment ensembles enable unbiased assessments of sequence homology and phylogeny

Multiple sequence alignments are widely used to infer evolutionary relationships, enabling inferences of structure, function, and phylogeny. Applied to the phylogeny of RNA viruses, ensemble analysis shows that recently adopted taxonomic phyla are probably polyphyletic.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Standard practice is to construct one alignment by some preferred method and use it in further analysis; however, undetected alignment bias can be problematic.
• 2 I describe Muscle5, a novel algorithm which constructs an ensemble of high-accuracy alignment with diverse biases by perturbing a hidden Markov model and permuting its guide tree.
• 3 Confidence in an inference is assessed as the fraction of the ensemble which supports it.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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