ArticleSystematic biology2021
The Cumulative Indel Model: Fast and Accurate Statistical Evolutionary Alignment.
Article in Systematic biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Nested birth-death processes are competitive with parameter-heavy neural networks as time-dependent models of protein evolution.bioRxiv : the preprint server for biology · 2026Article
- Protein Structural Phylogenetics.Genome biology and evolution · 2025Review
- Algorithms to reconstruct past indels: The deletion-only parsimony problem.PLoS computational biology · 2025Article
- Single-character insertion-deletion model preserves long indels in ancestral sequence reconstruction.BMC bioinformatics · 2024Article
- Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence-A Critical Review.Plants (Basel, Switzerland) · 2024Review
- Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.Molecular biology and evolution · 2024Review
- COATi: Statistical Pairwise Alignment of Protein-Coding Sequences.Molecular biology and evolution · 2024Article
- Correlations between alignment gaps and nucleotide substitution or amino acid replacement.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Measuring Phylogenetic Information of Incomplete Sequence Data.Systematic biology · 2022Article
- Tatajuba: exploring the distribution of homopolymer tracts.NAR genomics and bioinformatics · 2022Article
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1 author.
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Abstract
Sequence alignment is essential for phylogenetic and molecular evolution inference, as well as in many other areas of bioinformatics and evolutionary biology. Inaccurate alignments can lead to severe biases in most downstream statistical analyses. Statistical alignment based on probabilistic models of sequence evolution addresses these issues by replacing heuristic score functions with evolutionary model-based probabilities. However, score-based aligners and fixed-alignment phylogenetic approaches are still more prevalent than methods based on evolutionary indel models, mostly due to computational convenience. Here, I present new techniques for improving the accuracy and speed of statistical evolutionary alignment. The "cumulative indel model" approximates realistic evolutionary indel dynamics using differential equations. "Adaptive banding" reduces the computational demand of most alignment algorithms without requiring prior knowledge of divergence levels or pseudo-optimal alignments. Using simulations, I show that these methods lead to fast and accurate pairwise alignment inference. Also, I show that it is possible, with these methods, to align and infer evolutionary parameters from a single long synteny block ($\approx$530 kbp) between the human and chimp genomes. The cumulative indel model and adaptive banding can therefore improve the performance of alignment and phylogenetic methods. [Evolutionary alignment; pairHMM; sequence evolution; statistical alignment; statistical genetics.].
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