ArticleBMC bioinformatics2017
Solving the master equation for Indels.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.Molecular biology and evolution · 2024Review
- Insertions and deletions as phylogenetic signal in an alignment-free context.PLoS computational biology · 2022Article
- Bridging the gaps in statistical models of protein alignment.Bioinformatics (Oxford, England) · 2022Article
- The Cumulative Indel Model: Fast and Accurate Statistical Evolutionary Alignment.Systematic biology · 2021Article
- Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy.Genome biology and evolution · 2020Article
- Eleven grand challenges in single-cell data science.Genome biology · 2020Review
- Estimation of duplication history under a stochastic model for tandem repeats.BMC bioinformatics · 2019Article
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Authors and funding
1 author.
Funding
Abstract
backgroundDespite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events. MAIN TEXT: This paper discusses progress in the area of insertion-deletion models, in view of recent work by Ezawa (BMC Bioinformatics 17:304, 2016); (BMC Bioinformatics 17:397, 2016); (BMC Bioinformatics 17:457, 2016) on the calculation of time-dependent gap length distributions in pairwise alignments, and current approaches for extending these approaches from ancestor-descendant pairs to phylogenetic trees.
conclusionsWhile approximations that use finite-state machines (Pair HMMs and transducers) currently represent the most practical approach to problems such as sequence alignment and phylogeny, more rigorous approaches that work directly with the matrix exponential of the underlying continuous-time Markov chain also show promise, especially in view of recent advances.
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Registered trials
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