Solving the master equation for Indels

Background Despite 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...

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Vydáno v:BMC bioinformatics Ročník 18; číslo 1; s. 255 - 10
Hlavní autor: Holmes, Ian H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 12.05.2017
Springer Nature B.V
BMC
Témata:
Algorithms
Alignment
Bioinformatics
Biomedical and Life Sciences
Commentary
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer Simulation
Evolution
INDEL Mutation - genetics
Indels
Insertion
Life Sciences
Markov analysis
Markov Chains
Mathematical analysis
Mathematical models
Microarrays
Models, Theoretical
Monte Carlo Method
Nucleotide sequence
Pathogens
Phylogenetics
Phylogeny
Probability distribution
Proteins
Sequence Alignment
Sequence analysis (methods)
State machines
Statistical analysis
Statistics
Time dependence
Transducers
Phylogenetics
Alignment
Indels
ISSN:1471-2105, 1471-2105
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Shrnutí:Background Despite 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. Conclusions While 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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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-017-1665-1