Literature DB >> 19933876

Coalescent simulation of intracodon recombination.

Miguel Arenas1, David Posada.   

Abstract

The coalescent with recombination is a very useful tool in molecular population genetics. Under this framework, genealogies often represent the evolution of the substitution unit, and because of this, the few coalescent algorithms implemented for the simulation of coding sequences force recombination to occur only between codons. However, it is clear that recombination is expected to occur most often within codons. Here we have developed an algorithm that can evolve coding sequences under an ancestral recombination graph that represents the genealogies at each nucleotide site, thereby allowing for intracodon recombination. The algorithm is a modification of Hudson's coalescent in which, in addition to keeping track of events occurring in the ancestral material that reaches the sample, we need to keep track of events occurring in ancestral material that does not reach the sample but that is produced by intracodon recombination. We are able to show that at typical substitution rates the number of nonsynonymous changes induced by intracodon recombination is small and that intracodon recombination does not generally result in inflated estimates of the overall nonsynonymous/synonymous substitution ratio (omega). On the other hand, recombination can bias the estimation of omega at particular codons, resulting in apparent rate variation among sites and in the spurious identification of positively selected sites. Importantly, in this case, allowing for variable synonymous rates across sites greatly reduces the false-positive rate and recovers statistical power. Finally, coalescent simulations with intracodon recombination could be used to better represent the evolution of nuclear coding genes or fast-evolving pathogens such as HIV-1.We have implemented this algorithm in a computer program called NetRecodon, freely available at http://darwin.uvigo.es.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19933876      PMCID: PMC2828723          DOI: 10.1534/genetics.109.109736

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  51 in total

1.  Codon-substitution models for heterogeneous selection pressure at amino acid sites.

Authors:  Z Yang; R Nielsen; N Goldman; A M Pedersen
Journal:  Genetics       Date:  2000-05       Impact factor: 4.562

2.  The coalescent with gene conversion.

Authors:  C Wiuf; J Hein
Journal:  Genetics       Date:  2000-05       Impact factor: 4.562

3.  Estimating diversifying selection and functional constraint in the presence of recombination.

Authors:  Daniel J Wilson; Gilean McVean
Journal:  Genetics       Date:  2005-12-30       Impact factor: 4.562

4.  A Markov Chain Model of Coalescence with Recombination

Authors: 
Journal:  Theor Popul Biol       Date:  1997-08       Impact factor: 1.570

5.  Ancestral inference from samples of DNA sequences with recombination.

Authors:  R C Griffiths; P Marjoram
Journal:  J Comput Biol       Date:  1996       Impact factor: 1.479

6.  Genealogical evidence for positive selection in the nef gene of HIV-1.

Authors:  P M Zanotto; E G Kallas; R F de Souza; E C Holmes
Journal:  Genetics       Date:  1999-11       Impact factor: 4.562

7.  The neighbor-joining method: a new method for reconstructing phylogenetic trees.

Authors:  N Saitou; M Nei
Journal:  Mol Biol Evol       Date:  1987-07       Impact factor: 16.240

8.  Gene flow and the geographic structure of natural populations.

Authors:  M Slatkin
Journal:  Science       Date:  1987-05-15       Impact factor: 47.728

9.  A codon-based model of nucleotide substitution for protein-coding DNA sequences.

Authors:  N Goldman; Z Yang
Journal:  Mol Biol Evol       Date:  1994-09       Impact factor: 16.240

10.  The coalescent process in models with selection and recombination.

Authors:  R R Hudson; N L Kaplan
Journal:  Genetics       Date:  1988-11       Impact factor: 4.562

View more
  38 in total

1.  Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation.

Authors:  J S Lopes; M Arenas; D Posada; M A Beaumont
Journal:  Heredity (Edinb)       Date:  2013-10-23       Impact factor: 3.821

2.  The effect of recombination on the reconstruction of ancestral sequences.

Authors:  Miguel Arenas; David Posada
Journal:  Genetics       Date:  2010-02-01       Impact factor: 4.562

3.  A minimal descriptor of an ancestral recombinations graph.

Authors:  Laxmi Parida; Pier Francesco Palamara; Asif Javed
Journal:  BMC Bioinformatics       Date:  2011-02-15       Impact factor: 3.169

Review 4.  Microbial sequence typing in the genomic era.

Authors:  Marcos Pérez-Losada; Miguel Arenas; Eduardo Castro-Nallar
Journal:  Infect Genet Evol       Date:  2017-09-21       Impact factor: 3.342

5.  Detection of Regional Variation in Selection Intensity within Protein-Coding Genes Using DNA Sequence Polymorphism and Divergence.

Authors:  Zi-Ming Zhao; Michael C Campbell; Ning Li; Daniel S W Lee; Zhang Zhang; Jeffrey P Townsend
Journal:  Mol Biol Evol       Date:  2017-11-01       Impact factor: 16.240

6.  Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection.

Authors:  Kevin Dialdestoro; Jonas Andreas Sibbesen; Lasse Maretty; Jayna Raghwani; Astrid Gall; Paul Kellam; Oliver G Pybus; Jotun Hein; Paul A Jenkins
Journal:  Genetics       Date:  2016-02-08       Impact factor: 4.562

7.  Evolutionary genomic relationships and coupling in MK-STYX and STYX pseudophosphatases.

Authors:  Yi Qi; Di Kuang; Kylan Kelley; William J Buchser; Shantá D Hinton
Journal:  Sci Rep       Date:  2022-03-09       Impact factor: 4.379

8.  Synonymous and nonsynonymous distances help untangle convergent evolution and recombination.

Authors:  Peter B Chi; Sujay Chattopadhyay; Philippe Lemey; Evgeni V Sokurenko; Vladimir N Minin
Journal:  Stat Appl Genet Mol Biol       Date:  2015-08

9.  Protein evolution along phylogenetic histories under structurally constrained substitution models.

Authors:  Miguel Arenas; Helena G Dos Santos; David Posada; Ugo Bastolla
Journal:  Bioinformatics       Date:  2013-09-12       Impact factor: 6.937

10.  Simulation of molecular data under diverse evolutionary scenarios.

Authors:  Miguel Arenas
Journal:  PLoS Comput Biol       Date:  2012-05-31       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.