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On calculating the probability of a set of orthologous sequences

Probabilistic DNA sequence models have been intensively applied to genome research. Within the evolutionary biology framework, this article investigates the feasibility for rigorously estimating the probability of a set of orthologous DNA sequences which evolve from a common progenitor. We propose M...

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Detalles Bibliográficos
Autores principales: Liu, Junfeng, Chen, Liang, Zhao, Hongyu, Moore, Dirk F, Lin, Yong, Shih, Weichung Joe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169941/
https://www.ncbi.nlm.nih.gov/pubmed/21918614
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author Liu, Junfeng
Chen, Liang
Zhao, Hongyu
Moore, Dirk F
Lin, Yong
Shih, Weichung Joe
author_facet Liu, Junfeng
Chen, Liang
Zhao, Hongyu
Moore, Dirk F
Lin, Yong
Shih, Weichung Joe
author_sort Liu, Junfeng
collection PubMed
description Probabilistic DNA sequence models have been intensively applied to genome research. Within the evolutionary biology framework, this article investigates the feasibility for rigorously estimating the probability of a set of orthologous DNA sequences which evolve from a common progenitor. We propose Monte Carlo integration algorithms to sample the unknown ancestral and/or root sequences a posteriori conditional on a reference sequence and apply pairwise Needleman–Wunsch alignment between the sampled and nonreference species sequences to estimate the probability. We test our algorithms on both simulated and real sequences and compare calculated probabilities from Monte Carlo integration to those induced by single multiple alignment.
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spelling pubmed-31699412011-09-14 On calculating the probability of a set of orthologous sequences Liu, Junfeng Chen, Liang Zhao, Hongyu Moore, Dirk F Lin, Yong Shih, Weichung Joe Adv Appl Bioinforma Chem Original Research Probabilistic DNA sequence models have been intensively applied to genome research. Within the evolutionary biology framework, this article investigates the feasibility for rigorously estimating the probability of a set of orthologous DNA sequences which evolve from a common progenitor. We propose Monte Carlo integration algorithms to sample the unknown ancestral and/or root sequences a posteriori conditional on a reference sequence and apply pairwise Needleman–Wunsch alignment between the sampled and nonreference species sequences to estimate the probability. We test our algorithms on both simulated and real sequences and compare calculated probabilities from Monte Carlo integration to those induced by single multiple alignment. Dove Medical Press 2009-02-26 /pmc/articles/PMC3169941/ /pubmed/21918614 Text en © 2009 Liu et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.
spellingShingle Original Research
Liu, Junfeng
Chen, Liang
Zhao, Hongyu
Moore, Dirk F
Lin, Yong
Shih, Weichung Joe
On calculating the probability of a set of orthologous sequences
title On calculating the probability of a set of orthologous sequences
title_full On calculating the probability of a set of orthologous sequences
title_fullStr On calculating the probability of a set of orthologous sequences
title_full_unstemmed On calculating the probability of a set of orthologous sequences
title_short On calculating the probability of a set of orthologous sequences
title_sort on calculating the probability of a set of orthologous sequences
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169941/
https://www.ncbi.nlm.nih.gov/pubmed/21918614
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