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هفتمین همایش بیو انفورماتیک ایران
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
The advantages and disadvantages of using three different kinds of character-based method in phylogenetic study |
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| چکیده انگلیسی مقاله |
Phylogenetic analysis is one of the most powerful tools to infer or estimate evolutionary relationships between lineages. The evolutionary history are inferred from phylogenetic analysis which is usually described as branching, tree like diagrams that signifies an estimated pedigree of the inherited relationships between molecules (‘‘gene trees’’), organisms, or both. There are two methods in reconstructing the phylogenetic tree including: 1. Distance-based method and 2. Characters-based method. There are three different kinds of character-based Method; Maximum Parsimony (MP), Maximum Likelihood (ML) and Bayesian inference (BI). In this research the advantages and disadvantages of these three methods were studied. The main idea behind the maximum parsimony is that the best tree is the tree with the shortest branch length possible. However, the number of trees that has been generated based on this method will increase exponentially with the addition of each new sequence. Parsimony-based phylogeny based on morphological characters [1]. Maximum parsimony is less complex compared to maximum likelihood. The probability of the data given the model and tree hypothesis is named likelihood. The adjustment between the data and the predictions made by the model and tree hypothesis are measured using the likelihood [2]. This method is the slowest and most computationally intensive method, although it seems to give the best result and the most informative tree [3]. Maximum likelihood analysis of DNA and amino acid sequence data has been made practical with recent advances in the models of DNA substitution, computer programs, and computational speed [4]. Maximum likelihood evaluates different tree topologies and uses all the sequence information. In contrast, this method is extremely slow. Another method that is perfect for data analysis is Bayesian inference (BI). Bayesian methods are character state methods that use an optimality criterion; however, they are conceptually very different from MP and ML in that they only search for the single best tree. This method has a strong connection to the maximum likelihood method; might be a faster way to assess support for trees than maximum likelihood bootstrapping. Bayesian methods rely on an algorithm [Markov chain Monte Carlo (MCMC)] that does not attempt to find the highest point in the space of all parameters [5]. The prior distributions for parameters must be specified; it can be difficult to determine whether the Markov chain Monte Carlo (MCMC) approximation has run for long enough. |
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| کلیدواژه‌های انگلیسی مقاله |
Phylogenetic, Character-based method, Maximum Likelihood, Maximum Parsimony, Bayesian inference. |
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| نویسندگان مقاله |
Maryam Moudi - University of Birjand, Birjand
Mahboubeh Sadat Hosseinzadeh - University of Birjand, Birjand
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| نشانی اینترنتی |
http://www.icb7.ir |
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| زبان مقاله منتشر شده |
en |
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