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LineageSpecificSeqgen: generating sequence data with lineage-specific variation in the proportion of variable sites


Grievink, LS and Penny, D and Hendy, MD and Holland, BR, LineageSpecificSeqgen: generating sequence data with lineage-specific variation in the proportion of variable sites, BMC Evolutionary Biology, 8, (1) pp. 1-6. ISSN 1471-2148 (2008) [Refereed Article]


Copyright Statement

2008 Grievink et al

DOI: doi:10.1186/1471-2148-8-317


Background: Commonly used phylogenetic models assume a homogeneous evolutionary process throughout the tree. It is known that these homogeneous models are often too simplistic, and that with time some properties of the evolutionary process can change (due to selection or drift). In particular, as constraints on sequences evolve, the proportion of variable sites can vary between lineages. This affects the ability of phylogenetic methods to correctly estimate phylogenetic trees, especially for long timescales. To date there is no phylogenetic model that allows for change in the proportion of variable sites, and the degree to which this affects phylogenetic reconstruction is unknown. Results: We present LineageSpecificSeqgen, an extension to the seq-gen program that allows generation of sequences with both changes in the proportion of variable sites and changes in the rate at which sites switch between being variable and invariable. In contrast to seq-gen and its derivatives to date, we interpret branch lengths as the mean number of substitutions per variable site, as opposed to the mean number of substitutions per site (which is averaged over all sites, including invariable sites). This allows specification of the substitution rates of variable sites, independently of the proportion of invariable sites. Conclusion: LineageSpecificSeqgen allows simulation of DNA and amino acid sequence alignments under a lineage-specific evolutionary process. The program can be used to test current models of evolution on sequences that have undergone lineage-specific evolution. It facilitates the development of both new methods to identify such processes in real data, and means to account for such processes. The program is available at:

Item Details

Item Type:Refereed Article
Research Division:Information and Computing Sciences
Research Group:Applied computing
Research Field:Applications in life sciences
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biological sciences
UTAS Author:Holland, BR (Professor Barbara Holland)
ID Code:63088
Year Published:2008
Web of Science® Times Cited:10
Deposited By:Mathematics
Deposited On:2010-04-14
Last Modified:2012-04-02
Downloads:399 View Download Statistics

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