DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood

BMC Bioinformatics

Tane Kim²  Weilong Hao¹ 

[1] Department of Biological Sciences, Wayne State University, 48202 Detroit, USA

[2] Mathematics Undergraduate Program, Wayne State University, 48202 Detroit, USA

关键词: Phylogeny;    Maximum likelihood;    Birth and death;    Gene family evolution;    Discrete character states;   

Others  :  1085776
DOI  :  10.1186/1471-2105-15-320

 received in 2014-07-26, accepted in 2014-09-25,  发布年份 2014

【 摘 要 】

Background

The study of discrete characters is crucial for the understanding of evolutionary processes. Even though great advances have been made in the analysis of nucleotide sequences, computer programs for non-DNA discrete characters are often dedicated to specific analyses and lack flexibility. Discrete characters often have different transition rate matrices, variable rates among sites and sometimes contain unobservable states. To obtain the ability to accurately estimate a variety of discrete characters, programs with sophisticated methodologies and flexible settings are desired.

Results

DiscML performs maximum likelihood estimation for evolutionary rates of discrete characters on a provided phylogeny with the options that correct for unobservable data, rate variations, and unknown prior root probabilities from the empirical data. It gives users options to customize the instantaneous transition rate matrices, or to choose pre-determined matrices from models such as birth-and-death (BD), birth-death-and-innovation (BDI), equal rates (ER), symmetric (SYM), general time-reversible (GTR) and all rates different (ARD). Moreover, we show application examples of DiscML on gene family data and on intron presence/absence data.

Conclusion

DiscML was developed as a unified R program for estimating evolutionary rates of discrete characters with no restriction on the number of character states, and with flexibility to use different transition models. DiscML is ideal for the analyses of binary (1s/0s) patterns, multi-gene families, and multistate discrete morphological characteristics.

【 授权许可】

   
2014 Kim and Hao; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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