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. 2005 Mar 22:6:64.
doi: 10.1186/1471-2105-6-64.

MBEToolbox: a MATLAB toolbox for sequence data analysis in molecular biology and evolution

James J Cai  1 , David K Smith, Xuhua Xia, Kwok-Yung Yuen
Affiliations

MBEToolbox: a MATLAB toolbox for sequence data analysis in molecular biology and evolution

James J Cai et al. BMC Bioinformatics. .

Abstract

Background: MATLAB is a high-performance language for technical computing, integrating computation, visualization, and programming in an easy-to-use environment. It has been widely used in many areas, such as mathematics and computation, algorithm development, data acquisition, modeling, simulation, and scientific and engineering graphics. However, few functions are freely available in MATLAB to perform the sequence data analyses specifically required for molecular biology and evolution.

Results: We have developed a MATLAB toolbox, called MBEToolbox, aimed at filling this gap by offering efficient implementations of the most needed functions in molecular biology and evolution. It can be used to manipulate aligned sequences, calculate evolutionary distances, estimate synonymous and nonsynonymous substitution rates, and infer phylogenetic trees. Moreover, it provides an extensible, functional framework for users with more specialized requirements to explore and analyze aligned nucleotide or protein sequences from an evolutionary perspective. The full functions in the toolbox are accessible through the command-line for seasoned MATLAB users. A graphical user interface, that may be especially useful for non-specialist end users, is also provided.

Conclusion: MBEToolbox is a useful tool that can aid in the exploration, interpretation and visualization of data in molecular biology and evolution. The software is publicly available at http://web.hku.hk/~jamescai/mbetoolbox/ and http://bioinformatics.org/project/?group_id=454

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Figures

Figure 1
Figure 1
Log-likelihood of evolutionary distance. (a) Likelihood as function of K2P distance. The distance is estimated by maximising the likelihood of the alignment with the bias of transitions to transversions, kappa, held fixed. (b) Likelihood as a function of distance and kappa. Both the distance and kappa are optimised simultaneously. The maximum likelihood peaks are marked with *. The two sequences used are the coding regions of Tamarin eosinophil-derived neurotoxin (Acc. No.: U24099) and human eosinophil cationic gene (Acc. No: NM_002935).
Figure 2
Figure 2
MBEToolbox GUI. (a) Distances submenu; (b) Phylogeny submenu; (c) Graph submenu; and (d) Polymorphism submenu.
Figure 3
Figure 3
A comparison between sliding window and enhanced sliding window methods. Sliding window analysis of Ks and Ka for the concatenated coding regions of two hepatitis C virus strains, HCV-JS and HCV-JT. The number of codons for the C, El, E2, NS2, NS3, NS4, NS5A, and NS5B genes are 191, 192, 426, 217, 631, 315, 447, and 591, respectively. The different coding regions are separated by vertical lines. (a) illustrates the result of a normal sliding window analysis; (b) illustrates the result of the enhanced sliding window analysis. Beginnings and ends of regions poor in synonymous substitutions (slope < 0) are indicated by the arrows a and b (genes C and El) and e and f (gene NS5B). A region rich in synonymous substitutions (slope > 0) in gene NS3 is indicated by arrows c and d.
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