After considering a number of commercial and open-source mathematical modeling tools, the group chose MATLAB® for its ease of use, interoperability, industry acceptance, and rich modeling and computational capabilities.
Research teams within the group use MATLAB to develop applications for genomic and proteomic analysis such as biomarker identification, two-dimensional gel analysis, and artificial neural networks. They make these packages available to other groups and the scientific community via the web using the MATLAB Web Server.
MATLAB, Statistics and Machine Learning Toolbox™, and Optimization Toolbox™ provide the foundation for much of the group’s work. “MATLAB is at the core of our ability to function. And everything we do uses Statistics and Machine Learning Toolbox. We also use Optimization Toolbox for numerical decoupling by tracing state variables with neural networks in conjunction with genetic algorithms,” says Almeida.
The group uses Bioinformatics Toolbox™ to simplify sequence alignment using Needleman-Wunsch and Smith-Waterman algorithms. The toolbox also enables them to normalize, visualize, and import microarrays, including data from NCBI’s Gene Expression Omnibus. They also use the SOAP client in MATLAB to interact with local and public data on the Internet.
MUSC uses Wavelet Toolbox™ and Image Processing Toolbox™ to denoise and identify clusters of proteins in two-dimensional gel samples.
In the area of biochemical system theory, researchers use Symbolic Math Toolbox™ to numerically decouple and recast systems of nonlinear differential equations.
They also use MATLAB to make system calls to other open-source technologies, including the PostgreSQL database and the statistical package R, and parse the results.
“There is a strong bias in the bioinformatics community for open-source tools, but we really believe MATLAB is comparable to an open-source tool because the code we develop has an open architecture, so anyone can see the source code,” says Almeida. “I can’t ever remember one of my reviewed papers not getting accepted because I used MATLAB.”
MUSC researchers plan to continue using MathWorks tools to develop applications for genomic, transcriptomic, and proteomic analysis.