| Bioinformatics Toolbox | |
The Bioinformatics Toolbox extends MATLAB® to provide an integrated and extendable software environment for genome and proteome analysis. Together, MATLAB and the Bioinformatics Toolbox give scientists and engineers a set of computational tools to solve problems and build applications in drug discovery, genetic engineering, and biological research.
You can use the basic bioinformatic functions provided with this toolbox to create more complex algorithms and applications. These robust and well tested functions are the functions that you would otherwise have to create yourself.
Data formats and databases — Connect to Web accessible databases. Read and convert between multiple data formats.
Sequence analysis — Determine statistical characteristics of data. Manipulate and align sequences. Model patterns in biological sequences using Hidden Markov Model (HMM) profiles.
Phylogenetic analysis — Create and manipulate phylogenetic tree data.
Microarray data analysis — Read, normalize, and visualize microarray data.
Mass spectrometry data — Analyze and enhance raw mass spectrometry data.
Statistical Learning — Classify and identify features in data sets with statistical learning tools.
Programming interface — Use other bioinformatic software (Bioperl and BioJava) within the MATLAB environment.
The field of bioinformatics is rapidly growing and will become increasingly important as biology becomes a more analytical science. The Bioinformatics Toolbox provides an open environment that you can customize for development and deployment of the analytical tools you will need.
Prototype and develop algorithms — Prototype new ideas in an open and extendable environment. Develop algorithms using efficient string processing and statistical functions, view the source code for existing functions, and use the code as a template for customizing, improving, or creating your own functions. See Prototype and Development Environment.
Visualize data — Visualize sequences and alignments, gene expression data, phylogenetic trees, mass spectrometry data, protein structure, and relationships between data with interconnected graphs. See Data Visualization.
Share and deploy applications — Use an interactive GUI builder to develop a custom graphical front end for your data analysis programs. Create stand-alone applications that run separately from MATLAB. See Algorithm Sharing and Application Deployment.
| | Getting Started | Expected User | |
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