Bioinformatics Toolbox 3.0

Category Genomics>Gene Expression Analysis/Profiling/Tools

Abstract Bioinformatics Toolbox 3.0 offers computational molecular biologists and other research scientists an open and extensible environment in which to explore ideas, prototype new algorithms, and build applications in drug research, genetic engineering, and other genomics and proteomics projects. The toolbox provides access to genomic and proteomic data formats, analysis techniques, and specialized visualizations for genomic and proteomic sequence and microarray analysis. Most functions are implemented in the open MATLAB (see Note 1) language, enabling you to customize the algorithms or develop your own. Key features include:

Microarray Data Analysis and Visualization - Product enables you to analyze and comprehend raw microarray data.

Microarray Normalization - You can use several methods for normalizing microarray data, including lowess, global mean, median absolute deviation (MAD), and quantile normalization. You can apply these methods to the entire microarray chip or to specific regions or blocks. Filtering and imputation functions let you clean raw data before running analysis and visualization routines.

Data Analysis and Visualization - Bioinformatics Toolbox lets you perform background adjustment and calculate gene (probe set) expression values from Affymetrix microarray probe-level data using robust multiarray average (RMA) and guanine-cytosine robust multiarray average (GCRMA). You can apply circular binary segmentation (CBS) to array comparative genomic hybridization (CGH) data and estimate the false discovery rate (FDR) of multiple hypotheses testing of gene expression data from a microarray experiment. You can perform rank-invariant set normalization on either probe intensities for multiple Affymetrix CEL (cell intensity) files or gene expression values from two different experimental conditions.

Specialized routines for visualizing microarray data include volcano plots, box plots, loglog plots, I-R plots, and spatial heat maps of the microarray. You can also visualize ideograms with G-banding (Giemsa stain-banding) patterns.

Using routines from Statistics Toolbox (required, and available separately - see Note 2), you can classify your results, perform hierarchical and K-means clustering, and represent your microarray data in statistical visualizations, such as two-dimensional clustergrams with optimal leaf ordering, heat maps, principle component plots, and classification trees.

Additional Key Features include:

Note 1: MATLAB is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and FORTRAN.

Note 2: Statistics Toolbox extends MATLAB to support a wide range of common statistical tasks. The toolbox contains two (2) categories of tools - 1) Building-block statistical functions for use in MATLAB programming; 2) Graphical user interfaces (GUIs) for interactive data analysis.

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Manufacturer Web Site The MathWorks, Inc.

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G6G Abstract Number 20027

G6G Manufacturer Number 102625