Predictive Genetic Algorithm:

Proteomics is an emerging field that uses many types of proteomic platforms however has few standardized procedures. Deciding which platform to use to perform large-scale proteomic studies is either based on personal preference or on so-called "figures of merit" such as dynamic range, resolution, and the limit of detection; these factors are often insufficient to predict the outcome of the experiment as the detection of peptides correlates to the chemical properties of each peptide. There is a need for a novel figure of merit that describes the overall performance of a platform based on measured output, which in proteomics is often a list of identified peptides.

We report the development of such a figure of merit in a computer program called the Peptide Properties Predictive Genetic Algorithm (PPPGA). This algorithm takes into account three properties of the observed peptides (length, hydrophobicity, and pI). The figures of merit obtained for different platforms can be clustered to find platforms that are biased in similar ways. Even though some platforms contain different components, they lead to the identification of similar peptide types and are therefore redundant. The algorithm can thus be used as an exploratory tool to suggest a minimal number of complementary experiments in order to maximize experimental efficiency.

The download consists of an archived file called PGA.zip. Extract the file to a temporary folder and run setup.exe. A help file in Microsoft Office TM called PPPGA can be found in the installation folder. An example file called 1_b1_cit.txt can also be found in the installation folder.

Spectral Search Engine Integrator:

The identification of proteins in functional proteomics relies on the generation of fragmentation patterns (MS/MS spectra) that are related to the amino acid sequences of peptides enzymatically derived from protein.

The Spectra Search Engine Integrator (SSEI) is a tool that integrates in a common interface the results from powerful search engines (Mascot, OMSSA and Tandem) that uses mass spectrometry data to identify proteins from primary sequence databases. This software also integrated an identification score that combines the identification scores of the individual search engines.

Analysator Parser:

Automated parser of d0- and d3-methyl esterified phosphopeptides analyzed by MS and identified using Mascot(TM).

Gene Network Reconstruction:

Software for "Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty"

Microarray gene expression data (16 time points, 3 biological replicates):

This data set is provided with permission from Pioneer Hi-Bred International, Inc.:

Intensity values after treatment (15.7 MB)
Intensity values of non-treated reference samples (15.6 MB)

The data set and an analysis of it are described here:
D. R. Bickel, Z. Montazeri, P.-C. Hsieh, M. Beatty, S. J. Lawit, and N. J. Bate, "Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: A case for the second derivative," Bioinformatics 25, 772-779 (2009). Full paper

Other papers on the analysis of gene expression data

Gene Network Reconstruction Software