Resources

 

All of the resources listed here, can be used by the scientific community free of charge.

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HD Proteome Base

A novel data repository for quantitative proteome profiles related to Huntington's disease (HD). The user-friendly web portal allows the researcher to query for proteins and to visualise their expression across the CAG repeat length series and across different tissues.

If you are interested to learn more about the HD Proteome Base, please click here.

SubExtractor: Identifying regulated subnetworks from phosphoproteomic data

SubExtractor is a computer programme that combines phosphoproteomic data with protein network information to identify differentially regulated subnetworks and individual proteins. The method is based on a Bayesian probabilistic model in combination with a genetic algorithm and rigorous significance testing. The Bayesian model is designed to account for information about differential regulation as well as network topology. SubExtractor is also applicable to gene or protein expression data. For further information see the BMC Bioinformatics article

Identification of Significant Features by the Global Mean Rank Test

Motivation

With the introduction of omics-technologies such as transcriptomics or proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as limited number of replicates, missing values and non-normal distribution. With the MeanRank (MR) test we aimed at developing a test which is robust against these constraints, while favorably scaling with an increasing number of replicates.

Results

The test proposed here is a global test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation.

In extensive simulations comparing the MeanRank (MR) test to other frequently used methods, we found that MR performs well with small and large numbers of replicates, variable variance between replicates, and variable regulation across features on normally distributed simulation data.

MR outperformed the other global methods in this study. Compared to the popular significance analysis of microarrays (SAM), MR performed better or similar in one-sample and comparable in two-sample simulations.

We then applied the best performing tests, MR and SAM, to identify significant changes in the phosphoproteome induced by the kinase inhibitors erlotinib and 3-MB-PP1 in two independently published mass spectrometry-based studies. In order to demonstrate the applicability to different technologies, we investigated the ability of both tests to find differentially regulated genes from Golub's leukemia microarray study.

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