Statistics and Bioinformatics for high-throughput Biological Data

One of our main interests lies in the interpretation of quantitative profiles of many proteins or other biomolecules measured simultaneously. Experiments producing such datasets may have many different designs, so a rich arsenal of tools and algorithms is needed to gain a comprehensive understanding of the data. The Perseus software is designed to achieve this by integrating computational methods from bioinformatics, statistics and machine learning.

Topics covered in Perseus include expression proteomics, posttranslational modifications, interaction proteomics, machine learning for biomarker discovery, time series analysis and cross-omics data analysis. All computational steps in Perseus are realized as plugins. Users can extend the software by programming their own plugins and sharing them with other users through a plugin store. Perseus is freely available at Linkhttp://www.perseus-framework.org and many open-source plugins already exist.

 

Selected publications:

Cox, J. and Mann, M. (2012) 1D and 2D annotation enrichment: A statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics, 13 Suppl 16:S12.

Robles, M.S., Cox, J. and Mann, M. (2014) In-vivo quantitative proteomics reveals a key contribution of post-transcriptional mechanisms to the circadian regulation of liver metabolism. PLoS Genetics 10(1):e1004047.

Geiger, T., Cox, J. and Mann, M. (2010). Proteomic changes resulting from gene copy number variations in cancer cells. PLoS Genetics 6.

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