DOSCHEDA: a web application for interactive chemoproteomics data analysis

Background. Mass Spectrometry (MS) based chemoproteomics has recently become a main tool to identify and quantify cellular target protein interactions with ligands/drugs in drug discovery. The complexity associated with these new types of data requires scientists with a limited computational background to perform systematic data quality controls as well as to visualize the results derived from the analysis to enable rapid decision making. To date, there are no readily accessible platforms specifically designed for chemoproteomics data analysis. Results. We developed a Shiny-based web application named DOSCHEDA (Down Stream Chemoproteomics Data Analysis) to assess the quality of chemoproteomics experiments, to filter peptide intensities based on linear correlations between replicates, and to perform statistical analysis based on the experimental design. In order to increase its accessibility, DOSCHEDA is designed to be used with minimal user input and it does not require programming knowledge. Typical inputs can be protein fold changes or peptide intensities obtained from Proteome Discover, MaxQuant or other similar software. DOSCHEDA aggregates results from bioinformatics analyses performed on the input dataset into a dynamic interface, it encompasses interactive graphics and enables customized output reports. Conclusions. DOSCHEDA is implemented entirely in R language. It can be launched by any system with R installed, including Windows, Mac OS and Linux distributions. DOSCHEDA is hosted on a shiny-server at https://doscheda.shinyapps.io/doscheda and is also available as a Bioconductor package (http://www.bioconductor.org/). Subjects Bioinformatics

[1]  Bernhard Kuster,et al.  Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors , 2007, Nature Biotechnology.

[2]  T. Hunter,et al.  The Protein Kinase Complement of the Human Genome , 2002, Science.

[3]  H. Neubert,et al.  Clinical chemoproteomics—Opportunities and obstacles , 2017, Science Translational Medicine.

[4]  Amber L. Couzens,et al.  The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data , 2013, Nature Methods.

[5]  Jean Yee Hwa Yang,et al.  OCAP: an open comprehensive analysis pipeline for iTRAQ , 2012, Bioinform..

[6]  Ian Collins,et al.  Measuring and interpreting the selectivity of protein kinase inhibitors , 2009, Journal of chemical biology.

[7]  Bernhard Kuster,et al.  Robust and Sensitive iTRAQ Quantification on an LTQ Orbitrap Mass Spectrometer*S , 2008, Molecular & Cellular Proteomics.

[8]  B. Kuster,et al.  Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. , 2013, Journal of proteome research.

[9]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[10]  Gordon K Smyth,et al.  Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2004, Statistical applications in genetics and molecular biology.

[11]  Qiang Kou,et al.  TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization , 2016, Bioinform..

[12]  Daniel Martinez Molina,et al.  The Cellular Thermal Shift Assay: A Novel Biophysical Assay for In Situ Drug Target Engagement and Mechanistic Biomarker Studies. , 2016, Annual review of pharmacology and toxicology.

[13]  Brendan MacLean,et al.  Bioinformatics Applications Note Gene Expression Skyline: an Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments , 2022 .

[14]  B. Kuster,et al.  Mass spectrometry-based proteomics in preclinical drug discovery. , 2012, Chemistry & biology.

[15]  Marco Y. Hein,et al.  The Perseus computational platform for comprehensive analysis of (prote)omics data , 2016, Nature Methods.

[16]  Florent Baty,et al.  Dose-Response Analysis Using R , 2015, PLoS ONE.

[17]  Marcus Bantscheff,et al.  Chemoproteomic approaches to drug target identification and drug profiling. , 2012, Bioorganic & medicinal chemistry.

[18]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[19]  Thomas Burger,et al.  DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics , 2016, Bioinform..

[20]  Bernhard Y. Renard,et al.  iPQF: a new peptide-to-protein summarization method using peptide spectra characteristics to improve protein quantification , 2016, Bioinform..

[21]  H. Daub.  Quantitative proteomics of kinase inhibitor targets and mechanisms. , 2015, ACS chemical biology.

[22]  Brendan MacLean,et al.  MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments , 2014, Bioinform..