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In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on mixed models can use large amounts of (computing) time. MS-based proteomics laboratories are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at qcquan.net ) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

QCQuan: A Web Tool for the Automated Assessment of Protein Expression and Data Quality of Labeled Mass Spectrometry Experiments