Background
Proteome analysis based on mass spectrometry becomes an essential part of many biological studies. The increasing value of the analysis results elevates the importance of proper quality control (QC) over the instrument performance and operation that becomes a key step in the experimental workflow. A large variety of earlier developed QC tools are typically loaded with numerous metrics, require complex mixtures and rely on extensive data pre-processing. Further, the results are often tricky for interpretation, especially for early-career scientists. Yet, rapid and simple assessment of the instrument’s readiness for the analysis is often all one needs in practice. In this work, we developed an approach and the software viQC (visual&intuitive quality control) based on a few recently demonstrated metrics for quick assessment of data quality in bottom-up proteomics.
Methods
Three datasets (one protein digest, whole-cell lysates, and PNNL QC dataset [1]) obtained from five different Orbitrap-based instruments were used for developed QC method evaluation. Further, we used the method for large scale quality analysis of datasets from PRIDE database.
Results
The proposed QC method was compared with unsupervised approach developed recently [2] using 57 whole cell lysate experimental runs and shown high specificity (100%) and selectivity (one-sided 95%-CI from 86.6% to 100%). Moreover, viQC algorithm can be effectively used even for a simple mixture, such as a single protein digest. By optimizing the instrument parameters following the results of data characterization by viQC, multifold improvements in the number of identifications was demonstrated.
Conclusion
The Python-based software viQC for fast, intuitive, and visual quality control of bottom-up proteomic experiments were proposed and developed. The software takes less than a minute on a regular PC for analysis of typical experimental runs. Accessing the metric is not requiring time-consuming data processing, spectra identification, and can be easily implemented for day-to-day practice.