Abstract
Label-free quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis, and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyze and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes a cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but also annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardization in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/.
Highlights
Proteomics can be defined as the application of technologies for identification and quantification of the protein content of complex biological samples
Different label-free quantification (LFQ) benchmarking datasets are chosen to verify that MD 1.0 recovers comparable results to Perseus
PXD002057 (“HER2 dataset”) contains raw data for an experiment with two groups, each with three samples. These two groups come from two cancer cell lines, a parental SKBR3 cell line and another cell line derived from the first, which is resistant to human epidermal growth factor receptor 2 (HER2)-targeted therapy
Summary
Proteomics can be defined as the application of technologies for identification and quantification of the protein content of complex biological samples. Over the past few decades, proteomics research and the complexity of experimental research questions addressed by it have developed rapidly and are having a growing impact in biological and medical research. Areas such as understanding mechanisms of action in disease progression and therapeutic intervention as well as detection of diagnostic markers, identifying candidates for vaccine production, and understanding pathogenic mechanisms and gene expression patterns have been of growing importance in advancing many areas of medically related research.[1,2]. LFQ is a simpler, more economical, and scalable method that requires a considered experimental design to achieve robust biological insights.[3]
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