Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.05.237412v1?rss=1 Authors: Taechawattananant, P., Yoshii, K., Ishihama, Y. Abstract: Recent advances in liquid chromatography/mass spectrometry (LC/MS) technology have notably improved the sensitivity, resolution, and speed of proteome analysis, resulting in increasing demand for more sophisticated algorithms to interpret highly complex mass spectrograms. We propose a novel application of mass spectrogram decomposition with a group sparsity constraint for joint identification and quantification of peptides and proteins. By incorporating protein-peptide hierarchical relationship knowledge, the isotopic distribution profiles of peptide ions, the learned noise subspace, and predicted retention time initialization into standard non-regularized approach, we have significantly improved the accuracy of analysis. In benchmarking studies, our proteomic mass spectrogram decomposition (protMSD) showed excellent agreement [3277 peptide ions (94.79%) and 493 proteins (98.21%)] with the results of conventional identification and quantification based on Mascot and Skyline of E. coli cell lysate. This is the first application of proteomic mass spectrogram decomposition as a tool for LC/MS-based identification and quantification. Since pre-processing, such as thresholding, is not required, protMSD can maximize the efficiency of both protein and peptide identification and quantification. Copy rights belong to original authors. Visit the link for more info