Sebastian Vaca

2020-01-27

Sebastian Vaca Sebastian Vaca , Ph.D. received his Master’s degree in Analytical Sciences from the University of Strasbourg, France. Afterward, he joined the BioOrganic Mass Spectrometry Laboratory (LSMBO, Strasbourg) to work with Dr. Alain Van Dorsselaer and Dr. Christine Carapito. His research aimed at improving the proteome characterization by quantitative mass spectrometry and proteogenomics method development. He received his Ph.D. in June 2016 and joined Dr. Jacob Jaffe’s lab at The Broad Institute, Cambridge, MA. He focuses on Data-Independent Acquisition method development and data analysis in order to understand phosphosignaling in cancerous cells. Sebastian’s motivation to develop an automated data-curation tool came from the hundreds of hours of sitting in front of a computer manually integrating chromatographic peaks.

Avant-garde: A Skyline External Tool for automated data-driven DIA data curation

Developments in Data-independent Acquisition data analysis have enabled the detection of large numbers of peptides. However, most tools focus on statistical validation of peptide detection (using target/decoy approaches) but do not address the quantitative suitability of the signals extracted. In practice, time-consuming manual signal curation is still required for rigorous quantitation. Here we introduce Avant-Garde (AvG), a Skyline External Tool for automated MS data curation meant to polish the results of DIA/PRM analysis tools and assess the quantitative suitability of DIA signals. Read More
AvG uses three modules to refine DIA data: 1) A transition refinement module eliminates interferences and noise. 2) A peak boundary refinement module improves peak picking without the need for RT standard peptides. 3) A peak scoring module estimates the false discovery rate (FDR) for quantitative suitability using intuitive metrics. AvG was made possible by Skyline’s Annotations feature that allows the import of external data.


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