Scientific Advisor Board

Michael J. MacCoss

Michael J. MacCoss

Michael J. MacCoss, PhD

Professor of Genome Sciences

University of Washington
Department of Genome Sciences, Institute for Protein Design & Institute for Stem Cell & Regenerative Medicine

Michael J. MacCoss, PhD is Professor of Genome Sciences at the University of Washington. His lab has focused on the development and application of mass spectrometry-based technologies for the high throughput characterization of complex protein mixtures. Realizing that software was a major limitation in proteomics, Dr. MacCoss has established a major software engineering effort within his group at the University of Washington. Their laboratory’s software (Skyline) is noted for its robustness, versatility and user friendliness, and has been adopted by all six major mass spectrometry vendors. Dr. MacCoss and his team continually work to improve their tools, provide documentation, and support a community around their software and methodology.

Dr. MacCoss has been working with mass spectrometry instrumentation since 1994. He obtained his Ph.D. in Analytical Chemistry with Professor Dwight Matthews at the University of Vermont in 2001, and completed his postdoctoral training with Professor John R. Yates III at The Scripps Research Institute. Professor MacCoss has been the recipient of several awards including the 2007 Presidential Award for Scientists and Engineers (PECASE), the 2015 Biemann Medal from the American Society for Mass Spectrometry, and the 2016 HUPO Award for Discovery in Proteomics Sciences.

Dr. Michael J. MacCoss has made a number of contributions of serious and long lasting impact to the field of proteomics. Chief among these is software development that has greatly facilitated proteomics. Dr. MacCoss’ philosophy on making software freely available and continually supporting this software so that it enables others has greatly benefitted the proteomic sciences. Bioinformatics tools developed by the MacCoss laboratory facilitate many different aspects of mass spectrometry data analysis. This includes tools for liquid chromatography mass spectrometry (LCMS) feature finding, spectrum library searching, peak detection, post-processors for peptide database searching, and more.

An important early contribution from his lab, the Percolator algorithm, improved peptide identifications from proteomic analyses through semi-supervised machine learning. Another high-impact contribution his laboratory is the development and continued support of an integrated set of software tools called Skyline. Critically, Skyline is a vendor-neutral toolset, thus enabling methods to be easily transferred and tested across labs, even those that utilize different instrument platforms. Dr. MacCoss has also substantially advanced the new area of data-independent MS analyses. His key contribution in this area has been to develop a multiplexed strategy to better isolate noise and improve signal detection and therefore sensitivity through observational coherence.