WEHI Bioinformatics and Computational Biology Special Seminar hosted by Dr Mengbo Li  

Associate Professor Uri Keich

School of Mathematics and Statistics – University of Sydney

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

Davis Auditorium

Join via ZOOM

Including Q&A session

A critical challenge in mass spectrometry proteomics is accurately assessing error control, especially given that software tools employ distinct methods for reporting errors. Many tools are closed-source and poorly documented, emphasizing the need for independent validation.

In our recent work we analyzed the three prevalent entrapment-based estimation methods for validating false discovery rate (FDR) control using entrapment experiments. We found that one is invalid, one provides only a lower bound on errors, and one is valid but often under-powered.

As an alternative we developed a new estimation method that is valid but more powerful at certifying tools that genuinely control the FDR.

An estimation method is only one half of an entrapment experiment, the other being the entrapment sequences. We will discuss how different entrapment constructions change the results and how we interpret them. Moreover, this raises the intriguing question of what FDR in this context really means.

Joint work with Bo Wen, Jack Freestone, Michael Riffle, Michael J. MacCoss, William S. Noble

Uri Keich is an Associate Professor in the School of Mathematics and Statistics at the University of Sydney. He received his PhD in Mathematics from the Courant Institute, New York University, and held positions at Caltech (Applied Math), UC Riverside (Math), UC San Diego (CS), and Cornell University (CS) before joining the Statistics group in Sydney in 2009. His research develops statistical methods and computationally efficient algorithms, with a particular focus on false discovery rate (FDR) control in multiple hypothesis testing and its application to tandem mass spectrometry proteomics. His publications span pure mathematics (Communications on Pure and Applied Mathematics), statistics (Journal of the American Statistical Association), and computational statistics (Journal of Computational and Graphical Statistics), through to Nature Methods. His work has also appeared at the leading computational-biology conferences RECOMB and ISMB. In 2025 he was appointed Faculty of Science Academic Lead for AI and Assessment. His awards include an NSF CAREER Award, a RECOMB Best Paper Award, and the Faculty of Science Award for Teaching and Learning Excellence.

All welcome!