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Ben Neely (Fed)

Ben Neely’s work is directly related to NIST’s core mission of supporting commerce by advancing and harmonizing measurement science as well as developing resources for stakeholders to accelerate research capabilities. His current research includes development of new materials for *omic applications and generating standardized proteomic data across non-model species as part of the CoMPARe Program (Comparative Mammalian Proteome Aggregator Resource). Acquiring high-quality *omic data from non-model organisms is especially exciting given the challenges of sequencing and annotating genomes, acquiring samples, and developing computational tools to compare results across species. His other primary focus is emerging proteomic applications, specifically working with stakeholders to optimize and standardize methods for data-independent acquisition and metaproteomic analysis. These projects are generating publicly available standardized methods, data, and data mining tools.

Publications

A multipathway phosphopeptide standard for rapid phosphoproteomics assay development

Author(s)
Brian Searle, Allis Chien, Antonius Koller, David Hawke, Anthony Herren, Jenny Kim, Kimberly Lee, Ryan Leib, Alissa Nelson, Jian Min Ren, Paul Stemmer, Yiying Zhu, Ben Neely, Bhavin Patel
Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and

Toward an Integrated Machine Learning Model of a Proteomics Experiment

Author(s)
Ben Neely, Viktoria Dorfer, Lennart Martens, Isabell Bludau, Robbin Bouwmeester, Sven Degroeve, Eric Deutsch, Siegfried Gessulat, Tobias Rehfeldt, Lukas Käll, Veit Schwämmle, Samuel Payne, Tobias Schmidt, Pawel Palczynski, Julian Uszkoreit, Juan Antonio Vizcaino, Mathias Wilhelm, Magnus Palmblad
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators
Created October 9, 2019, Updated December 8, 2022