Devon Kohler

Devon Kohler

PhD student at Northeastern University. Studying causal and statistical inference applications to mass spectrometry-based proteomics.

Publications

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Published in Nature Protocols, 2024

This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows.

Recommended citation: Kohler, D., Staniak, M., Yu, F. et al. An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing. Nat Protoc (2024). https://www.nature.com/articles/s41596-024-01000-3

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

Published in Journal of Proteome Research, 2023

Here, we introduce MSstats version 4.0 (v4.0), a statistical methodology and core package in the family of R/Bioconductor packages designed for statistical analysis of experiments with chromatography-based quantification.

Recommended citation: Kohler D, et al. MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale. J Proteome Res. 2023 May 5;22(5):1466-1482. https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00834

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

Published in Journal of Proteome Research, 2023

To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM.

Recommended citation: Kohler D, et al. MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments. J Proteome Res. 2023 Feb 3;22(2):551-556. https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00603

MSstatsPTM: Statistical Relative Quantification of Post-translational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

Published in Molecular & Cellular Proteomics, 2023

This manuscript proposes a versatile statistical analysis framework that accurately detects relative changes in PTMs.

Recommended citation: Kohler D, et al. MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics. Mol Cell Proteomics. 2023 Jan;22(1):100477. https://www.mcponline.org/article/S1535-9476(22)00285-7/fulltext#secsectitle0020

Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications

Published in Nature Protocols, 2022

We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins.

Recommended citation: Malinovska, L., Cappelletti, V., Kohler, D. et al. Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications. Nat Protoc 18, 659–682 (2023). https://www.nature.com/articles/s41596-022-00771-x

Recent Developments in MSstats Ecosystem: A collection of statistical methods for general scalable quantitative analysis of proteomic experiments.

Published in HUPO, 2022

The MSstats ecosystem is a family of open-source R/Bioconductor packages implementing statistical methods for quantitative mass spectrometry-based proteomic experiments. Here we review its recent developments, as well as advances in previously available methods and implementations.

Recommended citation: Kohler, D., Stankiak, M., & Vitek, O. (2022). Recent Developments in MSstats Ecosystem: A collection of statistical methods for general scalable quantitative analysis of proteomic experiments. HUPO News. https://hupo.org/News/12904194