Devon Kohler

Devon Kohler

PhD candidate in computational biology, building statistical & causal methods for MS-based proteomics, with experience in RNA-seq and multi-omics integration.

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 2

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Blog Post number 1

less than 1 minute read

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portfolio

publications

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

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

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

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

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

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

Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach

Published in Bioinformatics, 2025

We propose a statistical approach for estimating protein abundances, as well as site occupancies of post-translational modifications, based on quantitative information from shared peptides.

Recommended citation: Mateusz Staniak, Ting Huang, Amanda M Figueroa-Navedo, **Devon Kohler**, Meena Choi, Trent Hinkle, Tracy Kleinheinz, Robert Blake, Christopher M Rose, Yingrong Xu, Pierre M Jean Beltran, Liang Xue, Małgorzata Bogdan, Olga Vitek, Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach, Bioinformatics, Volume 41, Issue 3, March 2025, btaf046, https://doi.org/10.1093/bioinformatics/btaf046. https://academic.oup.com/bioinformatics/article/41/3/btaf046/7994328

talks

MSstatsPTM: an R/Bioconductor software for detecting quantitative changes in post-translational modifications

Published:

The scientific community widely utilizes mass spectrometry (MS)-based proteomics to quantify the abundance of proteins and their post-translational modifications (PTMs). Experiments targeting PTMs face several specific challenges. These include the low abundance, few representative peptides, and convolution with abundance changes in the overall protein expression. Due to these challenges, a robust approach to estimate relative changes in PTMs should combine PTM sites over several peptides, replicates in multiple conditions, and consider sources of confounding present in the experiment. We propose a general statistical model and workflow that is both reproducible and comprehensive. The method measures PTM and protein abundance by summarizing intensities through Tukey’s median polish method. Then a model based on the family of linear mixed-effects models is fit. Finally, the PTM abundances are adjusted to remove variance from changes in the overall protein. The package can handle a diverse range of acquisition types, including label-free, DDA, DIA, and TMT. We implement this model in the free and open-source R package MSstatsPTM and available at the links below.

Explorations of causal probabilistic programming approaches for rule-based models of biological signaling pathways

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Rule-based models handle the complexity of biological signaling pathways. Biological signalling pathways are complex systems that underlie many cellular process and whose dysregulation is the source of many morbidities. To address the combinatorial complexity of interactions, patterns of transitions between model states can be compactly represented as probabilistic events using rule-based models.

MSstatsShiny: A Multipurpose UI for Reproducible Analysis of Quantitative Proteomic Experiments

Published:

Quantitative mass spectrometry proteomic experiments require robust analytic and modeling techniques to ensure the results are correctly interpreted. There are a variety of different tools that can assist researchers in analysis, however they are generally only suited for one type of experimental design and are usually implemented in coding packages. To address these challenges, we have created the UI MSstats-Shiny, an RShiny based UI integrated with the packages Msstats, MSstatsTMT, and MSstatsPTM providing all users an end-to-end pipeline that can analyze a variety of experimental designs.

Single-cell mass spectrometry-based proteomics enables causal inference in observational studies

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Single cell proteomics greatly increases the number of replicates and cellular resolution in MS-based proteomic experiments. However, these experiments are expensive (especially when targeting perturbations). Causal inference methods, which are typically challenging to apply to traditional bulk-MS due to the lack of replicates, allow us to estimate the impact of perturbations from purely observational data. We propose a method and workflow for predicting the effect of interventions in observational single cell MS experiments and apply the workflow to a recent observational single cell experiment.

Analysis of Quantitative MS-based Proteomics Experiments using MSstats and MSstatsTMT

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Recent advances in the techniques and technologies used in Mass Spectrometry (MS)-based proteomics have greatly increased the variety and complexity of experimental designs in the field. Experiments can differ in the labeling method (label-free vs tandem mass tag), acquisition type (DDA/DIA/SRM/PRM), biological question of interest, and differing numbers of conditions and replicates. Statistical methods used to analyze these experiments need to be flexible enough to fit these design complexities, while being robust enough to not overfit to one specific design. The MSstats family of R packages is widely used to analyze the results of MS-based proteomics experiments and has been shown to outperform other methods on a variety of experimental designs. In this webinar we will review the core workflow and methods behind MSstats and its extensions, highlighting critical choices that need to be made in each step of the analysis. We will review how different analysis choices affect the final protein-level conclusions made on the experiment and discuss how to avoid potential analysis pitfalls. After reviewing the methods in MSstats, we will present a hands-on session using MSstatsShiny, where participants are provided with two datasets (label-free DIA and TMT-DDA) and are invited to follow along.

Data Independent Acquisition: After the Acquisition

Published:

This panel discussed topics related to mass spectrometry(MS)-based proteomics experiments using data independent acquisition (DIA). Specifically, it focused on what to do with the data resulting from these experiments. This included indentification and quantificaiton of mass spectra, as well as post id and quantificaiton processing of the data.

Bayesian statistical modeling reveals missing value mechanisms in label-free Mass Spectrometry-based proteomics experiments

Published:

This talk reviewed bayesian statistical modeling strategies for upstream data processing of MS-based proteomics experiments. This included missing value imputation and summarization of peptide ions to the protein-level. These processing steps were performed with a bayesian model which allowed error propogation at each step. The final summarized values were used for differential analysis in a error-in-variance model.

Single cell mass spectrometry (MS)-based proteomics coupled with suitable experimental design enables the application of causal inference methods to observational experiments.

Published:

This talk explored the integration of new single cell MS-based proteomics methods and causal inference. We argued that single cell MS-based proteomics enables estimating the effect of perturbations from observational studies. These methods were shown on a simulation and real world single cell experiment.

Enhanced usability in the MSstats family of statistical analysis software for quantitative mass spectrometry(MS)-based proteomics

Published:

This work presented new usability and infrastructure enhancements to the MSstats family of tools for statistical analysis of quantitative MS-based proteomics. The updates included expanded data converters (e.g., for DIA-NN, FragPipe, MetaMorpheus), scalable workflows for large datasets via MSstatsBig, improved support for planning TMT-labeled experiments, and new interactive visualization features. These developments reduced the coding burden on users, helped prevent common analytical mistakes, and make advanced statistical workflows more accessible, enabling accurate and efficient detection of differentially abundant proteins across diverse experimental designs.

Causal inference enables the estimation of outcomes of interventions from observational mass spectrometry (MS)-based proteomics experiments

Published:

We developed a new method that brings causal inference to mass spectrometry (MS)-based proteomics, allowing us to estimate the effects of interventions—like drug treatments—without having to run the experiments. By combining Bayesian modeling with biological knowledge graphs from the INDRA database, and tackling challenges like noise, missing values, and irrelevant network connections, this approach predicts how proteins respond to perturbations directly from observational data. I validated it on both simulated and real experiments, showing that it can outperform neural networks in predicting protein responses, including in studies of drug effects on transcription factor activity.

Integration of longitudinal quality metrics enhances differential analysis in noisy large-scale Mass Spectrometry(MS)-based proteomics experiments

Published:

This work presents a method for improving differential analysis in large-scale, noisy DIA-based proteomics experiments by integrating longitudinal quality metrics into an Isolation Forest anomaly detection framework. By identifying poor-quality peptide-spectrum matches using metrics like shape quality score and fragment noise, and incorporating these scores into weighted regression, the approach corrects low-quality and missing measurements, leading to more accurate and reliable detection of differential abundance compared to conventional methods.

Single-Cell Proteomics Workshop

Published:

This evening workshop at ASMS 2025 focused on key topics in single-cell proteomics by mass spectrometry (MS), including labeled vs. label-free approaches, data analysis strategies, balancing throughput and depth, and the future of the field. I was invited to serve as a panel member, contributing expert insights on data analysis. The discussion was audience-driven, with prioritized topics addressed in a panel format chaired by the organizers.

teaching

Algorithms

Teachers Assistant, Northeastern University, 2019

Teacher’s Assistant for master’s level algorithms class.

Quantitative Proteomics: Case Studies

Two-day Short Course, ASMS 2022, 2022

In Case study 1, we analyzed an SRM proteomics dataset from a case-control study of heart failure using a salt-sensitive rat model, which included biological and technical replicates. Participants explored the importance of study design (in particular of normalization and randomization), the importance of visualizing chromatograms (and signal interferences), and setting up, conducting and interpreting the results of statistical analyses in MSstats.

Quantitative Proteomics: Case Studies

Two-day Short Course, ASMS 2023, 2023

In Case study 1, we analyzed an SRM proteomics dataset from a case-control study of heart failure using a salt-sensitive rat model, which included biological and technical replicates. Participants explored the importance of study design (in particular of normalization and randomization), the importance of visualizing chromatograms (and signal interferences), and setting up, conducting and interpreting the results of statistical analyses in MSstats.

Quantitative Proteomics: Case Studies

Two-day Short Course, ASMS 2024, 2024

In Case study 1, we analyzed an SRM proteomics dataset from a case-control study of heart failure using a salt-sensitive rat model, which included biological and technical replicates. Participants explored the importance of study design (in particular of normalization and randomization), the importance of visualizing chromatograms (and signal interferences), and setting up, conducting and interpreting the results of statistical analyses in MSstats.

Statistical Analysis of Targeted Proteomics Experiments

Short Course, UW Targeted Mass Spectrometry Course, 2024

I taught a short course on quantitative proteomics, covering key concepts from standards, calibration, and statistical considerations in study design to statistical analysis of proteomics experiments. The course also included hands-on training in Panorama for data management and visualization, giving participants both the theoretical background and practical tools to design, analyze, and interpret quantitative proteomics studies with confidence.

Quantitative Proteomics: Case Studies

Two-day Short Course, ASMS 2025, 2025

In Case study 1, we analyzed an SRM proteomics dataset from a case-control study of heart failure using a salt-sensitive rat model, which included biological and technical replicates. Participants explored the importance of study design (in particular of normalization and randomization), the importance of visualizing chromatograms (and signal interferences), and setting up, conducting and interpreting the results of statistical analyses in MSstats.