Software

I develop and maintain open-source software for quantitative proteomics, statistical modeling, and causal inference in biological systems. Much of this work is aimed at building practical, reproducible tools that make advanced methodology more accessible to experimental researchers.

MSstats ecosystem

I am a lead developer and maintainer of the MSstats ecosystem, a suite of statistical software for quantitative mass spectrometry-based proteomics. These tools support a wide range of experimental designs and are used in both academic and pharmaceutical research.

MSstats

Core statistical framework for differential abundance analysis in label-free quantitative proteomics, including normalization, summarization, modeling, and visualization.

Links: GitHub · Bioconductor · Website

MSstatsPTM

Statistical framework for relative quantification of post-translational modifications while accounting for changes in underlying protein abundance.

Links: GitHub · Bioconductor

MSstatsLiP

Workflow for limited proteolysis-mass spectrometry experiments to detect protein structural or binding-related changes across conditions.

Links: GitHub · Bioconductor

MSstatsTMT

Methods for isobaric labeling experiments such as TMT and iTRAQ, including channel normalization and design-aware statistical modeling.

Links: GitHub · Bioconductor

MSstatsShiny

Graphical interface for MSstats workflows, designed to make statistical analysis of proteomics experiments accessible to users without coding experience.

Links: GitHub · Bioconductor

MSstatsConvert

Collection of import and conversion tools that connect proteomics search and quantification outputs to downstream MSstats workflows.

Links: GitHub · Bioconductor

MSstatsBig

Tools for scalable analysis of large proteomics datasets that exceed the memory limits of standard workflows.

Links: GitHub

MSstatsResponse

Statistical framework for chemoproteomics and drug-response experiments.

Links: GitHub

MSstats+

Quality-aware extension of the MSstats framework that integrates longitudinal peak quality metrics into protein-level inference for large DIA proteomics studies.

Status: Manuscript under revision; page can be updated with a public software link when you are ready to expose it.

Causal inference software

Causomic

Python package for causal inference and perturbation-effect estimation in omics research. The framework integrates prior biological knowledge with Bayesian causal modeling to support interventional prediction in complex molecular systems.

Link: GitHub

Additional work

Omega_Implementations

A collection of biological models implemented in Julia using the Omega probabilistic programming language.

Link: GitHub