RIANA - Relative Isotope Abundance Analyzer

RIANA enables protein turnover data analysis from mass spectrometry-based proteomics experiments using metabolic heavy water (D2O) labeling, with DDA (quantms) and DIA (DIA-NN) identification intake.

Author

Edward Lau

Published

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Abstract

This page provides the documentation for the RIANA package, which is a tool for the analysis of stable isotope labeling experiments for protein turnover measurements.

Keywords

Protein turnover, Proteomics, Mass spectrometry

Edward Lau Lab, University of Colorado School of Medicine
Maggie Lam Lab, University of Colorado School of Medicine

About RIANA

RIANA (Relative Isotope Abundance Analyzer) is a software to automate the analysis of mass spectrometry- based protein turnover measurement experiments.

RIANA quantifies protein turnover from metabolic heavy water (D₂O) labeling experiments. The MS1 isotopomer integration is label-agnostic, while kinetic fitting is currently D₂O-only (¹⁸O support is in progress; the earlier amino-acid / SILAC fitting path was retired in 1.0.0).

RIANA is orchestration-agnostic: peptide identification is owned upstream by established tools (quantms for DDA, DIA-NN for DIA), and RIANA is a linear integrate → fit → rollup pipeline — MS1 peak integration, per-peptidoform kinetic fitting, and protein-level roll-up with optional cross-condition turnover (Δk) testing. Both a command-line interface and a desktop GUI (riana gui) are provided.

Downloads

Latest Updates

v1.0.0

Major rewrite: typed streaming pipeline, apex-window peak detection, IsoSpec forward-model fractional synthesis.
New riana rollup for protein-level turnover + a cross-condition Δk test (--model "linear simple").
SDRF + manifest project workflow; quantms mzTab (DDA) and DIA-NN parquet (DIA) intake; gated match-between-runs (--mbr).
New PySide6 desktop GUI: riana gui.

v0.9.0

Stabilization release; mass-tolerance now means ±N ppm.

See Change Log for details.

The latest version and source code of RIANA can be found on github: https://github.com/ed-lau/riana.

See the Quick Start and Documentation for instructions.

Contributors

Edward Lau, PhD - ed-lau
Maggie Lam, PhD - Maggie-Lam
Jordan Currie, MSc - jordancurrie

Citations

If you use RIANA or the associated methods in your research, please consider citing the following papers:

Original publication, heavy water and SILAC comparison:

Harmonizing Labeling and Analytical Strategies to Obtain Protein Turnover Rates in Intact Adult animals Hammond DE, Simpson DM, Franco C, Wright Muelas M, Wasters J, Ludwig RW, PRescott MC, Hurst JL, Beynon RJ, E Lau Molecular & Cellular Proteomics 2022, 100252 doi:10.1016/j.mcpro.2022.100252 Epub 2022 May 28. PMID: 35636728; PMCID: PMC9249856.

Rule-based mass isotopomer selection method for heavy water:

Improved Method to Determine Protein Turnover Rates with Heavy Water Labeling by Mass Isotopomer Ratio Selection Currie J, Ng DCM, Pandi B, Black A, Manda V, Durham C, Pavelka J, Lam MPY, Lau E. Journal of Proteome Research 2025 Apr 4;24(4):1992-2005. doi: 10.1021/acs.jproteome.4c01012 Epub 2025 Mar 18. PMID: 40100644; PMCID: PMC11977540.

Application of heavy water labeling to cell culture:

Deuterium Labeling Enables Proteome Wide Turnover Kinetics Analysis in Cell Culture Alamillo L, Ng DCM, Currie J, Black A, Pandi B, Manda V, Pavelka J, Schaal P, Travers JG, McKinsey TA, Lam MPY, Lau E. Cell Rep Methods 2025 Jul 21;5(7):101104. doi: 10.1016/j.crmeth.2025.101104. Epub 2025 Jul 10. PMID: 40645189; PMCID: PMC12296481.