TRIC

TRIC performs retention-time correction and automated cross-run alignment and quantification of targeted proteomics data to ensure consistent peptide quantification across LC-MS/MS runs generated by SWATH-MS and SRM.


Key Features:

  • Graph-Based Alignment Strategy: Employs a graph-based, non-linear retention time correction that integrates information across all experimental runs for precise peptide alignment.
  • Automated Peak-Picking and Quantification: Automates peak-picking and quantification across runs using fragment-ion data.
  • Fragment-Ion–Based Cross-Run Alignment: Utilizes fragment-ion signals to align peptide analytes across multiple LC-MS/MS runs for large-scale analyses.
  • Error Reduction and Nonlinear Chromatography Correction: Corrects highly non-linear chromatographic effects and has demonstrated a greater than threefold decrease in identification error at constant recall compared to analyses without alignment.
  • Application to Isotopic Peak Groups: Aligns and quantifies thousands of isotopic peak groups, as applied to pulsed-SILAC experiments on human induced pluripotent stem cells.

Scientific Applications:

  • SWATH-MS and SRM targeted proteomics: Improving consistency and accuracy of targeted proteomics workflows from SWATH-MS and SRM LC-MS/MS data.
  • High-throughput cross-run quantification: Enabling large-scale peptide quantification across many LC-MS/MS runs in high-throughput proteomics experiments.
  • Pulsed-SILAC studies in human iPSC: Quantifying isotopic peak groups in pulsed-SILAC experiments on human induced pluripotent stem cells.
  • Proteomic investigations of complex systems: Supporting studies of complex biological systems, disease mechanisms, and therapeutic targets by providing consistent cross-run quantification.

Methodology:

Performs graph-based non-linear retention time correction integrating data from all runs, uses fragment-ion data for automated peak-picking, cross-run alignment and quantification, and corrects highly non-linear chromatographic effects.

Topics

Collections

Details

Tool Type:
command-line tool
Operating Systems:
Linux
Programming Languages:
Python
Added:
8/3/2017
Last Updated:
11/25/2024

Operations

Publications

Röst HL, Liu Y, D'Agostino G, Zanella M, Navarro P, Rosenberger G, Collins BC, Gillet L, Testa G, Malmström L, Aebersold R. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nature Methods. 2016;13(9):777-783. doi:10.1038/nmeth.3954. PMID:27479329. PMCID:PMC5008461.

Documentation

Links