Triqler Protein Quantification

Version: 1.0.3

Commit Hash: 70fd18d50505e97cc1b00d1bf9be63f057f4b5c9

Author: CauldronGO Team

Category: statistics

Subcategory: quantification

Protein quantification with integrated error propagation using Triqler. Propagates uncertainty from MS1 features through peptide to protein level using a probabilistic graphical model, producing posterior probabilities for differential expression. Based on: The & Käll (2019) Mol Cell Proteomics 18(3):561-570. Repository: https://github.com/statisticalbiotechnology/triqler

Install with Cauldron get_app
README

Triqler Protein Quantification

Installation

⬇️ Click here to install in Cauldron (requires Cauldron to be running)

Repository: https://github.com/noatgnu/triqler-plugin

Manual installation:

  1. Open Cauldron
  2. Go to Plugins → Install from Repository
  3. Paste: https://github.com/noatgnu/triqler-plugin
  4. Click Install

ID: triqler
Version: 1.0.2
Category: statistics
Author: CauldronGO Team

Description

Protein quantification with integrated error propagation using Triqler. Propagates uncertainty from MS1 features through peptide to protein level using a probabilistic graphical model, producing posterior probabilities for differential expression. Based on: The & Käll (2019) Mol Cell Proteomics 18(3):561-570. Repository: https://github.com/statisticalbiotechnology/triqler

Runtime

  • Environments: python

  • Entrypoint: triqler_runner.py

Inputs

Name Label Type Required Default Visibility
input_format Input Format select (triqler, diann, maxquant) Yes triqler Always visible
input_file Input File file Yes - Always visible
file_list_file Run Mapping File file No - Conditional
fold_change_eval Log2 Fold Change Threshold number (min: 0, max: 10, step: 0) No 1 Always visible
decoy_pattern Decoy Protein Prefix text No decoy_ Always visible
min_samples Minimum Samples number (min: 1, max: 20, step: 1) No 2 Always visible
missing_value_prior Missing Value Prior select (default, DIA) No default Always visible
num_threads Number of Threads number (min: 0, max: 64, step: 1) No 0 Always visible
use_ttest Use T-Test boolean No false Always visible
write_spectrum_quants Write Spectrum Quantifications boolean No false Always visible
write_protein_posteriors Write Protein Posteriors boolean No false Always visible
write_group_posteriors Write Group Posteriors boolean No false Always visible
write_fold_change_posteriors Write Fold Change Posteriors boolean No false Always visible

Input Details

Input Format (input_format)

Format of the input file. Select 'triqler' for pre-formatted files, 'diann' for DIA-NN report files, or 'maxquant' for MaxQuant evidence.txt

  • Options: triqler, diann, maxquant

Input File (input_file)

For triqler format: PSM file with columns (run, condition, charge, searchScore, intensity, peptide, proteins). For DIA-NN: report.tsv or report.parquet. For MaxQuant: evidence.txt

Run Mapping File (file_list_file)

Required for DIA-NN/MaxQuant: Tab-separated file (NO HEADER) mapping run names to conditions. For DIA-NN: run names must match the 'Run' column. For MaxQuant: must match 'Raw file' column without path. Columns: run, condition, [sample], [fraction]

Log2 Fold Change Threshold (fold_change_eval)

Log2 fold change evaluation threshold for differential expression

Decoy Protein Prefix (decoy_pattern)

Prefix used to identify decoy proteins in reversed database search

  • Placeholder: decoy_

Minimum Samples (min_samples)

Minimum number of peptide quantifications required per protein

Missing Value Prior (missing_value_prior)

Distribution fitting method for missing values. Use DIA for DIA data.

  • Options: default, DIA

Number of Threads (num_threads)

Number of CPU threads to use (0 = use all available cores)

Use T-Test (use_ttest)

Use t-test instead of Bayesian posterior probabilities for differential expression

Write Spectrum Quantifications (write_spectrum_quants)

Output consensus spectrum quantification data

Write Protein Posteriors (write_protein_posteriors)

Export protein posterior distributions to a separate file

Write Group Posteriors (write_group_posteriors)

Export treatment group posterior distributions to a separate file

Write Fold Change Posteriors (write_fold_change_posteriors)

Export fold change posterior distributions to a separate file

Outputs

Name File Type Format Description
protein_results proteins.tsv data tsv Protein-level quantification results with q-values, posterior error probabilities, fold change estimates, and gene names
condition_mapping condition_mapping.tsv data tsv Mapping between numerical condition IDs and original condition names
triqler_input triqler_input.tsv data tsv Converted triqler input file (only generated when using DIA-NN or MaxQuant format)
spectrum_quants spectrum_quants.tsv data tsv Consensus spectrum quantification data
protein_posteriors protein_posteriors.tsv data tsv Protein posterior distributions
group_posteriors group_posteriors.tsv data tsv Treatment group posterior distributions
fold_change_posteriors fold_change_posteriors.tsv data tsv Fold change posterior distributions

Requirements

  • Python Version: >=3.10

Python Dependencies (External File)

Dependencies are defined in: requirements.txt

  • triqler>=0.6.0
  • click>=8.0.0
  • pyarrow>=10.0.0

Note: When you create a custom environment for this plugin, these dependencies will be automatically installed.

Usage

Via UI

  1. Navigate to statistics → Triqler Protein Quantification
  2. Fill in the required inputs
  3. Click Run Analysis

Via Plugin System

const jobId = await pluginService.executePlugin('triqler', {
  // Add parameters here
});