Mass Dynamics

Mass Dynamics analyzes label-free quantification (LFQ) data from shotgun proteomics experiments to quantify relative protein abundance and support downstream proteomic analyses.


Key Features:

  • Cloud-Based Architecture: Centralized cloud infrastructure for storage and processing of large-scale LFQ proteomics data.
  • MaxQuant Integration: Imports LFQ output from MaxQuant for downstream analysis.
  • Automated Processing and Visualization: Automates LFQ data processing and generates visualizations.
  • Quality Control Reporting: Generates comprehensive quality control reports to assess experiment integrity.
  • Standardized Workflows: Applies standardized analysis workflows with minimal manual parameterization to promote reproducibility.
  • Benchmark Performance: Produces quantification results comparable to Perseus on benchmark datasets across a wide dynamic range.

Scientific Applications:

  • Comparative Label-Free Proteomics: Quantification and comparison of relative protein abundances across biological samples using LFQ data.
  • Pathway and Gene Set Enrichment: Enables pathway and gene set enrichment analyses from quantified proteomes.
  • Proteomic Quality Control and Benchmarking: Assessment and benchmarking of LFQ dataset quality and quantification performance.

Methodology:

Automated processing of LFQ data (including import from MaxQuant), generation of visualizations and quality control reports, and centralized cloud-based storage and processing.

Details

Added:
1/18/2024
Last Updated:
1/18/2024

Operations

Data Inputs & Outputs

Publications

Bloom J, Triantafyllidis A, Quaglieri A, Burton Ngov P, Infusini G, Webb A. Mass Dynamics 1.0: A Streamlined, Web-Based Environment for Analyzing, Sharing, and Integrating Label-Free Data. Journal of Proteome Research. 2021;20(11):5180-5188. doi:10.1021/acs.jproteome.1c00683. PMID:34647461.

Quaglieri A, Bloom J, Triantafyllidis A, Green B, Condina MR, Ngov PB, Infusini G, Webb AI. Mass Dynamics 2.0: An improved modular web-based platform for accelerated proteomics insight generation and decision making. Unknown Journal. 2022. doi:10.1101/2022.12.12.517480.