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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Updated: May 28, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Cloud-Enabled Scalable Analysis of Large Proteomics Cohorts.

Harendra Guturu¹, Andrew Nichols¹, Lee S Cantrell¹

¹Seer, Inc., Redwood City, California 94065, United States.

Journal of Proteome Research

|February 13, 2025

Summary

Scalable Match Between Runs (MBR) enables large-scale proteomic analysis by processing thousands of samples rapidly. This new algorithm achieves near-identical results to existing methods, enhancing data depth and completeness for cohort studies.

Keywords:

bioinformatics cloud search computational biology data-independent acquisition mass spectrometry match-between-runs population proteomics proteograph proteomics scalable computing

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Area of Science:

Proteomics
Computational Biology
Bioinformatics

Background:

Untargeted mass spectrometry enables large-scale proteomic and proteogenomic analyses.
Existing data infrastructure and search engines struggle to scale for cohort studies.
Library-free Match Between Runs (MBR) search enhances data depth but has scalability limitations.

Purpose of the Study:

To develop a scalable solution for Match Between Runs (MBR) search in large-scale proteomic data analysis.
To enable processing of thousands of individuals in cohort studies using MBR.
To enhance the throughput and resource scalability of proteomic data search engines.

Main Methods:

Deployment of search engines in a distributed cloud environment without source code modification.
Development of a novel algorithm, Scalable MBR, replicating DIA-NN's MBR procedure.
Testing scalability up to over 15,000 injections.

Main Results:

Scalable MBR can search thousands of MS raw files in hours, compared to days for the original DIA-NN MBR.
Results from Scalable MBR are nearly indistinguishable from DIA-NN native MBR.
Empirical spectra generated by Scalable MBR closely approximate DIA-NN native MBR.

Conclusions:

Scalable MBR significantly enhances the throughput and scalability of MBR-based proteomic data analysis.
The method preserves the accuracy and data quality of native MBR, supporting large cohort studies.
Scalable MBR is available within the Proteograph Analysis Suite for practical application.