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Updated: Jun 14, 2026

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

Published on: October 28, 2021

HTAPP: high-throughput autonomous proteomic pipeline.

Kebing Yu¹, Arthur R Salomon

¹Department of Chemistry, Brown University, Providence, RI 02903, USA.

|March 26, 2010

Summary

This summary is machine-generated.

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A new automated proteomic software platform streamlines complex sample analysis. This high-throughput pipeline enhances data processing, visualization, and biological meaning extraction from vast proteomic datasets.

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Rapid advancements in mass spectrometry and computational power have generated massive proteomic datasets.
Existing tools often lack integration, hindering efficient analysis of complex proteomic samples.

Purpose of the Study:

To develop a flexible, lab-based automated software platform for high-throughput proteomic data analysis.
To streamline the collection, processing, storage, and visualization of large-scale proteomic data.

Main Methods:

An integrated software pipeline controlling mass spectral data acquisition.
Automation of post-acquisition tasks including peptide quantification and MS/MS spectral database searching.
A user-configurable relational database for data storage and exploration.

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Last Updated: Jun 14, 2026

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

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Published on: October 28, 2021

Mapping Dysfunctional Protein-Protein Interactions in Disease

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Published on: October 24, 2025

Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)

Automated Sample Preparation for the Multiplexed Analysis of Single-Cell Histone Post-Translational Modifications (sc-hPTM2)

Published on: December 19, 2025

Main Results:

The platform accommodates diverse proteomic workflows with enhanced flexibility.
It addresses missing software functionality for efficient proteomic data analysis.
Accelerated extraction of biological insights from extensive proteomic datasets.

Conclusions:

The synergistic combination of software modules offers a novel, integrated approach to proteomic sample analysis.
This autonomous pipeline significantly improves the efficiency and accessibility of complex proteomic data interpretation.
The platform empowers researchers to derive greater biological meaning from proteomic studies.