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HyperQuant-A Computational Pipeline for Higher Order Multiplexed Quantitative Proteomics.

Suruchi Aggarwal1,2,3, Ajay Kumar1, Shilpa Jamwal1

  • 1Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad 121001, Haryana, India.

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Summary
This summary is machine-generated.

Quantitative proteomics now uses higher-order multiplexing (HOM) for higher throughput. A new pipeline, HyperQuant, accurately quantitates complex HOM data, addressing current computational challenges in the field.

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Area of Science:

  • Proteomics
  • Biotechnology
  • Computational Biology

Background:

  • Quantitative proteomics has advanced with higher-order multiplexing (HOM) techniques.
  • Methods like hyperplexing and BONPlex enable higher sample throughput by combining MS1 and MS2 labels.
  • Current computational tools struggle to fully analyze complex HOM data.

Purpose of the Study:

  • To develop a computational pipeline, HyperQuant, for accurate quantitation of complex higher-order multiplexing (HOM) data.
  • To address the limitations of existing tools in analyzing multiplexed quantitative proteomics data.
  • To provide a flexible solution for integrating various MS1 and MS2 labels in quantitative proteomics.

Main Methods:

  • Developed HyperQuant, a quantitative pipeline integrating identification results (MaxQuant or other engines) and quantitation results (QuantWizIQ).
  • Incorporated Mapper and Combiner modules for integrating labeled data and peptide spectrum match (PSM) intensity/ratio.
  • Enabled robust quantitation through appropriate combination of replicates/fractions before summarizing protein intensity/ratio.

Main Results:

  • HyperQuant provides accurate quantitation for complex HOM data.
  • The pipeline demonstrated utility in analyzing two 18-plex datasets from hyperplexing and BONplex studies.
  • It is the first tool offering flexible quantitation for any combination of MS1 and MS2 labels in HOM data.

Conclusions:

  • HyperQuant is a valuable open-source tool for advancing multiplexed quantitative proteomics.
  • It overcomes computational challenges in analyzing higher-order multiplexing data.
  • The pipeline facilitates robust and flexible quantitation, improving data analysis efficiency.