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Related Concept Videos

Proteomics01:33

Proteomics

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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
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A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data.

Charlotte Hutchings1, Charlotte S Dawson1, Thomas Krueger2

  • 1Cambridge Centre for Proteomics, University of Cambridge, Cambridge, CB2 1QR, UK.

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|November 29, 2023
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Summary
This summary is machine-generated.

This study presents an R-based workflow for analyzing quantitative proteomics data, covering processing, differential expression analysis, and interpretation for both tandem mass tag (TMT) and label-free quantitation (LFQ) methods.

Keywords:
BioconductorQFeaturesbottom-up proteomicsdata processingdifferential expressionlimmamass spectrometryproteomicsquality controlshotgun proteomics

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Expression proteomics globally evaluates protein abundances.
  • Differential expression analysis identifies protein changes after system perturbations.
  • Quantitative mass spectrometry is key to modern proteomics.

Purpose of the Study:

  • To provide a comprehensive, step-by-step workflow for quantitative proteomics data analysis.
  • To guide users through processing, analysis, and interpretation using open-source R packages.
  • To demonstrate the workflow with a practical example using HEK293 cell treatment data.

Main Methods:

  • Utilized open-source R software packages from Bioconductor.
  • Developed a workflow for quantitative mass spectrometry-based expression proteomics.
  • Applied the workflow to both tandem mass tag (TMT) labeled cellular proteins and label-free quantitation (LFQ) of secreted proteins.
  • Included data import, pre-processing, quality control, statistical differential expression analysis, and gene ontology enrichment analysis.

Main Results:

  • The workflow details software infrastructure, data import, pre-processing, and quality control for TMT and LFQ datasets.
  • Demonstrated statistical differential expression analysis and interpretation through gene ontology enrichment.
  • Successfully applied the workflow to experimental data from HEK293 cells.

Conclusions:

  • A comprehensive and accessible workflow for expression proteomics data analysis is presented.
  • The workflow is a valuable resource for the proteomics community, especially beginners familiar with R.
  • Enables users to make data-driven decisions in their proteomics analyses.