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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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Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0.

Eric W Deutsch1, Lydie Lane2, Christopher M Overall3

  • 1Institute for Systems Biology , Seattle , Washington 98109 , United States.

Journal of Proteome Research
|October 11, 2019
PubMed
Summary
This summary is machine-generated.

The Human Proteome Project updated its Mass Spectrometry Data Interpretation Guidelines to version 3.0. These revised guidelines improve accuracy and expand coverage for the complete human proteome.

Keywords:
B/D-HPPC-HPPHPPHuman Proteome ProjectUniversal Spectrum Identifier (USI)false-discovery ratesguidelinesmass spectrometrystandardsunicity checker

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Area of Science:

  • Proteomics
  • Genomics
  • Biochemistry

Background:

  • The Human Proteome Organization (HUPO) established Mass Spectrometry (MS) Data Interpretation Guidelines in 2016.
  • These guidelines ensured high accuracy and low false positives in identifying proteins for the human proteome draft.

Purpose of the Study:

  • To describe the updated version 3.0 of the HUPO Human Proteome Project (HPP) MS Data Interpretation Guidelines.
  • To address identified gaps and incorporate new MS technologies and data resources.

Main Methods:

  • Consensus-reaching discussions within the HPP community over the past year.
  • Incorporation of guidelines for data independent acquisition (DIA) MS workflows and the Universal Spectrum Identifier (USI) system.
  • Updates to the HPP pipeline for collecting MS evidence, including minimum evidence refinements and integration of MassIVE-KB.

Main Results:

  • Revised guidelines (version 3.0) address major and minor gaps in MS data interpretation.
  • New guidelines support emerging DIA MS workflows and the USI system.
  • Updated HPP pipeline and integration of MassIVE-KB aim for more comprehensive public MS data coverage.

Conclusions:

  • The updated version 3.0 guidelines will aid the HPP in its goal of curating MS evidence for all human proteins.
  • These revisions enhance the accuracy and completeness of the human proteome dataset.