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Related Concept Videos

Proteomics01:33

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 proteomics...

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PROTEINCHALLENGE: crowd sourcing in proteomics analysis and software development.

Sarah F Martin1, Heiner Falkenberg, Thomas F Dyrlund

  • 1Kinetic Parameter Facility, Centre for Synthetic and Systems Biology-SynthSys, University of Edinburgh, UK.

Journal of Proteomics
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

Large-scale proteomics data analysis requires robust tools and best practices. This paper offers ten tips and calls for community challenges to establish data handling standards.

Keywords:
BenchmarkingCommunity challengeCrowd sourcingData analysisOpen sourceSoftware

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Large-scale proteomics studies generate vast datasets.
  • Existing data analysis tools may be inadequate or poorly implemented.
  • This can compromise scientific findings and hinder research progress.

Purpose of the Study:

  • To address critical concerns in large-scale proteomics data analysis.
  • To advocate for community-wide open-source software development.
  • To propose solutions for handling "big data" in proteomics.

Main Methods:

  • Compilation of ten practical tips for data processing in proteomics.
  • Discussion of the need for standardized operating procedures and best practices.
  • Proposal of a community-driven initiative, PROTEINCHALLENGE, for workflow comparison.

Main Results:

  • A set of ten actionable tips to guide initial large-scale proteomics analyses.
  • Identification of the urgent need for improved, robust analytical tools.
  • A framework for community-driven challenges to evaluate and standardize analysis workflows.

Conclusions:

  • Standardized, robust tools and best practices are essential for reliable proteomics research.
  • Community-wide open-source development and standardized challenges are crucial for advancing the field.
  • The proposed PROTEINCHALLENGE aims to establish a gold standard for proteomics data handling and sharing.