Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Comparative analysis of protein abundance studies to quantify the Saccharomyces cerevisiae proteome

Brandon Ho, Anastasia Baryshnikova, View ORCID ProfileGrant W. Brown
doi: https://doi.org/10.1101/104919
Brandon Ho
1Department of Biochemistry and Donnelly Center, University of Toronto, Ontario M5S 1A8, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anastasia Baryshnikova
2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Grant W. Brown
1Department of Biochemistry and Donnelly Center, University of Toronto, Ontario M5S 1A8, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Grant W. Brown
  • For correspondence: grant.brown@utoronto.ca
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Global gene expression and proteomics tools have allowed large-scale analyses of the transcriptome and proteome in eukaryotic cells. These tools have enabled studies of protein abundance changes that occur in cells under stress conditions, providing insight into regulatory programs required for cellular adaptation. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukary-ote, each of the existing datasets is separate and reported in different units. A comparison of all the available protein abundance data sets is key towards developing a complete understanding of the yeast proteome. We evaluated 19 quantitative proteomic analyses performed under normal and stress conditions and normalized and converted all measurements of protein abundance into absolute molecules per cell. Our analysis yields an estimate of the cellular abundance of 97% of the proteins in the yeast proteome, as well as an assessment of the variation in each abundance measurement. We evaluate the variance and sensitivity associated with different measurement methods. We find that C-terminal tagging of proteins, and the accompanying alterations to the 3’ untranslated regions of the tagged genes, has little effect on protein abundance. Finally, our normalization of diverse datasets facilitates comparisons of protein abundance remodeling of the proteome during cellular stresses.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted February 02, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Comparative analysis of protein abundance studies to quantify the Saccharomyces cerevisiae proteome
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Comparative analysis of protein abundance studies to quantify the Saccharomyces cerevisiae proteome
Brandon Ho, Anastasia Baryshnikova, Grant W. Brown
bioRxiv 104919; doi: https://doi.org/10.1101/104919
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Comparative analysis of protein abundance studies to quantify the Saccharomyces cerevisiae proteome
Brandon Ho, Anastasia Baryshnikova, Grant W. Brown
bioRxiv 104919; doi: https://doi.org/10.1101/104919

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4237)
  • Biochemistry (9154)
  • Bioengineering (6796)
  • Bioinformatics (24050)
  • Biophysics (12149)
  • Cancer Biology (9560)
  • Cell Biology (13811)
  • Clinical Trials (138)
  • Developmental Biology (7650)
  • Ecology (11728)
  • Epidemiology (2066)
  • Evolutionary Biology (15532)
  • Genetics (10662)
  • Genomics (14344)
  • Immunology (9500)
  • Microbiology (22873)
  • Molecular Biology (9113)
  • Neuroscience (49077)
  • Paleontology (357)
  • Pathology (1487)
  • Pharmacology and Toxicology (2575)
  • Physiology (3851)
  • Plant Biology (8347)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2299)
  • Systems Biology (6202)
  • Zoology (1302)