Literature DB >> 34395810

Extracting and Integrating Protein Localization Changes from Multiple Image Screens of Yeast Cells.

Alex X Lu1, Louis-Francois Handfield1, Alan M Moses1,2,3.   

Abstract

The evaluation of protein localization changes in cells under diverse chemical and genetic perturbations is now possible due to the increasing quantity of screens that systematically image thousands of proteins in an organism. Integrating information from different screens provides valuable contextual information about the protein function. For example, proteins that change localization in response to many different stressful environmental perturbations may have different roles than those that only change in response to a few. We developed, to our knowledge, the first protocol that permits the quantitative comparison and clustering of protein localization changes across multiple screens. Our analysis allows for the exploratory analysis of proteins according to their pattern of localization changes across many different perturbations, potentially discovering new roles by association.
Copyright © The Authors; exclusive licensee Bio-protocol LLC.

Entities:  

Keywords:  Cell biology; Cluster analysis; Computational biology; Image analysis; Protein localization; Proteomics; Unsupervised machine learning

Year:  2018        PMID: 34395810      PMCID: PMC8328619          DOI: 10.21769/BioProtoc.3022

Source DB:  PubMed          Journal:  Bio Protoc        ISSN: 2331-8325


  10 in total

1.  Java Treeview--extensible visualization of microarray data.

Authors:  Alok J Saldanha
Journal:  Bioinformatics       Date:  2004-06-04       Impact factor: 6.937

2.  Open source clustering software.

Authors:  M J L de Hoon; S Imoto; J Nolan; S Miyano
Journal:  Bioinformatics       Date:  2004-02-10       Impact factor: 6.937

3.  Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis.

Authors:  Yolanda T Chong; Judice L Y Koh; Helena Friesen; Supipi Kaluarachchi Duffy; Kaluarachchi Duffy; Michael J Cox; Alan Moses; Jason Moffat; Charles Boone; Brenda J Andrews
Journal:  Cell       Date:  2015-06-04       Impact factor: 41.582

Review 4.  High-Content Screening for Quantitative Cell Biology.

Authors:  Mojca Mattiazzi Usaj; Erin B Styles; Adrian J Verster; Helena Friesen; Charles Boone; Brenda J Andrews
Journal:  Trends Cell Biol       Date:  2016-04-22       Impact factor: 20.808

5.  CYCLoPs: A Comprehensive Database Constructed from Automated Analysis of Protein Abundance and Subcellular Localization Patterns in Saccharomyces cerevisiae.

Authors:  Judice L Y Koh; Yolanda T Chong; Helena Friesen; Alan Moses; Charles Boone; Brenda J Andrews; Jason Moffat
Journal:  G3 (Bethesda)       Date:  2015-04-15       Impact factor: 3.154

6.  Automated analysis of high-content microscopy data with deep learning.

Authors:  Oren Z Kraus; Ben T Grys; Jimmy Ba; Yolanda Chong; Brendan J Frey; Charles Boone; Brenda J Andrews
Journal:  Mol Syst Biol       Date:  2017-04-18       Impact factor: 11.429

7.  Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins.

Authors:  Alex X Lu; Yolanda T Chong; Ian Shen Hsu; Bob Strome; Louis-Francois Handfield; Oren Kraus; Brenda J Andrews; Alan M Moses
Journal:  Elife       Date:  2018-04-05       Impact factor: 8.140

8.  Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins.

Authors:  Louis-François Handfield; Yolanda T Chong; Jibril Simmons; Brenda J Andrews; Alan M Moses
Journal:  PLoS Comput Biol       Date:  2013-06-13       Impact factor: 4.475

9.  Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress.

Authors:  Johnny M Tkach; Askar Yimit; Anna Y Lee; Michael Riffle; Michael Costanzo; Daniel Jaschob; Jason A Hendry; Jiongwen Ou; Jason Moffat; Charles Boone; Trisha N Davis; Corey Nislow; Grant W Brown
Journal:  Nat Cell Biol       Date:  2012-07-29       Impact factor: 28.824

10.  An Unsupervised kNN Method to Systematically Detect Changes in Protein Localization in High-Throughput Microscopy Images.

Authors:  Alex Xijie Lu; Alan M Moses
Journal:  PLoS One       Date:  2016-07-21       Impact factor: 3.240

  10 in total

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