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Related Experiment Video

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Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

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Published on: June 16, 2014

SILACtor: software to enable dynamic SILAC studies.

Michael R Hoopmann1, Juan D Chavez, James E Bruce

  • 1Department of Genome Sciences, University of Washington, Seattle, Washington 98109-4717, United States.

Analytical Chemistry
|September 30, 2011
PubMed
Summary
This summary is machine-generated.

Stable isotope labeling by amino acids in cell culture (SILAC) is a powerful proteomics technique. A new software tool, SILACtor, automates protein turnover analysis, simplifying large-scale studies and improving data accuracy.

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Area of Science:

  • Proteomics
  • Computational Biology
  • Biochemistry

Background:

  • Stable isotope labeling by amino acids in cell culture (SILAC) is widely used for large-scale protein turnover studies.
  • Automated computational tools are needed to simplify the complex data analysis involved in SILAC experiments.
  • Existing software lacks algorithms for automated analysis of protein turnover data.

Purpose of the Study:

  • To develop and present SILACtor, a software tool for automated analysis of SILAC-based protein turnover data.
  • To profile protein turnover rates for a large number of proteins using SILAC technology.
  • To create automated methods for generating inclusion lists for targeted peptide analysis.

Main Methods:

  • Development of the SILACtor software for tracing and comparing SILAC-labeled peptides across time points.
  • Application of SILACtor to profile protein turnover rates in over 500 HeLa cell proteins using a SILAC label-chase approach.
  • Implementation of automated generation of accurate mass and retention time inclusion lists within SILACtor.

Main Results:

  • SILACtor successfully profiled protein turnover rates for over 500 HeLa cell proteins.
  • The software automates the generation of inclusion lists for peptides with varying turnover rates.
  • SILACtor facilitates improved analysis of protein turnover using SILAC.

Conclusions:

  • SILACtor provides an automated solution for analyzing protein turnover data generated by SILAC.
  • The software enhances the efficiency and accuracy of large-scale proteomics studies.
  • SILACtor offers a framework for future comparative SILAC analyses and targeted proteomics methods.