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  MaxQuant for In-Depth Analysis of Large SILAC Datasets

Tyanova, S., Mann, M., & Cox, J. (2014). MaxQuant for In-Depth Analysis of Large SILAC Datasets. In Methods in Molecular Biology; Vol. 1188 (pp. 351-364). 999 RIVERVIEW DR, STE 208, TOTOWA, NJ 07512-1165 USA: HUMANA PRESS INC.

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 Creators:
Tyanova, Stefka1, Author              
Mann, Matthias2, Author              
Cox, Jürgen1, Author              
Affiliations:
1Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_2063284              
2Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              

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Free keywords: QUANTITATIVE PROTEOMICS; CELL-CULTURE; AMINO-ACIDSComputational proteomics; Protein quantification; Peptide quantification; Experimental design; Large-scale data analysis;
 Abstract: Proteomics experiments can generate very large volumes of data, in particular in situations where within one experimental design many samples are compared to each other, possibly in combination with pre-fractionation of samples prior to LC-MS analysis. Here we provide a step-by-step protocol explaining how the current MaxQuant version can be used to analyze large SILAC-labeling datasets in an efficient way.

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Language(s): eng - English
 Dates: 2014
 Publication Status: Published in print
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: Methods in Molecular Biology; Vol. 1188
  Alternative Title : STABLE ISOTOPE LABELING BY AMINO ACIDS IN CELL CULTURE (SILAC): METHODS AND PROTOCOLS
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Source Genre: Series
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Publ. Info: 999 RIVERVIEW DR, STE 208, TOTOWA, NJ 07512-1165 USA : HUMANA PRESS INC
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 351 - 364 Identifier: ISSN: 1064-3745
ISBN: 978-1-4939-1142-4; 978-1-4939-1141-7