MaxQuant for In-Depth Analysis of Large SILAC Datasets

Tyanova, Stefka; Mann, Matthias; Cox, Jürgen

doi:10.1007/978-1-4939-1142-4_24

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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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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-4598-B Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-4599-9

Genre: Book Chapter

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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: Published in Print: 2014

Publication Status: Published in print

Pages: 14

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Table of Contents: -

Rev. Type: Peer

Identifiers: ISI: 000344103800025
DOI: 10.1007/978-1-4939-1142-4_24

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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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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