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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Cox J, Mann M (2007) Is proteomics the new genomics? Cell 130:395–398
Cox J, Mann M (2011) Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 80:273–299
de Godoy LM, Olsen JV, Cox J et al (2008) Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455:1251–1254
Ong SE, Mann M (2006) A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat Protoc 1:2650–2660
Ong SE, Blagoev B, Kratchmarova I et al (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–386
Mann M (2006) Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol 7:952–958
Geiger T, Wisniewski JR, Cox J et al (2011) Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat Protoc 6:147–157
Geiger T, Cox J, Ostasiewicz P et al (2010) Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7:383–385
Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372
Cox J, Mann M (2009) Computational principles of determining and improving mass precision and accuracy for proteome measurements in an Orbitrap. J Am Soc Mass Spectrom 20:1477–1485
Cox J, Neuhauser N, Michalski A et al (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10:1794–1805
Cox J, Mann M (2012) 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinform 13(Suppl 16):S12
Luber CA, Cox J, Lauterbach H et al (2010) Quantitative proteomics reveals subset-specific viral recognition in dendritic cells. Immunity 32:279–289
Cox J, Michalski A, Mann M (2011) Software lock mass by two-dimensional minimization of peptide mass errors. J Am Soc Mass Spectrom 22:1373–1380
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Tyanova, S., Mann, M., Cox, J. (2014). MaxQuant for In-Depth Analysis of Large SILAC Datasets. In: Warscheid, B. (eds) Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC). Methods in Molecular Biology, vol 1188. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1142-4_24
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DOI: https://doi.org/10.1007/978-1-4939-1142-4_24
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Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-1141-7
Online ISBN: 978-1-4939-1142-4
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