SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods

SUMMARY With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses. AVAILABILITY AND IMPLEMENTATION SILVER and its source codes are freely available under the GNU General Public License v3.0 at http://bioinfo.hupo.org.cn/silver.

[1]  M. Mann,et al.  Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics* , 2002, Molecular & Cellular Proteomics.

[2]  Tao Xu,et al.  Toward objective evaluation of proteomic algorithms , 2012, Nature Methods.

[3]  Alexey I Nesvizhskii,et al.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. , 2002, Analytical chemistry.

[4]  Liwei Li,et al.  PepDistiller: A quality control tool to improve the sensitivity and accuracy of peptide identifications in shotgun proteomics , 2012, Proteomics.

[5]  John R Yates,et al.  Mass spectrometry in high-throughput proteomics: ready for the big time , 2010, Nature Methods.

[6]  Sung Kyu Park,et al.  A quantitative analysis software tool for mass spectrometry–based proteomics , 2008, Nature Methods.

[7]  Hongwei Xie,et al.  Evaluation of empirical rule of linearly correlated peptide selection (ERLPS) for proteotypic peptide‐based quantitative proteomics , 2014, Proteomics.

[8]  Guanghui Wang,et al.  Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: Reproducibility, linearity, and application with complex proteomes. , 2006, Journal of proteome research.

[9]  M. Mann,et al.  Andromeda: a peptide search engine integrated into the MaxQuant environment. , 2011, Journal of proteome research.

[10]  M. Mann,et al.  MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. , 2010, Journal of proteome research.

[11]  Stephen J. Callister,et al.  Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. , 2006, Journal of proteome research.

[12]  Borjana Arsova,et al.  Precision, Proteome Coverage, and Dynamic Range of Arabidopsis Proteome Profiling Using 15N Metabolic Labeling and Label-free Approaches , 2012, Molecular & Cellular Proteomics.

[13]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[14]  Yunhu Wan,et al.  IsoQuant: a software tool for stable isotope labeling by amino acids in cell culture-based mass spectrometry quantitation. , 2012, Analytical chemistry.