Using an accurate mass and time tag strategy 249

Joshua N. Adkins, Matthew E. Monroe, Kenneth J. Auberry, Yufeng Shen, Jon M.Jacobs, David G. Camp II, Frank Vitzthum, Karin D. Rodland, Richard, C. Zangar, Richard D. Smith and Joel G. Pounds

11.1 Introduction 250

11.2 Materials and methods 251

11.2.1 Human blood serum and plasma 251

11.2.2 Depletion of Igs and trypsin digestion 252

11.2.3 Peptide cleanup 252

11.2.4 Capillary RP-LC 253

11.2.6 SEQUEST identification of peptides 254

11.2.7 Putative mass and time tag database from SEQUEST results 254

11.2.8 FT-ICR-MS 255

11.2.9 cLC-FT-ICR MS data analysis 255

11.2.10 OmniViz cluster and visual analysis 257

11.3 Results 257

11.3.1 PuMT tag database 257

11.3.2 Summary of peptide/protein identifications by AMT tags 258

11.3.3 Protein concentration estimates from ion current 260

11.3.4 Global protein analysis 263

11.4 Discussion 264

11.4.1 Application of FT-ICR MS as a proteomic technology bridge 264

11.4.2 Confidence in any MS-based proteomic approach 266

11.4.3 Peptide/protein redundancy 267

11.4.4 Identification sensitivity versus specificity 267

11.4.5 Throughput and differential analysis 269

11.5 References 270

12 Analysis of Human Proteome Organization Plasma Proteome Project (HUPO PPP) reference specimens usingsurface enhanced laserdesorption/ ionization-time of flight (SELDI-TOF) mass spectrometry: Multi-institution correlation of spectra and identification of biomarkers 273

Alex J. Rai, Paul M. Stemmer, Zhen Zhang, Bao-lingAdam, William T. Morgan, Rebecca E. Cajjrey, Vladimir N. Podust, Manisha Patel, Lih-yin Lim, Natalia V. Shipulina, Daniel W. Chan, O.John Semmes and Hon-chiu Eastwood Leung

12.1 Introduction 273

12.2 Materials and methods 275

12.2.1 Sample preparation 275

12.2.2 Sample preprocessing 275

12.2.3 Target (CM10) chip preparation and sample incubation 275

12.2.4 Scanning protocol 276

12.2.5 Data processing 276

12.2.6 Bioinformatics analysis of data and correlation coefficient matrix 276

12.2.7 Protein purification, SDS-PAGE analysis, and extraction of proteins 276

12.2.8 Peptide mass fingerprinting (PMF) 277

12.2.9 MS/MS analysis 277

12.2.10 Western blot analysis 277

12.3 Results 278

12.4 Discussion 283

12.5 References 286

13 An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis 289

Eugene A. Kapp, Frédéric Schütz, Lisa M. Connolly, John A. Chakel,Jose E. Meza, Christine A. Miller, David Fenyo, Jimmy K. Eng, Joshua N. Adkins, Gilbert S. Omenn and Richard J. Simpson 13.1 Introduction 289 13.1.1 Heuristic algorithms 293

13.1.2 Probabilistic algorithms 292

13.2 Materials and methods 292

13.2.1 HUPO-PPP reference specimens 292

13.2.2 Sample preparation and MS analysis 293

13.2.3 Protein sequence databases 293

13.2.4 MS/MS database search strategy 293

13.2.4.1 SEQUESTand MASCOT workflow performed by the JPSL research group 294

13.2.4.2 SEQUESTand PeptideProphet workflow performed by the ISB research group 294

13.2.4.3 Spectrum Mill workflow performed by the Agilent group 295

13.2.4.4 Sonar and X!Tandem workflow performed by David Fenyo 295

13.2.5 Web interface for data validation, integration, and cross annotation 295

13.2.6 ROC curve generation 297

13.3 Results and discussion 298

13.3.1 Comparison of MS/MS search algorithms 299

13.3.1.1 Sensitivity and concordance between MS/MS search algorithms 299

13.3.1.2 Specificity and discriminatory power ofthe primary score statistic for the different MS/MS search algorithms: Distribution of scores and ROC plots 302

13.3.1.3 Calculation of score thresholds based on specified FP identification error rates 304

13.3.1.4 Benchmarking ofthe different MS/MS search algorithms at 1% FP error rate 320

13.3.1.5 Effect of database size and search strategy 322

13.3.1.6 Utility of reversed sequence searches 322

13.3.1.7 Consensus scoring between MS/MS search algorithms 322

13.4 Concluding remarks 323

13.5 References 324

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