Proteomic analysis of colon and rectal carcinoma using standard and customized databases

Understanding proteomic differences underlying the different phenotypic classes of colon and rectal carcinoma is important and may eventually lead to a better assessment of clinical behavior of these cancers. We here present a comprehensive description of the proteomic data obtained from 90 colon and rectal carcinomas previously subjected to genomic analysis by The Cancer Genome Atlas (TCGA). Here, the primary instrument files and derived secondary data files are compiled and presented in forms that will allow further analyses of the biology of colon and rectal carcinoma. We also discuss new challenges in processing these large proteomic datasets for relevant proteins and protein variants.

[1]  S. Gygi,et al.  Automation of nanoscale microcapillary liquid chromatography-tandem mass spectrometry with a vented column. , 2002, Analytical chemistry.

[2]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.

[3]  K. Resing,et al.  Comparison of Label-free Methods for Quantifying Human Proteins by Shotgun Proteomics*S , 2005, Molecular & Cellular Proteomics.

[4]  Michael K. Coleman,et al.  Correlation of relative abundance ratios derived from peptide ion chromatograms and spectrum counting for quantitative proteomic analysis using stable isotope labeling. , 2005, Analytical chemistry.

[5]  Michelle S. Scott,et al.  Global Survey of Organ and Organelle Protein Expression in Mouse: Combined Proteomic and Transcriptomic Profiling , 2006, Cell.

[6]  N. Samatova,et al.  Detecting differential and correlated protein expression in label-free shotgun proteomics. , 2006, Journal of proteome research.

[7]  D. Tabb,et al.  MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis. , 2007, Journal of proteome research.

[8]  Steven P Gygi,et al.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry , 2007, Nature Methods.

[9]  D. Tabb,et al.  Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. , 2007, Journal of proteome research.

[10]  Alexey I Nesvizhskii,et al.  Analysis and validation of proteomic data generated by tandem mass spectrometry , 2007, Nature Methods.

[11]  Michael D. Litton,et al.  IDPicker 2.0: Improved protein assembly with high discrimination peptide identification filtering. , 2009, Journal of proteome research.

[12]  K. Frazer,et al.  Human genetic variation and its contribution to complex traits , 2009, Nature Reviews Genetics.

[13]  Natalie I. Tasman,et al.  A guided tour of the Trans‐Proteomic Pipeline , 2010, Proteomics.

[14]  P. Pevzner,et al.  The Generating Function of CID, ETD, and CID/ETD Pairs of Tandem Mass Spectra: Applications to Database Search* , 2010, Molecular & Cellular Proteomics.

[15]  B. Searle,et al.  A Face in the Crowd: Recognizing Peptides Through Database Search* , 2011, Molecular & Cellular Proteomics.

[16]  Matthew Chambers,et al.  Supporting tool suite for production proteomics , 2011, Bioinform..

[17]  Bing Zhang,et al.  Protein identification using customized protein sequence databases derived from RNA-Seq data. , 2012, Journal of proteome research.

[18]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.

[19]  David L Tabb,et al.  Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment. , 2012, Journal of proteome research.

[20]  Natalie I. Tasman,et al.  A Cross-platform Toolkit for Mass Spectrometry and Proteomics , 2012, Nature Biotechnology.

[21]  R. Coffey,et al.  Proteomic Consequences of a Single Gene Mutation in a Colorectal Cancer Model , 2011, Journal of proteome research.

[22]  Jerry D. Holman,et al.  Identifying Proteomic LC‐MS/MS Data Sets with Bumbershoot and IDPicker , 2012, Current protocols in bioinformatics.

[23]  A. Nesvizhskii,et al.  Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data. , 2012, Journal of proteome research.

[24]  David L. Tabb,et al.  Refining comparative proteomics by spectral counting to account for shared peptides and multiple search engines , 2012, Analytical and Bioanalytical Chemistry.

[25]  Li Ding,et al.  Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. , 2013, Cell reports.

[26]  Xiaojing Wang,et al.  customProDB: an R package to generate customized protein databases from RNA-Seq data for proteomics search , 2013, Bioinform..

[27]  Mehdi Mesri,et al.  Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. , 2013, Cancer discovery.

[28]  W. Yarbrough,et al.  Proteomic analysis of oropharyngeal carcinomas reveals novel HPV‐associated biological pathways , 2013, International journal of cancer.

[29]  Jeffrey R. Whiteaker,et al.  Proteogenomic characterization of human colon and rectal cancer , 2014, Nature.

[30]  Xiaojing Wang,et al.  Proteogenomic analysis reveals unanticipated adaptations of colorectal tumor cells to deficiencies in DNA mismatch repair. , 2014, Cancer research.

[31]  A. Nesvizhskii.  Proteogenomics: concepts, applications and computational strategies , 2014, Nature Methods.

[32]  Ronald J Moore,et al.  Ischemia in Tumors Induces Early and Sustained Phosphorylation Changes in Stress Kinase Pathways but Does Not Affect Global Protein Levels* , 2014, Molecular & Cellular Proteomics.

[33]  David L. Tabb,et al.  QC Metrics from CPTAC Raw LC-MS/MS Data Interpreted through Multivariate Statistics , 2014, Analytical chemistry.

[34]  Jing Wang,et al.  Empowering biologists with multi-omics data: colorectal cancer as a paradigm , 2014, Bioinform..