Literature DB >> 26860878

A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline.

Paul A Rudnick1,2, Sanford P Markey2, Jeri Roth2, Yuri Mirokhin2, Xinjian Yan2, Dmitrii V Tchekhovskoi2, Nathan J Edwards3, Ratna R Thangudu4, Karen A Ketchum4, Christopher R Kinsinger5, Mehdi Mesri5, Henry Rodriguez5, Stephen E Stein2.   

Abstract

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced large proteomics data sets from the mass spectrometric interrogation of tumor samples previously analyzed by The Cancer Genome Atlas (TCGA) program. The availability of the genomic and proteomic data is enabling proteogenomic study for both reference (i.e., contained in major sequence databases) and nonreference markers of cancer. The CPTAC laboratories have focused on colon, breast, and ovarian tissues in the first round of analyses; spectra from these data sets were produced from 2D liquid chromatography-tandem mass spectrometry analyses and represent deep coverage. To reduce the variability introduced by disparate data analysis platforms (e.g., software packages, versions, parameters, sequence databases, etc.), the CPTAC Common Data Analysis Platform (CDAP) was created. The CDAP produces both peptide-spectrum-match (PSM) reports and gene-level reports. The pipeline processes raw mass spectrometry data according to the following: (1) peak-picking and quantitative data extraction, (2) database searching, (3) gene-based protein parsimony, and (4) false-discovery rate-based filtering. The pipeline also produces localization scores for the phosphopeptide enrichment studies using the PhosphoRS program. Quantitative information for each of the data sets is specific to the sample processing, with PSM and protein reports containing the spectrum-level or gene-level ("rolled-up") precursor peak areas and spectral counts for label-free or reporter ion log-ratios for 4plex iTRAQ. The reports are available in simple tab-delimited formats and, for the PSM-reports, in mzIdentML. The goal of the CDAP is to provide standard, uniform reports for all of the CPTAC data to enable comparisons between different samples and cancer types as well as across the major omics fields.

Entities:  

Keywords:  CPTAC; bioinformatics; cancer; data analysis pipeline; proteomics data resource

Mesh:

Substances:

Year:  2016        PMID: 26860878      PMCID: PMC5117628          DOI: 10.1021/acs.jproteome.5b01091

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  16 in total

1.  Universal and confident phosphorylation site localization using phosphoRS.

Authors:  Thomas Taus; Thomas Köcher; Peter Pichler; Carmen Paschke; Andreas Schmidt; Christoph Henrich; Karl Mechtler
Journal:  J Proteome Res       Date:  2011-11-10       Impact factor: 4.466

2.  The generating function of CID, ETD, and CID/ETD pairs of tandem mass spectra: applications to database search.

Authors:  Sangtae Kim; Nikolai Mischerikow; Nuno Bandeira; J Daniel Navarro; Louis Wich; Shabaz Mohammed; Albert J R Heck; Pavel A Pevzner
Journal:  Mol Cell Proteomics       Date:  2010-09-09       Impact factor: 5.911

3.  Addressing accuracy and precision issues in iTRAQ quantitation.

Authors:  Natasha A Karp; Wolfgang Huber; Pawel G Sadowski; Philip D Charles; Svenja V Hester; Kathryn S Lilley
Journal:  Mol Cell Proteomics       Date:  2010-04-10       Impact factor: 5.911

4.  The CPTAC Data Portal: A Resource for Cancer Proteomics Research.

Authors:  Nathan J Edwards; Mauricio Oberti; Ratna R Thangudu; Shuang Cai; Peter B McGarvey; Shine Jacob; Subha Madhavan; Karen A Ketchum
Journal:  J Proteome Res       Date:  2015-05-04       Impact factor: 4.466

5.  MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis.

Authors:  David L Tabb; Christopher G Fernando; Matthew C Chambers
Journal:  J Proteome Res       Date:  2007-02       Impact factor: 4.466

6.  Development and validation of a spectral library searching method for peptide identification from MS/MS.

Authors:  Henry Lam; Eric W Deutsch; James S Eddes; Jimmy K Eng; Nichole King; Stephen E Stein; Ruedi Aebersold
Journal:  Proteomics       Date:  2007-03       Impact factor: 3.984

7.  Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry.

Authors:  Lukas Reiter; Manfred Claassen; Sabine P Schrimpf; Marko Jovanovic; Alexander Schmidt; Joachim M Buhmann; Michael O Hengartner; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2009-07-16       Impact factor: 5.911

8.  Proteogenomic characterization of human colon and rectal cancer.

Authors:  Bing Zhang; Jing Wang; Xiaojing Wang; Jing Zhu; Qi Liu; Zhiao Shi; Matthew C Chambers; Lisa J Zimmerman; Kent F Shaddox; Sangtae Kim; Sherri R Davies; Sean Wang; Pei Wang; Christopher R Kinsinger; Robert C Rivers; Henry Rodriguez; R Reid Townsend; Matthew J C Ellis; Steven A Carr; David L Tabb; Robert J Coffey; Robbert J C Slebos; Daniel C Liebler
Journal:  Nature       Date:  2014-07-20       Impact factor: 49.962

9.  A cross-platform toolkit for mass spectrometry and proteomics.

Authors:  Matthew C Chambers; Brendan Maclean; Robert Burke; Dario Amodei; Daniel L Ruderman; Steffen Neumann; Laurent Gatto; Bernd Fischer; Brian Pratt; Jarrett Egertson; Katherine Hoff; Darren Kessner; Natalie Tasman; Nicholas Shulman; Barbara Frewen; Tahmina A Baker; Mi-Youn Brusniak; Christopher Paulse; David Creasy; Lisa Flashner; Kian Kani; Chris Moulding; Sean L Seymour; Lydia M Nuwaysir; Brent Lefebvre; Frank Kuhlmann; Joe Roark; Paape Rainer; Suckau Detlev; Tina Hemenway; Andreas Huhmer; James Langridge; Brian Connolly; Trey Chadick; Krisztina Holly; Josh Eckels; Eric W Deutsch; Robert L Moritz; Jonathan E Katz; David B Agus; Michael MacCoss; David L Tabb; Parag Mallick
Journal:  Nat Biotechnol       Date:  2012-10       Impact factor: 54.908

10.  A standardized framing for reporting protein identifications in mzIdentML 1.2.

Authors:  Sean L Seymour; Terry Farrah; Pierre-Alain Binz; Robert J Chalkley; John S Cottrell; Brian C Searle; David L Tabb; Juan Antonio Vizcaíno; Gorka Prieto; Julian Uszkoreit; Martin Eisenacher; Salvador Martínez-Bartolomé; Fawaz Ghali; Andrew R Jones
Journal:  Proteomics       Date:  2014-09-23       Impact factor: 3.984

View more
  29 in total

1.  Findings from the Section on Bioinformatics and Translational Informatics.

Authors:  H Dauchel; T Lecroq
Journal:  Yearb Med Inform       Date:  2016-11-10

2.  Integration and Analysis of CPTAC Proteomics Data in the Context of Cancer Genomics in the cBioPortal.

Authors:  Pamela Wu; Zachary J Heins; James T Muller; Lizabeth Katsnelson; Ino de Bruijn; Adam A Abeshouse; Nikolaus Schultz; David Fenyö; Jianjiong Gao
Journal:  Mol Cell Proteomics       Date:  2019-07-15       Impact factor: 5.911

Review 3.  Artificial Intelligence for Precision Oncology.

Authors:  Sherry Bhalla; Alessandro Laganà
Journal:  Adv Exp Med Biol       Date:  2022       Impact factor: 2.622

Review 4.  Cancer proteogenomics: current impact and future prospects.

Authors:  D R Mani; Karsten Krug; Bing Zhang; Shankha Satpathy; Karl R Clauser; Li Ding; Matthew Ellis; Michael A Gillette; Steven A Carr
Journal:  Nat Rev Cancer       Date:  2022-03-02       Impact factor: 60.716

Review 5.  Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome.

Authors:  Lois Gardner; Kostas Kostarelos; Parag Mallick; Caroline Dive; Marilena Hadjidemetriou
Journal:  Nat Rev Clin Oncol       Date:  2022-06-23       Impact factor: 65.011

Review 6.  A Review on Quantitative Multiplexed Proteomics.

Authors:  Nishant Pappireddi; Lance Martin; Martin Wühr
Journal:  Chembiochem       Date:  2019-04-18       Impact factor: 3.164

7.  Highlights of the Biology and Disease-driven Human Proteome Project, 2015-2016.

Authors:  Jennifer E Van Eyk; Fernando J Corrales; Ruedi Aebersold; Ferdinando Cerciello; Eric W Deutsch; Paola Roncada; Jean-Charles Sanchez; Tadashi Yamamoto; Pengyuan Yang; Hui Zhang; Gilbert S Omenn
Journal:  J Proteome Res       Date:  2016-09-20       Impact factor: 4.466

8.  Managing a Large-Scale Multiomics Project: A Team Science Case Study in Proteogenomics.

Authors:  Paul A Stewart; Eric A Welsh; Bin Fang; Victoria Izumi; Tania Mesa; Chaomei Zhang; Sean Yoder; Guolin Zhang; Ling Cen; Fredrik Pettersson; Yonghong Zhang; Zhihua Chen; Chia-Ho Cheng; Ram Thapa; Zachary Thompson; Melissa Avedon; Marek Wloch; Michelle Fournier; Katherine M Fellows; Jewel M Francis; James J Saller; Theresa A Boyle; Y Ann Chen; Eric B Haura; Jamie K Teer; Steven A Eschrich; John M Koomen
Journal:  Methods Mol Biol       Date:  2021

9.  hsa_circ_0008234 inhibits the progression of lung adenocarcinoma by sponging miR-574-5p.

Authors:  Wei Jiang; Yaozhou He; Zijian Ma; Yu Zhang; Chengpeng Zhang; Nianpeng Zheng; Xing Tang
Journal:  Cell Death Discov       Date:  2021-05-28

10.  The Correlation Between SPP1 and Immune Escape of EGFR Mutant Lung Adenocarcinoma Was Explored by Bioinformatics Analysis.

Authors:  Yi Zheng; Shiying Hao; Cheng Xiang; Yaguang Han; Yanhong Shang; Qiang Zhen; Yiyi Zhao; Miao Zhang; Yan Zhang
Journal:  Front Oncol       Date:  2021-06-10       Impact factor: 6.244

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.