Literature DB >> 27049631

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation.

Gloria M Sheynkman1,2,3, Michael R Shortreed3, Anthony J Cesnik3, Lloyd M Smith3,4.   

Abstract

Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

Entities:  

Keywords:  alternative splicing; customized protein databases; genetic variation; isoforms; novel splice junction; polymorphism; proteoform; proteomics; sample-specific databases; single amino acid variant

Mesh:

Substances:

Year:  2016        PMID: 27049631      PMCID: PMC4991544          DOI: 10.1146/annurev-anchem-071015-041722

Source DB:  PubMed          Journal:  Annu Rev Anal Chem (Palo Alto Calif)        ISSN: 1936-1327            Impact factor:   10.745


  167 in total

1.  Computational analysis of unassigned high-quality MS/MS spectra in proteomic data sets.

Authors:  Kang Ning; Damian Fermin; Alexey I Nesvizhskii
Journal:  Proteomics       Date:  2010-07       Impact factor: 3.984

2.  Large-scale quantification of single amino-acid variations by a variation-associated database search strategy.

Authors:  Chunxia Song; Fangjun Wang; Kai Cheng; Xiaoluan Wei; Yangyang Bian; Keyun Wang; Yexiong Tan; Hongyang Wang; Mingliang Ye; Hanfa Zou
Journal:  J Proteome Res       Date:  2013-11-25       Impact factor: 4.466

3.  A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome.

Authors:  Marc Sultan; Marcel H Schulz; Hugues Richard; Alon Magen; Andreas Klingenhoff; Matthias Scherf; Martin Seifert; Tatjana Borodina; Aleksey Soldatov; Dmitri Parkhomchuk; Dominic Schmidt; Sean O'Keeffe; Stefan Haas; Martin Vingron; Hans Lehrach; Marie-Laure Yaspo
Journal:  Science       Date:  2008-07-03       Impact factor: 47.728

Review 4.  Next-generation sequencing platforms.

Authors:  Elaine R Mardis
Journal:  Annu Rev Anal Chem (Palo Alto Calif)       Date:  2013       Impact factor: 10.745

5.  Construction and assessment of individualized proteogenomic databases for large-scale analysis of nonsynonymous single nucleotide variants.

Authors:  Karsten Krug; Sasa Popic; Alejandro Carpy; Christoph Taumer; Boris Macek
Journal:  Proteomics       Date:  2014-11-17       Impact factor: 3.984

6.  A bioinformatics workflow for variant peptide detection in shotgun proteomics.

Authors:  Jing Li; Zengliu Su; Ze-Qiang Ma; Robbert J C Slebos; Patrick Halvey; David L Tabb; Daniel C Liebler; William Pao; Bing Zhang
Journal:  Mol Cell Proteomics       Date:  2011-03-09       Impact factor: 5.911

Review 7.  RNA-Seq: a revolutionary tool for transcriptomics.

Authors:  Zhong Wang; Mark Gerstein; Michael Snyder
Journal:  Nat Rev Genet       Date:  2009-01       Impact factor: 53.242

8.  N-terminal proteomics and ribosome profiling provide a comprehensive view of the alternative translation initiation landscape in mice and men.

Authors:  Petra Van Damme; Daria Gawron; Wim Van Criekinge; Gerben Menschaert
Journal:  Mol Cell Proteomics       Date:  2014-03-12       Impact factor: 5.911

9.  Quantitative analysis of single amino acid variant peptides associated with pancreatic cancer in serum by an isobaric labeling quantitative method.

Authors:  Song Nie; Haidi Yin; Zhijing Tan; Michelle A Anderson; Mack T Ruffin; Diane M Simeone; David M Lubman
Journal:  J Proteome Res       Date:  2014-11-24       Impact factor: 4.466

10.  Assessment of transcript reconstruction methods for RNA-seq.

Authors:  Josep F Abril; Pär G Engström; Felix Kokocinski; Tamara Steijger; Tim J Hubbard; Roderic Guigó; Jennifer Harrow; Paul Bertone
Journal:  Nat Methods       Date:  2013-11-03       Impact factor: 28.547

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  22 in total

Review 1.  Moonshot Objectives: Catalyze New Scientific Breakthroughs-Proteogenomics.

Authors:  Karin D Rodland; Paul Piehowski; Richard D Smith
Journal:  Cancer J       Date:  2018 May/Jun       Impact factor: 3.360

Review 2.  Methods, Tools and Current Perspectives in Proteogenomics.

Authors:  Kelly V Ruggles; Karsten Krug; Xiaojing Wang; Karl R Clauser; Jing Wang; Samuel H Payne; David Fenyö; Bing Zhang; D R Mani
Journal:  Mol Cell Proteomics       Date:  2017-04-29       Impact factor: 5.911

3.  Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line.

Authors:  Zhijing Tan; Song Nie; Sean P McDermott; Max S Wicha; David M Lubman
Journal:  J Proteome Res       Date:  2017-01-20       Impact factor: 4.466

4.  Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line.

Authors:  Zhijing Tan; Jianhui Zhu; Paul M Stemmer; Liangliang Sun; Zhichang Yang; Kendall Schultz; Matthew J Gaffrey; Anthony J Cesnik; Xinpei Yi; Xiaohu Hao; Michael R Shortreed; Tujin Shi; David M Lubman
Journal:  J Proteome Res       Date:  2020-02-27       Impact factor: 4.466

5.  Comparative Secretome Profiling and Mutant Protein Identification in Metastatic Prostate Cancer Cells by Quantitative Mass Spectrometry-based Proteomics.

Authors:  Oh Kwang Kwon; Ju Mi Jeon; Eunji Sung; Ann-Yea Na; Sun Joo Kim; Sangkyu Lee
Journal:  Cancer Genomics Proteomics       Date:  2018 Jul-Aug       Impact factor: 4.069

Review 6.  Advances and Trends in Omics Technology Development.

Authors:  Xiaofeng Dai; Li Shen
Journal:  Front Med (Lausanne)       Date:  2022-07-01

Review 7.  Clinical potential of mass spectrometry-based proteogenomics.

Authors:  Bing Zhang; Jeffrey R Whiteaker; Andrew N Hoofnagle; Geoffrey S Baird; Karin D Rodland; Amanda G Paulovich
Journal:  Nat Rev Clin Oncol       Date:  2019-04       Impact factor: 66.675

8.  Elucidating Escherichia coli Proteoform Families Using Intact-Mass Proteomics and a Global PTM Discovery Database.

Authors:  Yunxiang Dai; Michael R Shortreed; Mark Scalf; Brian L Frey; Anthony J Cesnik; Stefan Solntsev; Leah V Schaffer; Lloyd M Smith
Journal:  J Proteome Res       Date:  2017-11-03       Impact factor: 4.466

9.  Spritz: A Proteogenomic Database Engine.

Authors:  Anthony J Cesnik; Rachel M Miller; Khairina Ibrahim; Lei Lu; Robert J Millikin; Michael R Shortreed; Brian L Frey; Lloyd M Smith
Journal:  J Proteome Res       Date:  2020-10-07       Impact factor: 4.466

10.  Improved methods for RNAseq-based alternative splicing analysis.

Authors:  Patrick Pirrotte; Nicholas J Schork; Rebecca F Halperin; Apurva Hegde; Jessica D Lang; Elizabeth A Raupach; Christophe Legendre; Winnie S Liang; Patricia M LoRusso; Aleksandar Sekulic; Jeffrey A Sosman; Jeffrey M Trent; Sampathkumar Rangasamy
Journal:  Sci Rep       Date:  2021-05-24       Impact factor: 4.996

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