501
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Cho JY, Lee HJ, Jeong SK, Kim KY, Kwon KH, Yoo JS, Omenn GS, Baker MS, Hancock WS, Paik YK. Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project. J Proteome Res 2015; 14:4959-66. [DOI: 10.1021/acs.jproteome.5b00578] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jin-Young Cho
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
| | - Hyoung-Joo Lee
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
| | - Seul-Ki Jeong
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
| | - Kwang-Youl Kim
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
| | | | | | - Gilbert S. Omenn
- Center
for Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor 48109, Michigan United States
| | - Mark S. Baker
- Department
of Biomedical Science, Faculty of Medicine and Health Science, Macquarie University, New South Wales 2109, Australia
| | | | - Young-Ki Paik
- Yonsei
Proteome Research Center, Department of Integrated OMICS for Biomedical
Science and Department of Biochemistry, College of Life Science and
Biotechnology, Yonsei University, 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea
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502
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Jeong SK, Hancock WS, Paik YK. GenomewidePDB 2.0: A Newly Upgraded Versatile Proteogenomic Database for the Chromosome-Centric Human Proteome Project. J Proteome Res 2015; 14:3710-9. [PMID: 26272709 DOI: 10.1021/acs.jproteome.5b00541] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Since the launch of the Chromosome-centric Human Proteome Project (C-HPP) in 2012, the number of "missing" proteins has fallen to 2932, down from ∼5932 since the number was first counted in 2011. We compared the characteristics of missing proteins with those of already annotated proteins with respect to transcriptional expression pattern and the time periods in which newly identified proteins were annotated. We learned that missing proteins commonly exhibit lower levels of transcriptional expression and less tissue-specific expression compared with already annotated proteins. This makes it more difficult to identify missing proteins as time goes on. One of the C-HPP goals is to identify alternative spliced product of proteins (ASPs), which are usually difficult to find by shot-gun proteomic methods due to their sequence similarities with the representative proteins. To resolve this problem, it may be necessary to use a targeted proteomics approach (e.g., selected and multiple reaction monitoring [S/MRM] assays) and an innovative bioinformatics platform that enables the selection of target peptides for rarely expressed missing proteins or ASPs. Given that the success of efforts to identify missing proteins may rely on more informative public databases, it was necessary to upgrade the available integrative databases. To this end, we attempted to improve the features and utility of GenomewidePDB by integrating transcriptomic information (e.g., alternatively spliced transcripts), annotated peptide information, and an advanced search interface that can find proteins of interest when applying a targeted proteomics strategy. This upgraded version of the database, GenomewidePDB 2.0, may not only expedite identification of the remaining missing proteins but also enhance the exchange of information among the proteome community. GenomewidePDB 2.0 is available publicly at http://genomewidepdb.proteomix.org/.
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Affiliation(s)
- Seul-Ki Jeong
- Yonsei Proteome Research Center and Biomedical Proteome Research Center , 50 Yonsei-Ro, Seodaemun-gu, Seoul 120-749, Korea
| | - William S Hancock
- Barnett Institute and Department of Chemistry and Chemical Biology, Northeastern University , 12 Oxford Street, Boston, Massachusetts 02115, United States
| | - Young-Ki Paik
- Yonsei Proteome Research Center and Biomedical Proteome Research Center , 50 Yonsei-Ro, Seodaemun-gu, Seoul 120-749, Korea.,Department of Biochemistry, Department of Integrated Omics for Biomedical Science (World Class University Graduate Program), Yonsei University , 50 Yonsei-Ro, Sudaemoon-ku, Seoul 120-749, Korea
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503
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Kitata RB, Dimayacyac-Esleta BRT, Choong WK, Tsai CF, Lin TD, Tsou CC, Weng SH, Chen YJ, Yang PC, Arco SD, Nesvizhskii AI, Sung TY, Chen YJ. Mining Missing Membrane Proteins by High-pH Reverse-Phase StageTip Fractionation and Multiple Reaction Monitoring Mass Spectrometry. J Proteome Res 2015. [PMID: 26202522 DOI: 10.1021/acs.jproteome.5b00477] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Despite significant efforts in the past decade toward complete mapping of the human proteome, 3564 proteins (neXtProt, 09-2014) are still "missing proteins". Over one-third of these missing proteins are annotated as membrane proteins, owing to their relatively challenging accessibility with standard shotgun proteomics. Using nonsmall cell lung cancer (NSCLC) as a model study, we aim to mine missing proteins from disease-associated membrane proteome, which may be still largely under-represented. To increase identification coverage, we employed Hp-RP StageTip prefractionation of membrane-enriched samples from 11 NSCLC cell lines. Analysis of membrane samples from 20 pairs of tumor and adjacent normal lung tissue was incorporated to include physiologically expressed membrane proteins. Using multiple search engines (X!Tandem, Comet, and Mascot) and stringent evaluation of FDR (MAYU and PeptideShaker), we identified 7702 proteins (66% membrane proteins) and 178 missing proteins (74 membrane proteins) with PSM-, peptide-, and protein-level FDR of 1%. Through multiple reaction monitoring using synthetic peptides, we provided additional evidence of eight missing proteins including seven with transmembrane helix domains. This study demonstrates that mining missing proteins focused on cancer membrane subproteome can greatly contribute to map the whole human proteome. All data were deposited into ProteomeXchange with the identifier PXD002224.
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Affiliation(s)
- Reta Birhanu Kitata
- Institute of Chemistry, Academia Sinica , No. 128, Academia Road Sec. 2, Taipei 115, Taiwan.,Department of Chemistry, National Tsing Hua University , 101, Sec 2, Kuang-Fu Road, Hsinchu 30013, Taiwan.,Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica , No. 1, Roosevelt Road, Sec. 4, Taipei 10617, Taiwan
| | - Baby Rorielyn T Dimayacyac-Esleta
- Institute of Chemistry, Academia Sinica , No. 128, Academia Road Sec. 2, Taipei 115, Taiwan.,Institute of Chemistry, University of the Philippines , Diliman Quezon City, Philippines
| | - Wai-Kok Choong
- Institute of Information Science, Academia Sinica , 128 Academia Road, Section 2, Taipei 115, Taiwan
| | - Chia-Feng Tsai
- Institute of Chemistry, Academia Sinica , No. 128, Academia Road Sec. 2, Taipei 115, Taiwan
| | - Tai-Du Lin
- Institute of Chemistry, Academia Sinica , No. 128, Academia Road Sec. 2, Taipei 115, Taiwan.,Department of Biochemical Sciences, National Taiwan University , 1 Roosevelt Road, Sec. 4, Taipei 106, Taiwan
| | - Chih-Chiang Tsou
- Department of Computational Medicine and Bioinformatics and Department of Pathology, University of Michigan Medical School , 1301 Catherine, Ann Arbor, Michigan 48109, United States
| | - Shao-Hsing Weng
- Institute of Chemistry, Academia Sinica , No. 128, Academia Road Sec. 2, Taipei 115, Taiwan.,Genome and Systems Biology Degree Program, National Taiwan University , 1, Roosevelt Road, Section 4, Taipei 10617, Taiwan
| | - Yi-Ju Chen
- Institute of Chemistry, Academia Sinica , No. 128, Academia Road Sec. 2, Taipei 115, Taiwan
| | - Pan-Chyr Yang
- Department of Internal Medicine, National Taiwan University Hospital , 1 Jen-Ai Road, Section 1, Taipei 10051, Taiwan.,National Taiwan University College of Medicine , No. 1, Section 1, Ren'ai Road, Taipei 100, Taiwan.,Institute of Biomedical Science, Academia Sinica , 128 Academia Road, Section 2, Taipei 115, Taiwan
| | - Susan D Arco
- Institute of Chemistry, University of the Philippines , Diliman Quezon City, Philippines
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics and Department of Pathology, University of Michigan Medical School , 1301 Catherine, Ann Arbor, Michigan 48109, United States
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica , 128 Academia Road, Section 2, Taipei 115, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica , No. 128, Academia Road Sec. 2, Taipei 115, Taiwan.,Department of Chemistry, National Tsing Hua University , 101, Sec 2, Kuang-Fu Road, Hsinchu 30013, Taiwan.,Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica , No. 1, Roosevelt Road, Sec. 4, Taipei 10617, Taiwan
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504
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Díez P, Droste C, Dégano RM, González-Muñoz M, Ibarrola N, Pérez-Andrés M, Garin-Muga A, Segura V, Marko-Varga G, LaBaer J, Orfao A, Corrales FJ, De Las Rivas J, Fuentes M. Integration of Proteomics and Transcriptomics Data Sets for the Analysis of a Lymphoma B-Cell Line in the Context of the Chromosome-Centric Human Proteome Project. J Proteome Res 2015. [PMID: 26216070 DOI: 10.1021/acs.jproteome.5b00474] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A comprehensive study of the molecular active landscape of human cells can be undertaken to integrate two different but complementary perspectives: transcriptomics, and proteomics. After the genome era, proteomics has emerged as a powerful tool to simultaneously identify and characterize the compendium of thousands of different proteins active in a cell. Thus, the Chromosome-centric Human Proteome Project (C-HPP) is promoting a full characterization of the human proteome combining high-throughput proteomics with the data derived from genome-wide expression profiling of protein-coding genes. Here we present a full proteomic profiling of a human lymphoma B-cell line (Ramos) performed using a nanoUPLC-LTQ-Orbitrap Velos proteomic platform, combined to an in-depth transcriptomic profiling of the same cell type. Data are available via ProteomeXchange with identifier PXD001933. Integration of the proteomic and transcriptomic data sets revealed a 94% overlap in the proteins identified by both -omics approaches. Moreover, functional enrichment analysis of the proteomic profiles showed an enrichment of several functions directly related to the biological and morphological characteristics of B-cells. In turn, about 30% of all protein-coding genes present in the whole human genome were identified as being expressed by the Ramos cells (stable average of 30% genes along all the chromosomes), revealing the size of the protein expression-set present in one specific human cell type. Additionally, the identification of missing proteins in our data sets has been reported, highlighting the power of the approach. Also, a comparison between neXtProt and UniProt database searches has been performed. In summary, our transcriptomic and proteomic experimental profiling provided a high coverage report of the expressed proteome from a human lymphoma B-cell type with a clear insight into the biological processes that characterized these cells. In this way, we demonstrated the usefulness of combining -omics for a comprehensive characterization of specific biological systems.
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Affiliation(s)
- Paula Díez
- Department of Medicine and General Cytometry Service-Nucleus, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain.,Proteomics Unit. Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - Conrad Droste
- Bioinformatics and Functional Genomics Research Group, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - Rosa M Dégano
- Proteomics Unit. Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - María González-Muñoz
- Department of Medicine and General Cytometry Service-Nucleus, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - Nieves Ibarrola
- Proteomics Unit. Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - Martín Pérez-Andrés
- Department of Medicine and General Cytometry Service-Nucleus, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - Alba Garin-Muga
- Division of Hepatology and Gene Therapy, Proteomics and Bioinformatics Unit, Centre for Applied Medical Research (CIMA), University of Navarra , 31008 Pamplona, Spain
| | - Víctor Segura
- Division of Hepatology and Gene Therapy, Proteomics and Bioinformatics Unit, Centre for Applied Medical Research (CIMA), University of Navarra , 31008 Pamplona, Spain
| | - Gyorgy Marko-Varga
- Clinical Protein Science and Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University , BMC D13, 221 84 Lund, Sweden
| | - Joshua LaBaer
- Biodesign Institute, Arizona State University , 1001 South McAllister Avenue, Tempe, Arizona 85287, United States
| | - Alberto Orfao
- Department of Medicine and General Cytometry Service-Nucleus, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - Fernando J Corrales
- Division of Hepatology and Gene Therapy, Proteomics and Bioinformatics Unit, Centre for Applied Medical Research (CIMA), University of Navarra , 31008 Pamplona, Spain
| | - Javier De Las Rivas
- Bioinformatics and Functional Genomics Research Group, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - Manuel Fuentes
- Department of Medicine and General Cytometry Service-Nucleus, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain.,Proteomics Unit. Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
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505
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Omenn GS, Lane L, Lundberg EK, Beavis RC, Nesvizhskii AI, Deutsch EW. Metrics for the Human Proteome Project 2015: Progress on the Human Proteome and Guidelines for High-Confidence Protein Identification. J Proteome Res 2015; 14:3452-60. [PMID: 26155816 DOI: 10.1021/acs.jproteome.5b00499] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Remarkable progress continues on the annotation of the proteins identified in the Human Proteome and on finding credible proteomic evidence for the expression of "missing proteins". Missing proteins are those with no previous protein-level evidence or insufficient evidence to make a confident identification upon reanalysis in PeptideAtlas and curation in neXtProt. Enhanced with several major new data sets published in 2014, the human proteome presented as neXtProt, version 2014-09-19, has 16,491 unique confident proteins (PE level 1), up from 13,664 at 2012-12 and 15,646 at 2013-09. That leaves 2948 missing proteins from genes classified having protein existence level PE 2, 3, or 4, as well as 616 dubious proteins at PE 5. Here, we document the progress of the HPP and discuss the importance of assessing the quality of evidence, confirming automated findings and considering alternative protein matches for spectra and peptides. We provide guidelines for proteomics investigators to apply in reporting newly identified proteins.
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Affiliation(s)
- Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan , 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Lydie Lane
- CALIPHO Group, Swiss Institute of Bioinformatics , Michel-Servet 1, 1211 Geneva 4, Switzerland
| | - Emma K Lundberg
- SciLifeLab Stockholm and School of Biotechnology, KTH , Karolinska Institutet Science Park, Tomtebodavägen 23, SE-171 65 Solna, Sweden
| | - Ronald C Beavis
- Biochemistry & Medical Genetics, University of Manitoba , Winnipeg, MB, Canada R3T 2N2
| | - Alexey I Nesvizhskii
- Pathology Department, University of Michigan , Medical Science Building 1, M4237, Ann Arbor, Michigan 48109-5602, United States
| | - Eric W Deutsch
- Institute for Systems Biology , 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
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506
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Deutsch EW, Sun Z, Campbell D, Kusebauch U, Chu CS, Mendoza L, Shteynberg D, Omenn GS, Moritz RL. State of the Human Proteome in 2014/2015 As Viewed through PeptideAtlas: Enhancing Accuracy and Coverage through the AtlasProphet. J Proteome Res 2015; 14:3461-73. [PMID: 26139527 DOI: 10.1021/acs.jproteome.5b00500] [Citation(s) in RCA: 93] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Human PeptideAtlas is a compendium of the highest quality peptide identifications from over 1000 shotgun mass spectrometry proteomics experiments collected from many different laboratories, all reanalyzed through a uniform processing pipeline. The latest 2015-03 build contains substantially more input data than past releases, is mapped to a recent version of our merged reference proteome, and uses improved informatics processing and the development of the AtlasProphet to provide the highest quality results. Within the set of ∼20,000 neXtProt primary entries, 14,070 (70%) are confidently detected in the latest build, 5% are ambiguous, 9% are redundant, leaving the total percentage of proteins for which there are no mapping detections at just 16% (3166), all derived from over 133 million peptide-spectrum matches identifying more than 1 million distinct peptides using AtlasProphet to characterize and classify the protein matches. Improved handling for detection and presentation of single amino-acid variants (SAAVs) reveals the detection of 5326 uniquely mapping SAAVs across 2794 proteins. With such a large amount of data, the control of false positives is a challenge. We present the methodology and results for maintaining rigorous quality along with a discussion of the implications of the remaining sources of errors in the build.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - Zhi Sun
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - David Campbell
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - Ulrike Kusebauch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - Caroline S Chu
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - Luis Mendoza
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - David Shteynberg
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - Gilbert S Omenn
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States.,Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics and School of Public Health, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
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507
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Horvatovich P, Lundberg EK, Chen YJ, Sung TY, He F, Nice EC, Goode RJ, Yu S, Ranganathan S, Baker MS, Domont GB, Velasquez E, Li D, Liu S, Wang Q, He QY, Menon R, Guan Y, Corrales FJ, Segura V, Casal JI, Pascual-Montano A, Albar JP, Fuentes M, Gonzalez-Gonzalez M, Diez P, Ibarrola N, Degano RM, Mohammed Y, Borchers CH, Urbani A, Soggiu A, Yamamoto T, Salekdeh GH, Archakov A, Ponomarenko E, Lisitsa A, Lichti CF, Mostovenko E, Kroes RA, Rezeli M, Végvári Á, Fehniger TE, Bischoff R, Vizcaíno JA, Deutsch EW, Lane L, Nilsson CL, Marko-Varga G, Omenn GS, Jeong SK, Lim JS, Paik YK, Hancock WS. Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project. J Proteome Res 2015; 14:3415-31. [DOI: 10.1021/pr5013009] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Péter Horvatovich
- Analytical
Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan
1, 9713 AV Groningen, The Netherlands
| | - Emma K. Lundberg
- Science
for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Yu-Ju Chen
- Institute
of Chemistry, Academia Sinica, 128 Academia Road Sec. 2, Taipei 115, Taiwan
| | - Ting-Yi Sung
- Institute
of Information Science, Academia Sinica, 128 Academia Road Sec. 2, Taipei 115, Taiwan
| | - Fuchu He
- The State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, No. 27 Taiping Road, Haidian District, Beijing 100850, China
| | - Edouard C. Nice
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Robert J. Goode
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Simon Yu
- Department
of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria 3800, Australia
| | - Shoba Ranganathan
- Department
of Chemistry and Biomolecular Sciences and ARC Centre of Excellence
in Bioinformatics, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Mark S. Baker
- Australian
School of Advanced Medicine, Macquarie University, Sydney, NSW 2109, Australia
| | - Gilberto B. Domont
- Proteomics Unit, Institute of Chemistry, Federal University of Rio de Janeiro, Cidade Universitária, Av Athos da Silveira Ramos 149, CT-A542, 21941-909 Rio de Janeriro, Rj, Brazil
| | - Erika Velasquez
- Proteomics Unit, Institute of Chemistry, Federal University of Rio de Janeiro, Cidade Universitária, Av Athos da Silveira Ramos 149, CT-A542, 21941-909 Rio de Janeriro, Rj, Brazil
| | - Dong Li
- The State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, No. 27 Taiping Road, Haidian District, Beijing 100850, China
| | - Siqi Liu
- Beijing Institute of Genomics and BGI Shenzhen, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
- BGI Shenzhen, Beishan Road, Yantian District, Shenzhen, 518083, China
| | - Quanhui Wang
- Beijing Institute of Genomics and BGI Shenzhen, No. 1 Beichen West Road, Chaoyang District, Beijing 100101, China
| | - Qing-Yu He
- Key Laboratory of Functional Protein
Research of Guangdong
Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Rajasree Menon
- Department of Computational Medicine & Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Yuanfang Guan
- Departments of Computational Medicine & Bioinformatics and Computer Sciences, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Fernando J. Corrales
- ProteoRed-ISCIII,
Biomolecular and Bioinformatics Resources Platform (PRB2), Spanish
Consortium of C-HPP (Chr-16), CIMA, University of Navarra, 31008 Pamplona, Spain
- Chr16 SpHPP Consortium, CIMA, University of Navarra, 31008 Pamplona, Spain
| | - Victor Segura
- ProteoRed-ISCIII,
Biomolecular and Bioinformatics Resources Platform (PRB2), Spanish
Consortium of C-HPP (Chr-16), CIMA, University of Navarra, 31008 Pamplona, Spain
- Chr16 SpHPP Consortium, CIMA, University of Navarra, 31008 Pamplona, Spain
| | - J. Ignacio Casal
- Department
of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas (CIB-CSIC), 28040 Madrid, Spain
| | | | - Juan P. Albar
- Centro Nacional de Biotecnologia (CNB-CSIC), Cantoblanco, 28049 Madrid, Spain
| | - Manuel Fuentes
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Maria Gonzalez-Gonzalez
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Paula Diez
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Nieves Ibarrola
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Rosa M. Degano
- Cancer
Research Center. Proteomics Unit and General Service of Cytometry,
Department of Medicine, University of Salmanca-CSIC, IBSAL, Campus Miguel de Unamuno
s/n, 37007 Salamanca, Spain
| | - Yassene Mohammed
- University of Victoria-Genome British Columbia Proteomics
Centre, Vancouver Island
Technology Park, #3101−4464 Markham Street, Victoria, British Columbia V8Z 7X8, Canada
- Center
for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Christoph H. Borchers
- University of Victoria-Genome British Columbia Proteomics
Centre, Vancouver Island
Technology Park, #3101−4464 Markham Street, Victoria, British Columbia V8Z 7X8, Canada
| | - Andrea Urbani
- Proteomics
and Metabonomic, Laboratory, Fondazione Santa Lucia, Rome, Italy
- Department
of Experimental Medicine and Surgery, University of Rome “Tor Vergata”, Rome, Italy
| | - Alessio Soggiu
- Department
of Veterinary Science and Public Health (DIVET), University of Milano, via Celoria 10, 20133 Milano, Italy
| | - Tadashi Yamamoto
- Institute
of Nephrology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Ghasem Hosseini Salekdeh
- Department of Molecular Systems Biology at Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
- Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran, Karaj, Iran
| | | | | | - Andrey Lisitsa
- Orechovich Institute of Biomedical Chemistry, Moscow, Russia
| | - Cheryl F. Lichti
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - Ekaterina Mostovenko
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - Roger A. Kroes
- Falk Center for Molecular Therapeutics, Department of Biomedical Engineering, Northwestern University, 1801 Maple Ave., Suite 4300, Evanston, Illinois 60201, United States
| | - Melinda Rezeli
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Ákos Végvári
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Thomas E. Fehniger
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Rainer Bischoff
- Analytical
Biochemistry, Department of Pharmacy, University of Groningen, A. Deusinglaan
1, 9713 AV Groningen, The Netherlands
| | - Juan Antonio Vizcaíno
- European Molecular
Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, CB10 1SD, Hinxton, Cambridge, United Kingdom
| | - Eric W. Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109, United States
| | - Lydie Lane
- SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- Department
of Human Protein Science, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Carol L. Nilsson
- Department
of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, Texas 77555-0617, United States
| | - György Marko-Varga
- Clinical Protein Science & Imaging, Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden
| | - Gilbert S. Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics and School of Public Health, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
| | - Seul-Ki Jeong
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - Jong-Sun Lim
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - Young-Ki Paik
- Departments of Integrated Omics for Biomedical Science & Biochemistry, College of Life Science and Technology, Yonsei Proteome Research Center, Yonsei University, Seoul, 120-749, Korea
| | - William S. Hancock
- The
Barnett Institute of Chemical and Biological Analysis, Northeastern University, 140 The Fenway, Boston, Massachusetts 02115, United States
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508
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Woo S, Cha SW, Bonissone S, Na S, Tabb DL, Pevzner PA, Bafna V. Advanced Proteogenomic Analysis Reveals Multiple Peptide Mutations and Complex Immunoglobulin Peptides in Colon Cancer. J Proteome Res 2015; 14:3555-67. [PMID: 26139413 DOI: 10.1021/acs.jproteome.5b00264] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Aiming toward an improved understanding of the regulation of proteins in cancer, recent studies from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have focused on analyzing cancer tissue using proteomic technologies and workflows. Although many proteogenomics approaches for the study of cancer samples have been proposed, serious methodological challenges remain, especially in the identification of multiple mutational variants or structural variations such as fusion gene events. In addition, although immune system genes play an important role in cancer, identification of IgG peptides remains challenging in proteomic data sets. Here, we describe an integrative proteogenomic method that extends the limit of proteogenomic searches to identify multiple variant peptides as well as immunoglobulin gene variations/rearrangements using customized mining of RNA-seq data. Our results also provide the first extensive characterization of tumor immune response and demonstrate the potential of this method to improve the molecular characterization of tumor subtypes.
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Affiliation(s)
| | | | | | | | - David L Tabb
- Department of Biomedical Informatics, Vanderbilt University , Nashville, Tennessee 37203, United States
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509
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Polyakova A, Kuznetsova K, Moshkovskii S. Proteogenomics meets cancer immunology: mass spectrometric discovery and analysis of neoantigens. Expert Rev Proteomics 2015; 12:533-41. [DOI: 10.1586/14789450.2015.1070100] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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510
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Martínez-Esteso MJ, Martínez-Márquez A, Sellés-Marchart S, Morante-Carriel JA, Bru-Martínez R. The role of proteomics in progressing insights into plant secondary metabolism. FRONTIERS IN PLANT SCIENCE 2015; 6:504. [PMID: 26217358 PMCID: PMC4493368 DOI: 10.3389/fpls.2015.00504] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Accepted: 06/23/2015] [Indexed: 05/29/2023]
Abstract
The development of omics has enabled the genome-wide exploration of all kinds of biological processes at the molecular level. Almost every field of plant biology has been analyzed at the genomic, transcriptomic and proteomic level. Here we focus on the particular contribution that proteomic technologies have made in progressing knowledge and characterising plant secondary metabolism (SM) pathways since early expectations were created 15 years ago. We analyzed how three major issues in the proteomic analysis of plant SM have been implemented in various research studies. These issues are: (i) the selection of a suitable plant material rich in secondary metabolites of interest, such as specialized tissues and organs, and in vitro cell cultures; (ii) the proteomic strategy to access target proteins, either a comprehensive or a differential analysis; (iii) the proteomic approach, represented by the hypothesis-free discovery proteomics and the hypothesis-driven targeted proteomics. We also examine to what extent the most-advanced technologies have been incorporated into proteomic research in plant SM and highlight some cutting edge techniques that would strongly benefit the progress made in this field.
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Affiliation(s)
- María J. Martínez-Esteso
- Plant Proteomics and Functional Genomics Group, Department of Agrochemistry and Biochemistry, Multidisciplinary Institute for Environmental Studies “Ramon Margalef”, University of Alicante, Alicante, Spain
| | - Ascensión Martínez-Márquez
- Plant Proteomics and Functional Genomics Group, Department of Agrochemistry and Biochemistry, Multidisciplinary Institute for Environmental Studies “Ramon Margalef”, University of Alicante, Alicante, Spain
| | - Susana Sellés-Marchart
- Plant Proteomics and Functional Genomics Group, Department of Agrochemistry and Biochemistry, Multidisciplinary Institute for Environmental Studies “Ramon Margalef”, University of Alicante, Alicante, Spain
- Biotechnology and Molecular Biology Group, Quevedo State Technical University, Quevedo, Ecuador
| | - Jaime A. Morante-Carriel
- Proteomics and Genomics Division, Research Technical Facility, University of Alicante, Alicante, Spain
| | - Roque Bru-Martínez
- Plant Proteomics and Functional Genomics Group, Department of Agrochemistry and Biochemistry, Multidisciplinary Institute for Environmental Studies “Ramon Margalef”, University of Alicante, Alicante, Spain
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511
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Slebos RJ, Wang X, Wang X, Zhang B, Tabb DL, Liebler DC. Proteomic analysis of colon and rectal carcinoma using standard and customized databases. Sci Data 2015; 2:150022. [PMID: 26110064 PMCID: PMC4477697 DOI: 10.1038/sdata.2015.22] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 02/17/2015] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Robbert J.C. Slebos
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232, USA
| | - Xia Wang
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Xaojing Wang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Bing Zhang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - David L. Tabb
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Daniel C. Liebler
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232, USA
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512
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Aeschimann F, Xiong J, Arnold A, Dieterich C, Großhans H. Transcriptome-wide measurement of ribosomal occupancy by ribosome profiling. Methods 2015; 85:75-89. [PMID: 26102273 DOI: 10.1016/j.ymeth.2015.06.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 06/10/2015] [Accepted: 06/15/2015] [Indexed: 01/08/2023] Open
Abstract
Gene expression profiling provides a tool to analyze the internal states of cells or organisms, and their responses to perturbations. While global measurements of mRNA levels have thus been widely used for many years, it is only through the recent development of the ribosome profiling technique that an analogous examination of global mRNA translation programs has become possible. Ribosome profiling reveals which RNAs are being translated to what extent and where the translated open reading frames are located. In addition, different modes of translation regulation can be distinguished and characterized. Here, we present an optimized, step-by-step protocol for ribosome profiling. Although established in Caenorhabditis elegans, our protocol and optimization approaches should be equally usable for other model organisms or cell culture with little adaptation. Next to providing a protocol, we compare two different methods for isolation of single ribosomes and two different library preparations, and describe strategies to optimize the RNase digest and to reduce ribosomal RNA contamination in the libraries. Moreover, we discuss bioinformatic strategies to evaluate the quality of the data and explain how the data can be analyzed for different applications. In sum, this article seeks to facilitate the understanding, execution, and optimization of ribosome profiling experiments.
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Affiliation(s)
- Florian Aeschimann
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Jieyi Xiong
- Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Straße 9b, 50931 Cologne, Germany
| | - Andreas Arnold
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Christoph Dieterich
- Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Straße 9b, 50931 Cologne, Germany.
| | - Helge Großhans
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.
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513
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Zhang K, Fu Y, Zeng WF, He K, Chi H, Liu C, Li YC, Gao Y, Xu P, He SM. A note on the false discovery rate of novel peptides in proteogenomics. Bioinformatics 2015; 31:3249-53. [PMID: 26076724 PMCID: PMC4595894 DOI: 10.1093/bioinformatics/btv340] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Accepted: 05/27/2015] [Indexed: 11/15/2022] Open
Abstract
Motivation: Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes. Results: To quantitatively model this problem, we theoretically analyze the subgroup false discovery rates of annotated and novel peptides. Our analysis shows that the annotation completeness ratio of a genome is the dominant factor influencing the subgroup FDR of novel peptides. Experimental results on two real datasets of Escherichia coli and Mycobacterium tuberculosis support our conjecture. Contact:yfu@amss.ac.cn or xupingghy@gmail.com or smhe@ict.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kun Zhang
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, University of Chinese Academy of Sciences, Beijing 100049
| | - Yan Fu
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 and
| | - Wen-Feng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, University of Chinese Academy of Sciences, Beijing 100049
| | - Kun He
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, University of Chinese Academy of Sciences, Beijing 100049
| | - Hao Chi
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190
| | - Chao Liu
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190
| | - Yan-Chang Li
- State Key Laboratory of Proteomics, National Engineering Research Center for Protein Drugs, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Yuan Gao
- State Key Laboratory of Proteomics, National Engineering Research Center for Protein Drugs, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Ping Xu
- State Key Laboratory of Proteomics, National Engineering Research Center for Protein Drugs, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Si-Min He
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190
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514
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Kim H, Park H, Paek E. NextSearch: A Search Engine for Mass Spectrometry Data against a Compact Nucleotide Exon Graph. J Proteome Res 2015; 14:2784-91. [PMID: 26004133 DOI: 10.1021/acs.jproteome.5b00047] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Proteogenomics research has been using six-frame translation of the whole genome or amino acid exon graphs to overcome the limitations of reference protein sequence database; however, six-frame translation is not suitable for annotating genes that span over multiple exons, and amino acid exon graphs are not convenient to represent novel splice variants and exon skipping events between exons of incompatible reading frames. We propose a proteogenomic pipeline NextSearch (Nucleotide EXon-graph Transcriptome Search) that is based on a nucleotide exon graph. This pipeline consists of constructing a compact nucleotide exon graph that systematically incorporates novel splice variations and a search tool that identifies peptides by directly searching the nucleotide exon graph against tandem mass spectra. Because our exon graph stores nucleotide sequences, it can easily represent novel splice variations and exon skipping events between incompatible reading frame exons. Searching for peptide identification is performed against this nucleotide exon graph, without converting it into a protein sequence in FASTA format, achieving an order of magnitude reduction in the size of the sequence database storage. NextSearch outputs the proteome-genome/transcriptome mapping results in a general feature format (GFF) file, which can be visualized by public tools such as the UCSC Genome Browser.
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Affiliation(s)
- Hyunwoo Kim
- †Department of Electronics and Computer Engineering and ‡Department of Computer Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea
| | - Heejin Park
- †Department of Electronics and Computer Engineering and ‡Department of Computer Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea
| | - Eunok Paek
- †Department of Electronics and Computer Engineering and ‡Department of Computer Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea
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515
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Xu X, Liu T, Ren X, Liu B, Yang J, Chen L, Wei C, Zheng J, Dong J, Sun L, Zhu Y, Jin Q. Proteogenomic Analysis of Trichophyton rubrum Aided by RNA Sequencing. J Proteome Res 2015; 14:2207-18. [PMID: 25868943 DOI: 10.1021/acs.jproteome.5b00009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Infections caused by dermatophytes, Trichophyton rubrum in particular, are among the most common diseases in humans. In this study, we present a proteogenomic analysis of T. rubrum based on whole-genome proteomics and RNA-Seq studies. We confirmed 4291 expressed proteins in T. rubrum and validated their annotated gene structures based on 35 874 supporting peptides. In addition, we identified 323 novel peptides (not present in the current annotated protein database of T. rubrum) that can be used to enhance current T. rubrum annotations. A total of 104 predicted genes supported by novel peptides were identified, and 127 gene models suggested by the novel peptides that conflicted with existing annotations were manually assigned based on transcriptomic evidence. RNA-Seq confirmed the validity of 95% of the total peptides. Our study provides evidence that confirms and improves the genome annotation of T. rubrum and represents the first survey of T. rubrum genome annotations based on experimental evidence. Additionally, our integrated proteomics and multisourced transcriptomics approach provides stronger evidence for annotation refinement than proteomic data alone, which helps to address the dilemma of one-hit wonders (uncertainties supported by only one peptide).
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516
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Methionine to isothreonine conversion as a source of false discovery identifications of genetically encoded variants in proteogenomics. J Proteomics 2015; 120:169-78. [DOI: 10.1016/j.jprot.2015.03.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2014] [Revised: 03/03/2015] [Accepted: 03/07/2015] [Indexed: 01/07/2023]
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517
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Landry CR, Zhong X, Nielly-Thibault L, Roucou X. Found in translation: functions and evolution of a recently discovered alternative proteome. Curr Opin Struct Biol 2015; 32:74-80. [PMID: 25795211 DOI: 10.1016/j.sbi.2015.02.017] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 02/10/2015] [Accepted: 02/26/2015] [Indexed: 11/29/2022]
Abstract
A major goal in biology is to map entire proteomes to better understand the biology and evolution of cells. However, our current views of proteomes are conservative and biased against small proteins. Besides serendipitous discoveries of small proteins, it has been largely assumed that eukaryotic mature mRNAs contain a single ORF and that non-coding RNAs are not translated because their ORFs are too short to play a functional role. A flurry of recent studies brought to light an unexplored proteome that is mainly translated from short ORFs in non-coding regions and from alternative ORFs (AltORFs) in reference genes. The detection of these small proteins and the elucidation of their functions remain challenging and open a new dimension of eukaryotic proteomes, including the birth of novel genes and proteins.
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Affiliation(s)
- Christian R Landry
- Département de Biologie, Université Laval, Québec, Québec, Canada; Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Québec, Canada; PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Québec, Québec, Canada.
| | - Xiangfu Zhong
- Département de Biologie, Université Laval, Québec, Québec, Canada; Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Québec, Canada; PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Québec, Québec, Canada
| | - Lou Nielly-Thibault
- Département de Biologie, Université Laval, Québec, Québec, Canada; Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Québec, Canada; PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Québec, Québec, Canada
| | - Xavier Roucou
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Québec, Québec, Canada; Département de Biochimie, Université de Sherbrooke, Sherbrooke, Québec, Canada
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518
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Faulkner S, Dun MD, Hondermarck H. Proteogenomics: emergence and promise. Cell Mol Life Sci 2015; 72:953-7. [PMID: 25609363 PMCID: PMC11113406 DOI: 10.1007/s00018-015-1837-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 01/08/2015] [Accepted: 01/12/2015] [Indexed: 12/14/2022]
Abstract
Proteogenomics, or the integration of proteomics with genomics and transcriptomics, is emerging as the next step towards a unified understanding of cellular functions. Looking globally and simultaneously at gene structure, RNA expression, protein synthesis and post-translational modifications have become technically feasible and offer a new perspective to molecular processes. Recent publications have highlighted the value of proteogenomics in oncology for defining the molecular signature of human tumors, and translation to other areas of biomedicine and life sciences is anticipated. This mini-review will discuss recent developments, challenges and perspectives in proteogenomics.
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Affiliation(s)
- Sam Faulkner
- Faculty of Health and Medicine, School of Biomedical Sciences and Pharmacy and Hunter Medical Research Institute, Life Science Building, University of Newcastle, Callaghan, NSW 2308 Australia
| | - Matthew D. Dun
- Faculty of Health and Medicine, School of Biomedical Sciences and Pharmacy and Hunter Medical Research Institute, Life Science Building, University of Newcastle, Callaghan, NSW 2308 Australia
| | - Hubert Hondermarck
- Faculty of Health and Medicine, School of Biomedical Sciences and Pharmacy and Hunter Medical Research Institute, Life Science Building, University of Newcastle, Callaghan, NSW 2308 Australia
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519
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Onco-proteogenomics: cancer proteomics joins forces with genomics. Nat Methods 2015; 11:1107-13. [PMID: 25357240 DOI: 10.1038/nmeth.3138] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Accepted: 06/26/2014] [Indexed: 12/21/2022]
Abstract
The complexities of tumor genomes are rapidly being uncovered, but how they are regulated into functional proteomes remains poorly understood. Standard proteomics workflows use databases of known proteins, but these databases do not capture the uniqueness of the cancer transcriptome, with its point mutations, unusual splice variants and gene fusions. Onco-proteogenomics integrates mass spectrometry-generated data with genomic information to identify tumor-specific peptides. Linking tumor-derived DNA, RNA and protein measurements into a central-dogma perspective has the potential to improve our understanding of cancer biology.
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