1
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Ariffin N, Newman DW, Nelson MG, O’cualain R, Hubbard SJ. Proteogenomic Gene Structure Validation in the Pineapple Genome. J Proteome Res 2024; 23:1583-1592. [PMID: 38651221 PMCID: PMC11077482 DOI: 10.1021/acs.jproteome.3c00675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 03/15/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
MD2 pineapple (Ananas comosus) is the second most important tropical crop that preserves crassulacean acid metabolism (CAM), which has high water-use efficiency and is fast becoming the most consumed fresh fruit worldwide. Despite the significance of environmental efficiency and popularity, until very recently, its genome sequence has not been determined and a high-quality annotated proteome has not been available. Here, we have undertaken a pilot proteogenomic study, analyzing the proteome of MD2 pineapple leaves using liquid chromatography-mass spectrometry (LC-MS/MS), which validates 1781 predicted proteins in the annotated F153 (V3) genome. In addition, a further 603 peptide identifications are found that map exclusively to an independent MD2 transcriptome-derived database but are not found in the standard F153 (V3) annotated proteome. Peptide identifications derived from these MD2 transcripts are also cross-referenced to a more recent and complete MD2 genome annotation, resulting in 402 nonoverlapping peptides, which in turn support 30 high-quality gene candidates novel to both pineapple genomes. Many of the validated F153 (V3) genes are also supported by an independent proteomics data set collected for an ornamental pineapple variety. The contigs and peptides have been mapped to the current F153 genome build and are available as bed files to display a custom gene track on the Ensembl Plants region viewer. These analyses add to the knowledge of experimentally validated pineapple genes and demonstrate the utility of transcript-derived proteomics to discover both novel genes and genetic structure in a plant genome, adding value to its annotation.
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Affiliation(s)
- Norazrin Ariffin
- School
of Biological Sciences, Faculty of Biology Medicine and Health, MAHSC, University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
- Department
of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor Darul Ehsan, Malaysia
| | - David Wells Newman
- School
of Biological Sciences, Faculty of Biology Medicine and Health, MAHSC, University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
| | - Michael G. Nelson
- School
of Biological Sciences, Faculty of Biology Medicine and Health, MAHSC, University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
| | - Ronan O’cualain
- School
of Biological Sciences, Faculty of Biology Medicine and Health, MAHSC, University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
| | - Simon J. Hubbard
- School
of Biological Sciences, Faculty of Biology Medicine and Health, MAHSC, University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, United Kingdom
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2
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Provencher N, Leblanc S, Jacques JF, Roucou X. Exploring the Alternative Proteome with OpenProt and Mass Spectrometry. Methods Mol Biol 2024; 2836:3-17. [PMID: 38995532 DOI: 10.1007/978-1-0716-4007-4_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Proteogenomics has revealed the translation of unannotated open reading frames (ORFs) present in mRNAs and in noncoding RNAs (ncRNAs). OpenProt annotates all ORFs with a minimum of 30 codons in the transcriptome of several species and displays many functional features associated with the corresponding proteins. Two types of proteins are annotated: reference or canonical proteins which are proteins already annotated in UniProt, RefSeq, or Ensembl and noncanonical proteins. Noncanonical proteins form two groups: predicted novel isoforms that display a significant level of homology with a reference protein and alternative proteins that are new proteins with no significant homology to known proteins. This chapter describes how to check whether a gene and/or transcript contains multiple open reading frames and how to use OpenProt databases for the detection of alternative proteins and novel isoforms by mass spectrometry-based proteomics.
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Affiliation(s)
- Nicolas Provencher
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Sébastien Leblanc
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Jean-François Jacques
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Xavier Roucou
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada.
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, QC, Canada.
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3
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Oreper D, Klaeger S, Jhunjhunwala S, Delamarre L. The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens. Semin Immunol 2023; 67:101758. [PMID: 37027981 DOI: 10.1016/j.smim.2023.101758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 04/08/2023]
Abstract
Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.
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Affiliation(s)
- Daniel Oreper
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
| | - Susan Klaeger
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
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4
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Malekos E, Carpenter S. Short open reading frame genes in innate immunity: from discovery to characterization. Trends Immunol 2022; 43:741-756. [PMID: 35965152 PMCID: PMC10118063 DOI: 10.1016/j.it.2022.07.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 12/27/2022]
Abstract
Next-generation sequencing (NGS) technologies have greatly expanded the size of the known transcriptome. Many newly discovered transcripts are classified as long noncoding RNAs (lncRNAs) which are assumed to affect phenotype through sequence and structure and not via translated protein products despite the vast majority of them harboring short open reading frames (sORFs). Recent advances have demonstrated that the noncoding designation is incorrect in many cases and that sORF-encoded peptides (SEPs) translated from these transcripts are important contributors to diverse biological processes. Interest in SEPs is at an early stage and there is evidence for the existence of thousands of SEPs that are yet unstudied. We hope to pique interest in investigating this unexplored proteome by providing a discussion of SEP characterization generally and describing specific discoveries in innate immunity.
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Affiliation(s)
- Eric Malekos
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA; Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Susan Carpenter
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA; Department of Molecular Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA.
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5
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Fancello L, Burger T. An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics. Genome Biol 2022; 23:132. [PMID: 35725496 PMCID: PMC9208142 DOI: 10.1186/s13059-022-02701-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 06/09/2022] [Indexed: 12/03/2022] Open
Abstract
Background Proteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome- or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases, which only contain proteins whose transcripts are detected in the sample-matched transcriptome. These were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach. Results We establish that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases. As anti-conservative false discovery rates (FDRs) are likely to hamper the robustness of the resulting biological conclusions, we advocate for alternative FDR control methods that are less sensitive to database size. Nevertheless, reduced transcriptome-informed databases are useful, as they reduce the ambiguity of protein identifications, yielding fewer shared peptides. Furthermore, searching the reference database and subsequently filtering proteins whose transcripts are not expressed reduces protein identification ambiguity to a similar extent, but is more transparent and reproducible. Conclusions In summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce the ambiguity of protein identifications. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02701-2.
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Affiliation(s)
- Laura Fancello
- CNRS, CEA, Inserm, BioSanté U1292, Profi FR2048, Université Grenoble Alpes, Grenoble, France
| | - Thomas Burger
- CNRS, CEA, Inserm, BioSanté U1292, Profi FR2048, Université Grenoble Alpes, Grenoble, France.
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6
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Tay AP, Hamey JJ, Martyn GE, Wilson LOW, Wilkins MR. Identification of Protein Isoforms Using Reference Databases Built from Long and Short Read RNA-Sequencing. J Proteome Res 2022; 21:1628-1639. [PMID: 35612954 DOI: 10.1021/acs.jproteome.1c00968] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Alternative splicing can lead to distinct protein isoforms. These can have different functions in specific cells and tissues or in different developmental stages. In this study, we explored whether transcripts assembled from long read, nanopore-based, direct RNA-sequencing (RNA-seq) could improve the identification of protein isoforms in human K562 cells. By comparing with Illumina-based short read RNA-seq, we showed that a large proportion of Ensembl transcripts (5949/14,326) and genes expressing alternatively spliced transcripts (486/2981) identified with long direct reads were missed by short paired-end reads. By co-analyzing proteomic and transcriptomic data, we also showed that some peptides (826/35,976), proteins (262/3215), and protein isoforms arising from distinct transcript variants (574/1212) identified with isoform-specific peptides via custom long-read-based databases were missed in Illumina-derived databases. Finally, we generated unequivocal peptide evidence for a set of protein isoforms and showed that long read, direct RNA-seq allows the discovery of novel protein isoforms not already in reference databases or custom databases built from short read RNA-seq data. Our analysis highlights the benefits of long read RNA-seq data in the generation of reference databases to increase tandem mass spectrometry (MS/MS) identification of protein isoforms.
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Affiliation(s)
- Aidan P Tay
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, New South Wales 2052, Australia.,Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Sydney, New South Wales 2113, Australia.,Applied Biosciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Joshua J Hamey
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Gabriella E Martyn
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Laurence O W Wilson
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Sydney, New South Wales 2113, Australia.,Applied Biosciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Marc R Wilkins
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, New South Wales 2052, Australia
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7
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Aggarwal S, Raj A, Kumar D, Dash D, Yadav AK. False discovery rate: the Achilles' heel of proteogenomics. Brief Bioinform 2022; 23:6582880. [PMID: 35534181 DOI: 10.1093/bib/bbac163] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/14/2022] [Accepted: 04/12/2022] [Indexed: 12/25/2022] Open
Abstract
Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.
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Affiliation(s)
- Suruchi Aggarwal
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd milestone, PO Box No. 04, Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
| | - Anurag Raj
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics & Integrative Biology, South Campus, Mathura Road, New Delhi 110025, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Dhirendra Kumar
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics & Integrative Biology, South Campus, Mathura Road, New Delhi 110025, India
| | - Debasis Dash
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics & Integrative Biology, South Campus, Mathura Road, New Delhi 110025, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Amit Kumar Yadav
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd milestone, PO Box No. 04, Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
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8
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Zhu H, Jiang S, Zhou W, Chi H, Sun J, Shi J, Zhang Z, Chang L, Yu L, Zhang L, Lyu Z, Xu P, Zhang Y. Ac-LysargiNase efficiently helps genome reannotation of Mycolicibacterium smegmatis MC2 155. J Proteomics 2022; 264:104622. [DOI: 10.1016/j.jprot.2022.104622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/10/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022]
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9
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IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry. Biomolecules 2022; 12:biom12040579. [PMID: 35454168 PMCID: PMC9025654 DOI: 10.3390/biom12040579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 01/02/2023] Open
Abstract
Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search, and then build a targeted database for refined search. Evaluated on 18 representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 76%, compared to conventional searches with unspecific digestions, while maintaining a very high level of accuracy (~96%), as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data, so that it performs equally well on both well-studied and poorly-studied HLA types, unlike the previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to a conventional database search. Finally, we demonstrate the practical value of IntroSpect by discovering neoepitopes from MS data directly, an important application in cancer immunotherapies. IntroSpect is freely available to download and use.
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10
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Ahrens CH, Wade JT, Champion MM, Langer JD. A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry. J Bacteriol 2022; 204:e0035321. [PMID: 34748388 PMCID: PMC8765459 DOI: 10.1128/jb.00353-21] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Small proteins of up to ∼50 amino acids are an abundant class of biomolecules across all domains of life. Yet due to the challenges inherent in their size, they are often missed in genome annotations, and are difficult to identify and characterize using standard experimental approaches. Consequently, we still know few small proteins even in well-studied prokaryotic model organisms. Mass spectrometry (MS) has great potential for the discovery, validation, and functional characterization of small proteins. However, standard MS approaches are poorly suited to the identification of both known and novel small proteins due to limitations at each step of a typical proteomics workflow, i.e., sample preparation, protease digestion, liquid chromatography, MS data acquisition, and data analysis. Here, we outline the major MS-based workflows and bioinformatic pipelines used for small protein discovery and validation. Special emphasis is placed on highlighting the adjustments required to improve detection and data quality for small proteins. We discuss both the unbiased detection of small proteins and the targeted analysis of small proteins of interest. Finally, we provide guidelines to prioritize novel small proteins, and an outlook on methods with particular potential to further improve comprehensive discovery and characterization of small proteins.
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Affiliation(s)
- Christian H. Ahrens
- Agroscope, Method Development and Analytics & SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Joseph T. Wade
- Wadsworth Center, New York State Department of Health, Albany, New York, USA
- Department of Biomedical Sciences, School of Public Health, University at Albany, Albany, New York, USA
| | - Matthew M. Champion
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana, USA
| | - Julian D. Langer
- Mass Spectrometry and Proteomics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
- Proteomics, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
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11
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Salz R, Bouwmeester R, Gabriels R, Degroeve S, Martens L, Volders PJ, 't Hoen PAC. Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection. J Proteome Res 2021; 20:3353-3364. [PMID: 33998808 PMCID: PMC8280751 DOI: 10.1021/acs.jproteome.1c00264] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Indexed: 12/30/2022]
Abstract
Discovery of variant peptides such as a single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA sequences and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false-positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.
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Affiliation(s)
- Renee Salz
- Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Pieter-Jan Volders
- VIB-UGent Center for Medical Biotechnology VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
| | - Peter A C 't Hoen
- Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
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12
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Verbruggen S, Gessulat S, Gabriels R, Matsaroki A, Van de Voorde H, Kuster B, Degroeve S, Martens L, Van Criekinge W, Wilhelm M, Menschaert G. Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Mol Cell Proteomics 2021; 20:100076. [PMID: 33823297 PMCID: PMC8214147 DOI: 10.1016/j.mcpro.2021.100076] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/04/2021] [Accepted: 03/25/2021] [Indexed: 11/17/2022] Open
Abstract
Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting. First proteogenomics with PSM rescoring using machine learning–predicted spectra Demonstrated on both ribosome profiling and nanopore RNA-Seq–derived databases Rescoring leads to elevated stringency and increased identification rates Rescoring compensates for the search space size issues in proteogenomics
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Affiliation(s)
- Steven Verbruggen
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; OHMX.bio, Ghent, Belgium
| | - Siegfried Gessulat
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Ralf Gabriels
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | | | | | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Sven Degroeve
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Lennart Martens
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
| | - Wim Van Criekinge
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Gerben Menschaert
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; OHMX.bio, Ghent, Belgium.
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13
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Ruiz Cuevas MV, Hardy MP, Hollý J, Bonneil É, Durette C, Courcelles M, Lanoix J, Côté C, Staudt LM, Lemieux S, Thibault P, Perreault C, Yewdell JW. Most non-canonical proteins uniquely populate the proteome or immunopeptidome. Cell Rep 2021; 34:108815. [PMID: 33691108 PMCID: PMC8040094 DOI: 10.1016/j.celrep.2021.108815] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 01/29/2021] [Accepted: 02/10/2021] [Indexed: 12/16/2022] Open
Abstract
Combining RNA sequencing, ribosome profiling, and mass spectrometry, we elucidate the contribution of non-canonical translation to the proteome and major histocompatibility complex (MHC) class I immunopeptidome. Remarkably, of 14,498 proteins identified in three human B cell lymphomas, 2,503 are non-canonical proteins. Of these, 28% are novel isoforms and 72% are cryptic proteins encoded by ostensibly non-coding regions (60%) or frameshifted canonical genes (12%). Cryptic proteins are translated as efficiently as canonical proteins, have more predicted disordered residues and lower stability, and critically generate MHC-I peptides 5-fold more efficiently per translation event. Translating 5' "untranslated" regions hinders downstream translation of genes involved in transcription, translation, and antiviral responses. Novel protein isoforms show strong enrichment for signaling pathways deregulated in cancer. Only a small fraction of cryptic proteins detected in the proteome contribute to the MHC-I immunopeptidome, demonstrating the high preferential access of cryptic defective ribosomal products to the class I pathway.
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Affiliation(s)
- Maria Virginia Ruiz Cuevas
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada; Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Marie-Pierre Hardy
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Jaroslav Hollý
- Cellular Biology Section, Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Éric Bonneil
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Chantal Durette
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Mathieu Courcelles
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Joël Lanoix
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Caroline Côté
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Louis M Staudt
- Lymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sébastien Lemieux
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada; Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada; Department of Chemistry, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC H3C 3J7, Canada; Department of Medicine, Université de Montréal, Montreal, QC H3C 3J7, Canada.
| | - Jonathan W Yewdell
- Cellular Biology Section, Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
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14
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Petruschke H, Schori C, Canzler S, Riesbeck S, Poehlein A, Daniel R, Frei D, Segessemann T, Zimmerman J, Marinos G, Kaleta C, Jehmlich N, Ahrens CH, von Bergen M. Discovery of novel community-relevant small proteins in a simplified human intestinal microbiome. MICROBIOME 2021; 9:55. [PMID: 33622394 PMCID: PMC7903761 DOI: 10.1186/s40168-020-00981-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 12/16/2020] [Indexed: 05/13/2023]
Abstract
BACKGROUND The intestinal microbiota plays a crucial role in protecting the host from pathogenic microbes, modulating immunity and regulating metabolic processes. We studied the simplified human intestinal microbiota (SIHUMIx) consisting of eight bacterial species with a particular focus on the discovery of novel small proteins with less than 100 amino acids (= sProteins), some of which may contribute to shape the simplified human intestinal microbiota. Although sProteins carry out a wide range of important functions, they are still often missed in genome annotations, and little is known about their structure and function in individual microbes and especially in microbial communities. RESULTS We created a multi-species integrated proteogenomics search database (iPtgxDB) to enable a comprehensive identification of novel sProteins. Six of the eight SIHUMIx species, for which no complete genomes were available, were sequenced and de novo assembled. Several proteomics approaches including two earlier optimized sProtein enrichment strategies were applied to specifically increase the chances for novel sProtein discovery. The search of tandem mass spectrometry (MS/MS) data against the multi-species iPtgxDB enabled the identification of 31 novel sProteins, of which the expression of 30 was supported by metatranscriptomics data. Using synthetic peptides, we were able to validate the expression of 25 novel sProteins. The comparison of sProtein expression in each single strain versus a multi-species community cultivation showed that six of these sProteins were only identified in the SIHUMIx community indicating a potentially important role of sProteins in the organization of microbial communities. Two of these novel sProteins have a potential antimicrobial function. Metabolic modelling revealed that a third sProtein is located in a genomic region encoding several enzymes relevant for the community metabolism within SIHUMIx. CONCLUSIONS We outline an integrated experimental and bioinformatics workflow for the discovery of novel sProteins in a simplified intestinal model system that can be generically applied to other microbial communities. The further analysis of novel sProteins uniquely expressed in the SIHUMIx multi-species community is expected to enable new insights into the role of sProteins on the functionality of bacterial communities such as those of the human intestinal tract. Video abstract.
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Affiliation(s)
- Hannes Petruschke
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Christian Schori
- Agroscope, Molecular Diagnostics, Genomics & Bioinformatics and SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Sebastian Canzler
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Sarah Riesbeck
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Anja Poehlein
- Institute of Microbiology and Genetics, Department of Genomic and Applied Microbiology, Georg-August University of Göttingen, Göttingen, Germany
| | - Rolf Daniel
- Institute of Microbiology and Genetics, Department of Genomic and Applied Microbiology, Georg-August University of Göttingen, Göttingen, Germany
| | - Daniel Frei
- Agroscope, Molecular Diagnostics, Genomics & Bioinformatics and SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Tina Segessemann
- Agroscope, Molecular Diagnostics, Genomics & Bioinformatics and SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Johannes Zimmerman
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Georgios Marinos
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Christian H Ahrens
- Agroscope, Molecular Diagnostics, Genomics & Bioinformatics and SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland.
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany.
- Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, University of Leipzig, Leipzig, Germany.
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15
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Schiebenhoefer H, Schallert K, Renard BY, Trappe K, Schmid E, Benndorf D, Riedel K, Muth T, Fuchs S. A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane. Nat Protoc 2020; 15:3212-3239. [PMID: 32859984 DOI: 10.1038/s41596-020-0368-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 05/29/2020] [Indexed: 12/14/2022]
Abstract
Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.
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Affiliation(s)
- Henning Schiebenhoefer
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Kay Schallert
- Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Hasso Plattner Institute, Faculty for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Kathrin Trappe
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
| | - Emanuel Schmid
- ID Computational & Data Science Support, Eidgenössische Technische Hochschule, Zurich, Switzerland
| | - Dirk Benndorf
- Bioprocess Engineering, Otto von Guericke University, Magdeburg, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Katharina Riedel
- Center for Functional Genomics of Microbes (CFGM), Institute of Microbiology, University of Greifswald, Greifswald, Germany
| | - Thilo Muth
- Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany
- Section S.3 eScience, Federal Institute for Materials Research and Testing (BAM), Berlin, Germany
| | - Stephan Fuchs
- Department of Infectious Diseases, Robert Koch Institute, Wernigerode, Germany.
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16
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Bouwmeester R, Gabriels R, Van Den Bossche T, Martens L, Degroeve S. The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows. Proteomics 2020; 20:e1900351. [PMID: 32267083 DOI: 10.1002/pmic.201900351] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 03/21/2020] [Indexed: 12/30/2022]
Abstract
A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. Therefore, highly promising recent machine learning developments in proteomics are pointed out in this viewpoint, alongside some of the remaining challenges.
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Affiliation(s)
- Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Albert Baertsoenkaai 3, B-9000, Ghent, Belgium
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17
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Zhang Z, Zhang S, Li X, Zhao Z, Chen C, Zhang J, Li M, Wei Z, Jiang W, Pan B, Li Y, Liu Y, Cao Y, Zhao W, Gu Y, Yu Y, Meng Q, Qi L. Reference genome and annotation updates lead to contradictory prognostic predictions in gene expression signatures: a case study of resected stage I lung adenocarcinoma. Brief Bioinform 2020; 22:5834482. [PMID: 32383445 DOI: 10.1093/bib/bbaa081] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/02/2020] [Accepted: 04/18/2020] [Indexed: 12/28/2022] Open
Abstract
RNA-sequencing enables accurate and low-cost transcriptome-wide detection. However, expression estimates vary as reference genomes and gene annotations are updated, confounding existing expression-based prognostic signatures. Herein, prognostic 9-gene pair signature (GPS) was applied to 197 patients with stage I lung adenocarcinoma derived from previous and latest data from The Cancer Genome Atlas (TCGA) processed with different reference genomes and annotations. For 9-GPS, 6.6% of patients exhibited discordant risk classifications between the two TCGA versions. Similar results were observed for other prognostic signatures, including IRGPI, 15-gene and ORACLE. We found that conflicting annotations for gene length and overlap were the major cause of their discordant risk classification. Therefore, we constructed a prognostic 40-GPS based on stable genes across GENCODE v20-v30 and validated it using public data of 471 stage I samples (log-rank P < 0.0010). Risk classification was still stable in RNA-sequencing data processed with the newest GENCODE v32 versus GENCODE v20-v30. Specifically, 40-GPS could predict survival for 30 stage I samples with formalin-fixed paraffin-embedded tissues (log-rank P = 0.0177). In conclusion, this method overcomes the vulnerability of existing prognostic signatures due to reference genome and annotation updates. 40-GPS may offer individualized clinical applications due to its prognostic accuracy and classification stability.
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18
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Chong C, Müller M, Pak H, Harnett D, Huber F, Grun D, Leleu M, Auger A, Arnaud M, Stevenson BJ, Michaux J, Bilic I, Hirsekorn A, Calviello L, Simó-Riudalbas L, Planet E, Lubiński J, Bryśkiewicz M, Wiznerowicz M, Xenarios I, Zhang L, Trono D, Harari A, Ohler U, Coukos G, Bassani-Sternberg M. Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes. Nat Commun 2020; 11:1293. [PMID: 32157095 PMCID: PMC7064602 DOI: 10.1038/s41467-020-14968-9] [Citation(s) in RCA: 173] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 02/12/2020] [Indexed: 12/20/2022] Open
Abstract
Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides derived from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single-cell transcriptomics, ribosome profiling, and two MS/MS search tools in combination. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides, including an immunogenic peptide derived from an open reading frame downstream of the melanoma stem cell marker gene ABCB5. These findings hold great promise for the discovery of previously unknown tumor antigens for cancer immunotherapy.
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Affiliation(s)
- Chloe Chong
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Markus Müller
- Vital IT, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015, Lausanne, Switzerland
| | - HuiSong Pak
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Dermot Harnett
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Delphine Grun
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
| | - Marion Leleu
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015, Lausanne, Switzerland
| | - Aymeric Auger
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Brian J Stevenson
- Vital IT, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015, Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Ilija Bilic
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
| | - Antje Hirsekorn
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
| | - Lorenzo Calviello
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
| | - Laia Simó-Riudalbas
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
| | - Evarist Planet
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
| | - Jan Lubiński
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, ul. Rybacka 1, 70-204, Szczecin, Poland
- International Institute for Molecular Oncology, Jakuba Krauthofera 23, 60-203, Poznań, Poland
| | - Marta Bryśkiewicz
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, ul. Rybacka 1, 70-204, Szczecin, Poland
- International Institute for Molecular Oncology, Jakuba Krauthofera 23, 60-203, Poznań, Poland
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, Jakuba Krauthofera 23, 60-203, Poznań, Poland
- Poznan University of Medical Sciences, Fredry 10, 61-701, Poznań, Poland
| | - Ioannis Xenarios
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Genome Center Health 2030, Chemin de Mines 9, 1202, Genève, Switzerland
- Department of Training and Research, CHUV/UNIL Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
| | - Lin Zhang
- Center for Research on Reproduction and Women's Health, University of Pennsylvania, 421 Curie Boulevard, Philadelphia, PA, 19104, USA
- Department of Obstetrics and Gynecology, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Didier Trono
- School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015, Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Uwe Ohler
- Max Delbrück Centre for Molecular Medicine in the Helmholtz Association, Institute for Medical Systems Biology, Hannoversche Straße 28, 10115, Berlin, Germany
- Departments of Biology and Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
| | - George Coukos
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center, Rue du Bugnon 25A, 1005, Lausanne, Switzerland.
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland.
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19
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Developing Well-Annotated Species-Specific Protein Databases Using Comparative Proteogenomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1140:389-400. [PMID: 31347060 DOI: 10.1007/978-3-030-15950-4_22] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Proteomics is a mass spectrometry-based discipline that aims to analyze proteomes and their functions. Many proteomic studies require well-developed protein databases for reference. However, most proteomes are not well-annotated, aside from model organisms. Techniques like six-frame translation, ab initio gene prediction, and EST databases can aid in maximizing the amount of proteins identified in proteomics experiments, however, each of these has its downfalls. Proteogenomics is a term used to describe the union of proteomics, genomics and transcriptomics to assist in the identification of peptides which would help build better annotated proteome databases. Here, current proteomic and proteogenomic methods will be reviewed, and an example of a comparative proteomics method using lake trout liver samples will be described.
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20
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Machado KCT, Fortuin S, Tomazella GG, Fonseca AF, Warren RM, Wiker HG, de Souza SJ, de Souza GA. On the Impact of the Pangenome and Annotation Discrepancies While Building Protein Sequence Databases for Bacteria Proteogenomics. Front Microbiol 2019; 10:1410. [PMID: 31281302 PMCID: PMC6596428 DOI: 10.3389/fmicb.2019.01410] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 06/05/2019] [Indexed: 01/19/2023] Open
Abstract
In proteomics, peptide information within mass spectrometry (MS) data from a specific organism sample is routinely matched against a protein sequence database that best represent such organism. However, if the species/strain in the sample is unknown or genetically poorly characterized, it becomes challenging to determine a database which can represent such sample. Building customized protein sequence databases merging multiple strains for a given species has become a strategy to overcome such restrictions. However, as more genetic information is publicly available and interesting genetic features such as the existence of pan- and core genes within a species are revealed, we questioned how efficient such merging strategies are to report relevant information. To test this assumption, we constructed databases containing conserved and unique sequences for 10 different species. Features that are relevant for probabilistic-based protein identification by proteomics were then monitored. As expected, increase in database complexity correlates with pangenomic complexity. However, Mycobacterium tuberculosis and Bordetella pertussis generated very complex databases even having low pangenomic complexity. We further tested database performance by using MS data from eight clinical strains from M. tuberculosis, and from two published datasets from Staphylococcus aureus. We show that by using an approach where database size is controlled by removing repeated identical tryptic sequences across strains/species, computational time can be reduced drastically as database complexity increases.
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Affiliation(s)
- Karla C T Machado
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Suereta Fortuin
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Gisele Guicardi Tomazella
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
- The Gade Research Group for Infection and Immunity, Department of Clinical Science, University of Bergen, Bergen, Norway
- The Institute of Bioinformatics and Biotechnology, Natal, Brazil
| | - Andre F Fonseca
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Robin Mark Warren
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Harald G Wiker
- The Gade Research Group for Infection and Immunity, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Sandro Jose de Souza
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
- The Brain Institute, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Gustavo Antonio de Souza
- Bioinformatics Multidisciplinary Environment, Universidade Federal do Rio Grande do Norte, Natal, Brazil
- Department of Biochemistry, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
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21
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Weldatsadik R, Datta N, Kolmeder C, Vuopio J, Kere J, Wilkman S, Flatt J, Vuento R, Haapasalo K, Keskitalo S, Varjosalo M, Jokiranta T. Pool-seq driven proteogenomic database for Group G Streptococcus. J Proteomics 2019; 201:84-92. [DOI: 10.1016/j.jprot.2019.04.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 03/29/2019] [Accepted: 04/17/2019] [Indexed: 02/07/2023]
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22
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Schiebenhoefer H, Van Den Bossche T, Fuchs S, Renard BY, Muth T, Martens L. Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis. Expert Rev Proteomics 2019; 16:375-390. [PMID: 31002542 DOI: 10.1080/14789450.2019.1609944] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.
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Affiliation(s)
- Henning Schiebenhoefer
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Tim Van Den Bossche
- b VIB - UGent Center for Medical Biotechnology, VIB , Ghent , Belgium.,c Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences , Ghent University , Ghent , Belgium
| | - Stephan Fuchs
- d FG13 Division of Nosocomial Pathogens and Antibiotic Resistances , Robert Koch Institute , Wernigerode , Germany
| | - Bernhard Y Renard
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Thilo Muth
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Lennart Martens
- b VIB - UGent Center for Medical Biotechnology, VIB , Ghent , Belgium.,c Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences , Ghent University , Ghent , Belgium
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23
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Omasits U, Varadarajan AR, Schmid M, Goetze S, Melidis D, Bourqui M, Nikolayeva O, Québatte M, Patrignani A, Dehio C, Frey JE, Robinson MD, Wollscheid B, Ahrens CH. An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics. Genome Res 2017; 27:2083-2095. [PMID: 29141959 PMCID: PMC5741054 DOI: 10.1101/gr.218255.116] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 10/25/2017] [Indexed: 12/18/2022]
Abstract
Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics data set against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and single amino acid variations (SAAVs) identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin. We release iPtgxDBs for B. henselae, Bradyrhizobium diazoefficiens and Escherichia coli and the software to generate both proteogenomics search databases and integrated annotation files that can be viewed in a genome browser for any prokaryote.
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Affiliation(s)
- Ulrich Omasits
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Adithi R Varadarajan
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland.,Department of Health Sciences and Technology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology Zurich, CH-8093 Zurich, Switzerland
| | - Michael Schmid
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Sandra Goetze
- Department of Health Sciences and Technology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology Zurich, CH-8093 Zurich, Switzerland
| | - Damianos Melidis
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Marc Bourqui
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Olga Nikolayeva
- Institute for Molecular Life Sciences & SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland
| | | | - Andrea Patrignani
- Functional Genomics Center Zurich, ETH & UZH Zurich, CH-8057 Zurich, Switzerland
| | | | - Juerg E Frey
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Mark D Robinson
- Institute for Molecular Life Sciences & SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland
| | - Bernd Wollscheid
- Department of Health Sciences and Technology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology Zurich, CH-8093 Zurich, Switzerland
| | - Christian H Ahrens
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
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24
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Heunis T, Dippenaar A, Warren RM, van Helden PD, van der Merwe RG, Gey van Pittius NC, Pain A, Sampson SL, Tabb DL. Proteogenomic Investigation of Strain Variation in Clinical Mycobacterium tuberculosis Isolates. J Proteome Res 2017; 16:3841-3851. [PMID: 28820946 DOI: 10.1021/acs.jproteome.7b00483] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Mycobacterium tuberculosis consists of a large number of different strains that display unique virulence characteristics. Whole-genome sequencing has revealed substantial genetic diversity among clinical M. tuberculosis isolates, and elucidating the phenotypic variation encoded by this genetic diversity will be of the utmost importance to fully understand M. tuberculosis biology and pathogenicity. In this study, we integrated whole-genome sequencing and mass spectrometry (GeLC-MS/MS) to reveal strain-specific characteristics in the proteomes of two clinical M. tuberculosis Latin American-Mediterranean isolates. Using this approach, we identified 59 peptides containing single amino acid variants, which covered ∼9% of all coding nonsynonymous single nucleotide variants detected by whole-genome sequencing. Furthermore, we identified 29 distinct peptides that mapped to a hypothetical protein not present in the M. tuberculosis H37Rv reference proteome. Here, we provide evidence for the expression of this protein in the clinical M. tuberculosis SAWC3651 isolate. The strain-specific databases enabled confirmation of genomic differences (i.e., large genomic regions of difference and nonsynonymous single nucleotide variants) in these two clinical M. tuberculosis isolates and allowed strain differentiation at the proteome level. Our results contribute to the growing field of clinical microbial proteogenomics and can improve our understanding of phenotypic variation in clinical M. tuberculosis isolates.
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Affiliation(s)
- Tiaan Heunis
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Anzaan Dippenaar
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Robin M Warren
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Paul D van Helden
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Ruben G van der Merwe
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Nicolaas C Gey van Pittius
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Arnab Pain
- Pathogen Genomics Laboratory, BESE Division, King Abdullah University of Science and Technology , Thuwal 23955, Saudi Arabia
| | - Samantha L Sampson
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - David L Tabb
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
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25
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Wingo TS, Duong DM, Zhou M, Dammer EB, Wu H, Cutler DJ, Lah JJ, Levey AI, Seyfried NT. Integrating Next-Generation Genomic Sequencing and Mass Spectrometry To Estimate Allele-Specific Protein Abundance in Human Brain. J Proteome Res 2017; 16:3336-3347. [PMID: 28691493 DOI: 10.1021/acs.jproteome.7b00324] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Gene expression contributes to phenotypic traits and human disease. To date, comparatively less is known about regulators of protein abundance, which is also under genetic control and likely influences clinical phenotypes. However, identifying and quantifying allele-specific protein abundance by bottom-up proteomics is challenging since single nucleotide variants (SNVs) that alter protein sequence are not considered in standard human protein databases. To address this, we developed the GenPro software and used it to create personalized protein databases (PPDs) to identify single amino acid variants (SAAVs) at the protein level from whole exome sequencing. In silico assessment of PPDs generated by GenPro revealed only a 1% increase in tryptic search space compared to a direct translation of all human transcripts and an equivalent search space compared to the UniProtKB reference database. To identify a large unbiased number of SAAV peptides, we performed high-resolution mass spectrometry-based proteomics for two human post-mortem brain samples and searched the collected MS/MS spectra against their respective PPD. We found an average of ∼117 000 unique peptides mapping to ∼9300 protein groups for each sample, and of these, 977 were unique variant peptides. We found that over 400 reference and SAAV peptide pairs were, on average, equally abundant in human brain by label-free ion intensity measurements and confirmed the absolute levels of three reference and SAAV peptide pairs using heavy labeled peptides standards coupled with parallel reaction monitoring (PRM). Our results highlight the utility of integrating genomic and proteomic sequencing data to identify sample-specific SAAV peptides and support the hypothesis that most alleles are equally expressed in human brain.
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Affiliation(s)
- Thomas S Wingo
- Division of Neurology, Department of Veterans Affairs Medical Center , Decatur, Georgia 30033, United States
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26
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Kroll JE, da Silva VL, de Souza SJ, de Souza GA. A tool for integrating genetic and mass spectrometry-based peptide data: Proteogenomics Viewer. Bioessays 2017; 39. [DOI: 10.1002/bies.201700015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- José Eduardo Kroll
- Institute of Bioinformatics and Biotechnology; Natal − RN Brazil
- Brain Institute; Universidade Federal do Rio Grande do Norte; Natal − RN Brazil
- Bioinformatics Multidisciplinary Environment; Instituto Metrópole Digital; UFRN, Natal-RN Brazil
| | - Vandeclécio Lira da Silva
- Brain Institute; Universidade Federal do Rio Grande do Norte; Natal − RN Brazil
- Bioinformatics Multidisciplinary Environment; Instituto Metrópole Digital; UFRN, Natal-RN Brazil
| | - Sandro José de Souza
- Brain Institute; Universidade Federal do Rio Grande do Norte; Natal − RN Brazil
- Bioinformatics Multidisciplinary Environment; Instituto Metrópole Digital; UFRN, Natal-RN Brazil
| | - Gustavo Antonio de Souza
- Brain Institute; Universidade Federal do Rio Grande do Norte; Natal − RN Brazil
- Bioinformatics Multidisciplinary Environment; Instituto Metrópole Digital; UFRN, Natal-RN Brazil
- Department of Immunology and Centre for Immune Regulation, Oslo University Hospital HF Rikshospitalet; University of Oslo; Oslo Norway
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27
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Li H, Park J, Kim H, Hwang KB, Paek E. Systematic Comparison of False-Discovery-Rate-Controlling Strategies for Proteogenomic Search Using Spike-in Experiments. J Proteome Res 2017; 16:2231-2239. [PMID: 28452485 DOI: 10.1021/acs.jproteome.7b00033] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Proteogenomic searches are useful for novel peptide identification from tandem mass spectra. Usually, separate and multistage approaches are adopted to accurately control the false discovery rate (FDR) for proteogenomic search. Their performance on novel peptide identification has not been thoroughly evaluated, however, mainly due to the difficulty in confirming the existence of identified novel peptides. We simulated a proteogenomic search using a controlled, spike-in proteomic data set. After confirming that the results of the simulated proteogenomic search were similar to those of a real proteogenomic search using a human cell line data set, we evaluated the performance of six FDR control methods-global, separate, and multistage FDR estimation, respectively, coupled to a target-decoy search and a mixture model-based method-on novel peptide identification. The multistage approach showed the highest accuracy for FDR estimation. However, global and separate FDR estimation with the mixture model-based method showed higher sensitivities than others at the same true FDR. Furthermore, the mixture model-based method performed equally well when applied without or with a reduced set of decoy sequences. Considering different prior probabilities for novel and known protein identification, we recommend using mixture model-based methods with separate FDR estimation for sensitive and reliable identification of novel peptides from proteogenomic searches.
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Affiliation(s)
- Honglan Li
- School of Computer Science and Engineering, Soongsil University , Seoul 06978, Republic of Korea
| | - Jonghun Park
- Department of Computer Science, Hanyang University , Seoul 04763, Republic of Korea
| | - Hyunwoo Kim
- Scientific Data Research Center, Korea Institute of Science and Technology Information , Daejeon 34141, Republic of Korea
| | - Kyu-Baek Hwang
- School of Computer Science and Engineering, Soongsil University , Seoul 06978, Republic of Korea
| | - Eunok Paek
- Department of Computer Science, Hanyang University , Seoul 04763, Republic of Korea
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28
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Willems P, Ndah E, Jonckheere V, Stael S, Sticker A, Martens L, Van Breusegem F, Gevaert K, Van Damme P. N-terminal Proteomics Assisted Profiling of the Unexplored Translation Initiation Landscape in Arabidopsis thaliana. Mol Cell Proteomics 2017; 16:1064-1080. [PMID: 28432195 PMCID: PMC5461538 DOI: 10.1074/mcp.m116.066662] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 04/11/2017] [Indexed: 01/05/2023] Open
Abstract
Proteogenomics is an emerging research field yet lacking a uniform method of analysis. Proteogenomic studies in which N-terminal proteomics and ribosome profiling are combined, suggest that a high number of protein start sites are currently missing in genome annotations. We constructed a proteogenomic pipeline specific for the analysis of N-terminal proteomics data, with the aim of discovering novel translational start sites outside annotated protein coding regions. In summary, unidentified MS/MS spectra were matched to a specific N-terminal peptide library encompassing protein N termini encoded in the Arabidopsis thaliana genome. After a stringent false discovery rate filtering, 117 protein N termini compliant with N-terminal methionine excision specificity and indicative of translation initiation were found. These include N-terminal protein extensions and translation from transposable elements and pseudogenes. Gene prediction provided supporting protein-coding models for approximately half of the protein N termini. Besides the prediction of functional domains (partially) contained within the newly predicted ORFs, further supporting evidence of translation was found in the recently released Araport11 genome re-annotation of Arabidopsis and computational translations of sequences stored in public repositories. Most interestingly, complementary evidence by ribosome profiling was found for 23 protein N termini. Finally, by analyzing protein N-terminal peptides, an in silico analysis demonstrates the applicability of our N-terminal proteogenomics strategy in revealing protein-coding potential in species with well- and poorly-annotated genomes.
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Affiliation(s)
- Patrick Willems
- From the ‡VIB/UGent Center for Plant Systems Biology, 9052 Ghent, Belgium.,§Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent.,¶VIB/UGent Center for Medical Biotechnology, 9000 Ghent, Belgium.,‖Ghent University, Department of Biochemistry, 9000 Ghent, Belgium
| | - Elvis Ndah
- ¶VIB/UGent Center for Medical Biotechnology, 9000 Ghent, Belgium.,‖Ghent University, Department of Biochemistry, 9000 Ghent, Belgium.,**Ghent University, Department of Mathematical Modeling, Statistics and Bioinformatics, 9000 Ghent, Belgium
| | - Veronique Jonckheere
- ¶VIB/UGent Center for Medical Biotechnology, 9000 Ghent, Belgium.,‖Ghent University, Department of Biochemistry, 9000 Ghent, Belgium
| | - Simon Stael
- From the ‡VIB/UGent Center for Plant Systems Biology, 9052 Ghent, Belgium.,§Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent.,¶VIB/UGent Center for Medical Biotechnology, 9000 Ghent, Belgium.,‖Ghent University, Department of Biochemistry, 9000 Ghent, Belgium
| | - Adriaan Sticker
- ¶VIB/UGent Center for Medical Biotechnology, 9000 Ghent, Belgium.,‖Ghent University, Department of Biochemistry, 9000 Ghent, Belgium.,**Ghent University, Department of Mathematical Modeling, Statistics and Bioinformatics, 9000 Ghent, Belgium
| | - Lennart Martens
- ¶VIB/UGent Center for Medical Biotechnology, 9000 Ghent, Belgium.,‖Ghent University, Department of Biochemistry, 9000 Ghent, Belgium.,**Ghent University, Department of Mathematical Modeling, Statistics and Bioinformatics, 9000 Ghent, Belgium
| | - Frank Van Breusegem
- From the ‡VIB/UGent Center for Plant Systems Biology, 9052 Ghent, Belgium.,§Ghent University, Department of Plant Biotechnology and Bioinformatics, 9052 Ghent
| | - Kris Gevaert
- ¶VIB/UGent Center for Medical Biotechnology, 9000 Ghent, Belgium.,‖Ghent University, Department of Biochemistry, 9000 Ghent, Belgium
| | - Petra Van Damme
- ¶VIB/UGent Center for Medical Biotechnology, 9000 Ghent, Belgium; .,‖Ghent University, Department of Biochemistry, 9000 Ghent, Belgium
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29
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O'Neill JR, Pak HS, Pairo-Castineira E, Save V, Paterson-Brown S, Nenutil R, Vojtěšek B, Overton I, Scherl A, Hupp TR. Quantitative Shotgun Proteomics Unveils Candidate Novel Esophageal Adenocarcinoma (EAC)-specific Proteins. Mol Cell Proteomics 2017; 16:1138-1150. [PMID: 28336725 PMCID: PMC5461543 DOI: 10.1074/mcp.m116.065078] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 02/26/2017] [Indexed: 12/11/2022] Open
Abstract
Esophageal cancer is the eighth most common cancer worldwide and the majority of patients have systemic disease at presentation. Esophageal adenocarcinoma (OAC), the predominant subtype in western countries, is largely resistant to current chemotherapy regimens. Selective markers are needed to enhance clinical staging and to allow targeted therapies yet there are minimal proteomic data on this cancer type. After histological review, lysates from OAC and matched normal esophageal and gastric samples from seven patients were subjected to LC MS/MS after tandem mass tag labeling and OFFGEL fractionation. Patient matched samples of OAC, normal esophagus, normal stomach, lymph node metastases and uninvolved lymph nodes were used from an additional 115 patients for verification of expression by immunohistochemistry (IHC). Over six thousand proteins were identified and quantified across samples. Quantitative reproducibility was excellent between technical replicates and a moderate correlation was seen across samples with the same histology. The quantitative accuracy was verified across the dynamic range for seven proteins by immunohistochemistry (IHC) on the originating tissues. Multiple novel tumor-specific candidates are proposed and EPCAM was verified by IHC. This shotgun proteomic study of OAC used a comparative quantitative approach to reveal proteins highly expressed in specific tissue types. Novel tumor-specific proteins are proposed and EPCAM was demonstrated to be specifically overexpressed in primary tumors and lymph node metastases compared with surrounding normal tissues. This candidate and others proposed in this study could be developed as tumor-specific targets for novel clinical staging and therapeutic approaches.
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Affiliation(s)
- J Robert O'Neill
- From the ‡Edinburgh Cancer Research Centre at the Institute of Genetics and Molecular Medicine, Edinburgh University; Robert.o'.,§Department of Surgery, Royal Infirmary of Edinburgh
| | - Hui-Song Pak
- ¶Department of Human Protein Sciences, Faculty of Medicine, University of Geneva
| | - Erola Pairo-Castineira
- ‖Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh.,**MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh University
| | - Vicki Save
- ‡‡Department of Pathology, Royal Infirmary of Edinburgh
| | | | - Rudolf Nenutil
- §§Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno
| | - Bořivoj Vojtěšek
- §§Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno
| | - Ian Overton
- ‖Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh.,**MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh University
| | - Alex Scherl
- ¶Department of Human Protein Sciences, Faculty of Medicine, University of Geneva
| | - Ted R Hupp
- From the ‡Edinburgh Cancer Research Centre at the Institute of Genetics and Molecular Medicine, Edinburgh University.,§§Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno
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30
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Li H, Joh YS, Kim H, Paek E, Lee SW, Hwang KB. Evaluating the effect of database inflation in proteogenomic search on sensitive and reliable peptide identification. BMC Genomics 2016; 17:1031. [PMID: 28155652 PMCID: PMC5259817 DOI: 10.1186/s12864-016-3327-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Proteogenomics is a promising approach for various tasks ranging from gene annotation to cancer research. Databases for proteogenomic searches are often constructed by adding peptide sequences inferred from genomic or transcriptomic evidence to reference protein sequences. Such inflation of databases has potential of identifying novel peptides. However, it also raises concerns on sensitive and reliable peptide identification. Spurious peptides included in target databases may result in underestimated false discovery rate (FDR). On the other hand, inflation of decoy databases could decrease the sensitivity of peptide identification due to the increased number of high-scoring random hits. Although several studies have addressed these issues, widely applicable guidelines for sensitive and reliable proteogenomic search have hardly been available. Results To systematically evaluate the effect of database inflation in proteogenomic searches, we constructed a variety of real and simulated proteogenomic databases for yeast and human tandem mass spectrometry (MS/MS) data, respectively. Against these databases, we tested two popular database search tools with various approaches to search result validation: the target-decoy search strategy (with and without a refined scoring-metric) and a mixture model-based method. The effect of separate filtering of known and novel peptides was also examined. The results from real and simulated proteogenomic searches confirmed that separate filtering increases the sensitivity and reliability in proteogenomic search. However, no one method consistently identified the largest (or the smallest) number of novel peptides from real proteogenomic searches. Conclusions We propose to use a set of search result validation methods with separate filtering, for sensitive and reliable identification of peptides in proteogenomic search. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3327-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Honglan Li
- School of Computer Science and Engineering, Soongsil University, Seoul, 06978, Republic of Korea
| | - Yoon Sung Joh
- Department of Computer Science, Hanyang University, Seoul, 04763, Republic of Korea
| | - Hyunwoo Kim
- Scientific Data Research Center, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea
| | - Eunok Paek
- Department of Computer Science, Hanyang University, Seoul, 04763, Republic of Korea
| | - Sang-Won Lee
- Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul, 02841, Republic of Korea
| | - Kyu-Baek Hwang
- School of Computer Science and Engineering, Soongsil University, Seoul, 06978, Republic of Korea.
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31
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Abstract
Omics approaches have become popular in biology as powerful discovery tools, and currently gain in interest for diagnostic applications. Establishing the accurate genome sequence of any organism is easy, but the outcome of its annotation by means of automatic pipelines remains imprecise. Some protein-encoding genes may be missed as soon as they are specific and poorly conserved in a given taxon, while important to explain the specific traits of the organism. Translational starts are also poorly predicted in a relatively important number of cases, thus impacting the protein sequence database used in proteomics, comparative genomics, and systems biology. The use of high-throughput proteomics data to improve genome annotation is an attractive option to obtain a more comprehensive molecular picture of a given organism. Here, protocols for reannotating prokaryote genomes are described based on shotgun proteomics and derivatization of protein N-termini with a positively charged reagent coupled to high-resolution tandem mass spectrometry.
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32
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Muth T, Renard BY, Martens L. Metaproteomic data analysis at a glance: advances in computational microbial community proteomics. Expert Rev Proteomics 2016; 13:757-69. [DOI: 10.1080/14789450.2016.1209418] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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33
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Wen B, Xu S, Zhou R, Zhang B, Wang X, Liu X, Xu X, Liu S. PGA: an R/Bioconductor package for identification of novel peptides using a customized database derived from RNA-Seq. BMC Bioinformatics 2016; 17:244. [PMID: 27316337 PMCID: PMC4912784 DOI: 10.1186/s12859-016-1133-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2015] [Accepted: 06/09/2016] [Indexed: 11/27/2022] Open
Abstract
Background Peptide identification based upon mass spectrometry (MS) is generally achieved by comparison of the experimental mass spectra with the theoretically digested peptides derived from a reference protein database. Obviously, this strategy could not identify peptide and protein sequences that are absent from a reference database. A customized protein database on the basis of RNA-Seq data is thus proposed to assist with and improve the identification of novel peptides. Correspondingly, development of a comprehensive pipeline, which provides an end-to-end solution for novel peptide detection with the customized protein database, is necessary. Results A pipeline with an R package, assigned as a PGA utility, was developed that enables automated treatment to the tandem mass spectrometry (MS/MS) data acquired from different MS platforms and construction of customized protein databases based on RNA-Seq data with or without a reference genome guide. Hence, PGA can identify novel peptides and generate an HTML-based report with a visualized interface. On the basis of a published dataset, PGA was employed to identify peptides, resulting in 636 novel peptides, including 510 single amino acid polymorphism (SAP) peptides, 2 INDEL peptides, 49 splice junction peptides, and 75 novel transcript-derived peptides. The software is freely available from http://bioconductor.org/packages/PGA/, and the example reports are available at http://wenbostar.github.io/PGA/. Conclusions The pipeline of PGA, aimed at being platform-independent and easy-to-use, was successfully developed and shown to be capable of identifying novel peptides by searching the customized protein database derived from RNA-Seq data. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1133-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bo Wen
- BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Ruo Zhou
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Bing Zhang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
| | - Xiaojing Wang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, 37232, USA
| | - Xin Liu
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Siqi Liu
- BGI-Shenzhen, Shenzhen, 518083, China. .,Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
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34
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Li Y, Wang X, Cho JH, Shaw TI, Wu Z, Bai B, Wang H, Zhou S, Beach TG, Wu G, Zhang J, Peng J. JUMPg: An Integrative Proteogenomics Pipeline Identifying Unannotated Proteins in Human Brain and Cancer Cells. J Proteome Res 2016; 15:2309-20. [PMID: 27225868 DOI: 10.1021/acs.jproteome.6b00344] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Proteogenomics is an emerging approach to improve gene annotation and interpretation of proteomics data. Here we present JUMPg, an integrative proteogenomics pipeline including customized database construction, tag-based database search, peptide-spectrum match filtering, and data visualization. JUMPg creates multiple databases of DNA polymorphisms, mutations, splice junctions, partially trypticity, as well as protein fragments translated from the whole transcriptome in all six frames upon RNA-seq de novo assembly. We use a multistage strategy to search these databases sequentially, in which the performance is optimized by re-searching only unmatched high-quality spectra and reusing amino acid tags generated by the JUMP search engine. The identified peptides/proteins are displayed with gene loci using the UCSC genome browser. Then, the JUMPg program is applied to process a label-free mass spectrometry data set of Alzheimer's disease postmortem brain, uncovering 496 new peptides of amino acid substitutions, alternative splicing, frame shift, and "non-coding gene" translation. The novel protein PNMA6BL specifically expressed in the brain is highlighted. We also tested JUMPg to analyze a stable-isotope labeled data set of multiple myeloma cells, revealing 991 sample-specific peptides that include protein sequences in the immunoglobulin light chain variable region. Thus, the JUMPg program is an effective proteogenomics tool for multiomics data integration.
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Affiliation(s)
| | | | | | | | | | | | - Hong Wang
- Integrated Biomedical Sciences Program, University of Tennessee Health Science Center , 920 Madison Avenue, Memphis, Tennessee 38163, United States
| | | | - Thomas G Beach
- Banner Sun Health Research Institute , Sun City, Arizona 85351, United States
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35
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Sheynkman GM, Shortreed MR, Cesnik AJ, Smith LM. Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:521-45. [PMID: 27049631 PMCID: PMC4991544 DOI: 10.1146/annurev-anchem-071015-041722] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
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.
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Affiliation(s)
- Gloria M Sheynkman
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215;
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706; ,
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706; ,
| | - Anthony J Cesnik
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706; ,
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706; ,
- Genome Center of Wisconsin, University of Wisconsin, Madison, Wisconsin 53706;
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36
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Improving GENCODE reference gene annotation using a high-stringency proteogenomics workflow. Nat Commun 2016; 7:11778. [PMID: 27250503 PMCID: PMC4895710 DOI: 10.1038/ncomms11778] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Accepted: 04/28/2016] [Indexed: 12/16/2022] Open
Abstract
Complete annotation of the human genome is indispensable for medical research. The GENCODE consortium strives to provide this, augmenting computational and experimental evidence with manual annotation. The rapidly developing field of proteogenomics provides evidence for the translation of genes into proteins and can be used to discover and refine gene models. However, for both the proteomics and annotation groups, there is a lack of guidelines for integrating this data. Here we report a stringent workflow for the interpretation of proteogenomic data that could be used by the annotation community to interpret novel proteogenomic evidence. Based on reprocessing of three large-scale publicly available human data sets, we show that a conservative approach, using stringent filtering is required to generate valid identifications. Evidence has been found supporting 16 novel protein-coding genes being added to GENCODE. Despite this many peptide identifications in pseudogenes cannot be annotated due to the absence of orthogonal supporting evidence. Identifying and annotating functional elements in the human genome remains a challenging but important task. Here the authors propose a priority annotation score to rank identifications and suggest how proteogenomics evidence can be interpreted and what additional information substantiates protein-coding potential for annotation.
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37
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Global proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical reading frames. Nat Commun 2016; 7:10238. [PMID: 26728094 PMCID: PMC4728431 DOI: 10.1038/ncomms10238] [Citation(s) in RCA: 174] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 11/16/2015] [Indexed: 12/21/2022] Open
Abstract
In view of recent reports documenting pervasive translation outside of canonical protein-coding sequences, we wished to determine the proportion of major histocompatibility complex (MHC) class I-associated peptides (MAPs) derived from non-canonical reading frames. Here we perform proteogenomic analyses of MAPs eluted from human B cells using high-throughput mass spectrometry to probe the six-frame translation of the B-cell transcriptome. We report that ∼10% of MAPs originate from allegedly noncoding genomic sequences or exonic out-of-frame translation. The biogenesis and properties of these ‘cryptic MAPs' differ from those of conventional MAPs. Cryptic MAPs come from very short proteins with atypical C termini, and are coded by transcripts bearing long 3′UTRs enriched in destabilizing elements. Relative to conventional MAPs, cryptic MAPs display different MHC class I-binding preferences and harbour more genomic polymorphisms, some of which are immunogenic. Cryptic MAPs increase the complexity of the MAP repertoire and enhance the scope of CD8 T-cell immunosurveillance. Cryptic translation of the 'non-coding' genome is increasingly recognised, however its biological significance remains unclear. Laumont et al. employ proteogenomic techniques to map the human immunoproteome, and find that approximately 10% of MHC class I-associated peptides are cryptic.
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38
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Olexiouk V, Menschaert G. Identification of Small Novel Coding Sequences, a Proteogenomics Endeavor. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 926:49-64. [PMID: 27686805 DOI: 10.1007/978-3-319-42316-6_4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The identification of small proteins and peptides has consistently proven to be challenging. However, technological advances as well as multi-omics endeavors facilitate the identification of novel small coding sequences, leading to new insights. Specifically, the application of next generation sequencing technologies (NGS), providing accurate and sample specific transcriptome / translatome information, into the proteomics field led to more comprehensive results and new discoveries. This book chapter focuses on the inclusion of RNA-Seq and RIBO-Seq also known as ribosome profiling, an RNA-Seq based technique sequencing the +/- 30 bp long fragments captured by translating ribosomes. We emphasize the identification of micropeptides and neo-antigens, two distinct classes of small translation products, triggering our current understanding of biology. RNA-Seq is capable of capturing sample specific genomic variations, enabling focused neo-antigen identification. RIBO-Seq can identify translation events in small open reading frames which are considered to be non-coding, leading to the discovery of micropeptides. The identification of small translation products requires the integration of multi-omics data, stressing the importance of proteogenomics in this novel research area.
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Affiliation(s)
- Volodimir Olexiouk
- Lab of Bioinformatics and Computational Genomics (BioBix), Faculty of Bioscience Engineering, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Building A, Ghent, 9000, Belgium.
| | - Gerben Menschaert
- Lab of Bioinformatics and Computational Genomics (BioBix), Faculty of Bioscience Engineering, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Building A, Ghent, 9000, Belgium
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39
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Zickmann F, Renard BY. MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms. Bioinformatics 2015; 31:i106-15. [PMID: 26072472 PMCID: PMC4765881 DOI: 10.1093/bioinformatics/btv236] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Summary: Ongoing advances in high-throughput technologies have facilitated accurate proteomic measurements and provide a wealth of information on genomic and transcript level. In proteogenomics, this multi-omics data is combined to analyze unannotated organisms and to allow more accurate sample-specific predictions. Existing analysis methods still mainly depend on six-frame translations or reference protein databases that are extended by transcriptomic information or known single nucleotide polymorphisms (SNPs). However, six-frames introduce an artificial sixfold increase of the target database and SNP integration requires a suitable database summarizing results from previous experiments. We overcome these limitations by introducing MSProGene, a new method for integrative proteogenomic analysis based on customized RNA-Seq driven transcript databases. MSProGene is independent from existing reference databases or annotated SNPs and avoids large six-frame translated databases by constructing sample-specific transcripts. In addition, it creates a network combining RNA-Seq and peptide information that is optimized by a maximum-flow algorithm. It thereby also allows resolving the ambiguity of shared peptides for protein inference. We applied MSProGene on three datasets and show that it facilitates a database-independent reliable yet accurate prediction on gene and protein level and additionally identifies novel genes. Availability and implementation: MSProGene is written in Java and Python. It is open source and available at http://sourceforge.net/projects/msprogene/. Contact:renardb@rki.de
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Affiliation(s)
- Franziska Zickmann
- Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany
| | - Bernhard Y Renard
- Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany
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40
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Shanmugam AK, Nesvizhskii AI. Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics. J Proteome Res 2015; 14:5169-78. [PMID: 26569054 DOI: 10.1021/acs.jproteome.5b00504] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In shotgun proteomics, peptides are typically identified using database searching, which involves scoring acquired tandem mass spectra against peptides derived from standard protein sequence databases such as Uniprot, Refseq, or Ensembl. In this strategy, the sensitivity of peptide identification is known to be affected by the size of the search space. Therefore, creating a targeted sequence database containing only peptides likely to be present in the analyzed sample can be a useful technique for improving the sensitivity of peptide identification. In this study, we describe how targeted peptide databases can be created based on the frequency of identification in the global proteome machine database (GPMDB), the largest publicly available repository of peptide and protein identification data. We demonstrate that targeted peptide databases can be easily integrated into existing proteome analysis workflows and describe a computational strategy for minimizing any loss of peptide identifications arising from potential search space incompleteness in the targeted search spaces. We demonstrate the performance of our workflow using several data sets of varying size and sample complexity.
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Affiliation(s)
- Avinash K Shanmugam
- Department of Computational Medicine and Bioinformatics and ‡Department of Pathology, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics and ‡Department of Pathology, University of Michigan , Ann Arbor, Michigan 48109, United States
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41
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Sun H, Chen C, Shi M, Wang D, Liu M, Li D, Yang P, Li Y, Xie L. Integration of mass spectrometry and RNA-Seq data to confirm human ab initio predicted genes and lncRNAs. Proteomics 2015; 14:2760-8. [PMID: 25339270 DOI: 10.1002/pmic.201400174] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Revised: 09/22/2014] [Accepted: 10/16/2014] [Indexed: 12/14/2022]
Abstract
MS/MS has been used to improve genome annotation in various organisms. The classical approach is to construct comprehensive theoretical peptide database with six frame translation model from the whole ORF of a genome and search against this database with real MS/MS spectra. In this work we took a more focused approach, we constructed a database containing only peptides from the ab initio predicted genes from current human genome annotation, and all theoretical peptides from currently annotated lncRNAs, and searched such a database with MS/MS data from human Hela cell line. The purpose of this design is to find translation evidence for ab initio predicted genes and to rule out possible wrongly defined lncRNAs in a systematic proteogenomics effort. To validate proteogenomics results, we integrated RNA-Seq data analysis for the same Hela cell line which generated MS/MS data, and performed MRM experiment on self-cultured Hela cell line samples. Six peptides were found to support ab initio predicted genes with both RNA-Seq and MRM validations, while none was found to support a translated lncRNA. This workflow could be flexibly applied to other human samples and datasets to help further improve human gene annotation.
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Affiliation(s)
- Han Sun
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, China; Key Laboratory of Systems Biology, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai, China
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42
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Jagtap PD, Blakely A, Murray K, Stewart S, Kooren J, Johnson JE, Rhodus NL, Rudney J, Griffin TJ. Metaproteomic analysis using the Galaxy framework. Proteomics 2015; 15:3553-65. [DOI: 10.1002/pmic.201500074] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 04/25/2015] [Accepted: 06/04/2015] [Indexed: 12/22/2022]
Affiliation(s)
- Pratik D. Jagtap
- Center for Mass Spectrometry and Proteomics; University of Minnesota; Minneapolis MN USA
- Department of Biochemistry; Molecular Biology and Biophysics; University of Minnesota; Minneapolis MN USA
| | | | - Kevin Murray
- Department of Biochemistry; Molecular Biology and Biophysics; University of Minnesota; Minneapolis MN USA
| | | | - Joel Kooren
- Department of Biochemistry; Molecular Biology and Biophysics; University of Minnesota; Minneapolis MN USA
| | | | - Nelson L. Rhodus
- School of Dentistry; University of Minnesota; Minneapolis MN USA
| | - Joel Rudney
- School of Dentistry; University of Minnesota; Minneapolis MN USA
| | - Timothy J. Griffin
- Center for Mass Spectrometry and Proteomics; University of Minnesota; Minneapolis MN USA
- Department of Biochemistry; Molecular Biology and Biophysics; University of Minnesota; Minneapolis MN USA
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43
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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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44
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Tay AP, Pang CNI, Twine NA, Hart-Smith G, Harkness L, Kassem M, Wilkins MR. Proteomic Validation of Transcript Isoforms, Including Those Assembled from RNA-Seq Data. J Proteome Res 2015; 14:3541-54. [PMID: 25961807 DOI: 10.1021/pr5011394] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Human proteome analysis now requires an understanding of protein isoforms. We recently published the PG Nexus pipeline, which facilitates high confidence validation of exons and splice junctions by integrating genomics and proteomics data. Here we comprehensively explore how RNA-seq transcriptomics data, and proteomic analysis of the same sample, can identify protein isoforms. RNA-seq data from human mesenchymal (hMSC) stem cells were analyzed with our new TranscriptCoder tool to generate a database of protein isoform sequences. MS/MS data from matching hMSC samples were then matched against the TranscriptCoder-derived database, along with Ensembl and the neXtProt database. Querying the TranscriptCoder-derived or Ensembl database could unambiguously identify ∼450 protein isoforms, with isoform-specific proteotypic peptides, including candidate hMSC-specific isoforms for the genes DPYSL2 and FXR1. Where isoform-specific peptides did not exist, groups of nonisoform-specific proteotypic peptides could specifically identify many isoforms. In both the above cases, isoforms will be detectable with targeted MS/MS assays. Unfortunately, our analysis also revealed that some isoforms will be difficult to identify unambiguously as they do not have peptides that are sufficiently distinguishing. We covisualize mRNA isoforms and peptides in a genome browser to illustrate the above situations. Mass spectrometry data is available via ProteomeXchange (PXD001449).
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Affiliation(s)
- Aidan P Tay
- Systems Biology Initiative, The University of New South Wales , Sydney, New South Wales 2052, Australia.,School of Biotechnology and Biomolecular Sciences, The University of New South Wales , Sydney, New South Wales 2052, Australia
| | - Chi Nam Ignatius Pang
- Systems Biology Initiative, The University of New South Wales , Sydney, New South Wales 2052, Australia.,School of Biotechnology and Biomolecular Sciences, The University of New South Wales , Sydney, New South Wales 2052, Australia
| | - Natalie A Twine
- Systems Biology Initiative, The University of New South Wales , Sydney, New South Wales 2052, Australia.,School of Biotechnology and Biomolecular Sciences, The University of New South Wales , Sydney, New South Wales 2052, Australia
| | - Gene Hart-Smith
- Systems Biology Initiative, The University of New South Wales , Sydney, New South Wales 2052, Australia.,School of Biotechnology and Biomolecular Sciences, The University of New South Wales , Sydney, New South Wales 2052, Australia
| | - Linda Harkness
- Endocrine Research Laboratory (KMEB), Department of Endocrinology and Metabolism, Odense University Hospital & University of Southern Denmark , Odense 5230, Denmark
| | - Moustapha Kassem
- Endocrine Research Laboratory (KMEB), Department of Endocrinology and Metabolism, Odense University Hospital & University of Southern Denmark , Odense 5230, Denmark
| | - Marc R Wilkins
- Systems Biology Initiative, The University of New South Wales , Sydney, New South Wales 2052, Australia.,School of Biotechnology and Biomolecular Sciences, The University of New South Wales , Sydney, New South Wales 2052, Australia
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45
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Muth T, Kolmeder CA, Salojärvi J, Keskitalo S, Varjosalo M, Verdam FJ, Rensen SS, Reichl U, de Vos WM, Rapp E, Martens L. Navigating through metaproteomics data: a logbook of database searching. Proteomics 2015; 15:3439-53. [PMID: 25778831 DOI: 10.1002/pmic.201400560] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Revised: 02/13/2015] [Accepted: 03/06/2015] [Indexed: 11/12/2022]
Abstract
Metaproteomic research involves various computational challenges during the identification of fragmentation spectra acquired from the proteome of a complex microbiome. These issues are manifold and range from the construction of customized sequence databases, the optimal setting of search parameters to limitations in the identification search algorithms themselves. In order to assess the importance of these individual factors, we studied the effect of strategies to combine different search algorithms, explored the influence of chosen database search settings, and investigated the impact of the size of the protein sequence database used for identification. Furthermore, we applied de novo sequencing as a complementary approach to classic database searching. All evaluations were performed on a human intestinal metaproteome dataset. Pyrococcus furiosus proteome data were used to contrast database searching of metaproteomic data to a classic proteomic experiment. Searching against subsets of metaproteome databases and the use of multiple search engines increased the number of identifications. The integration of P. furiosus sequences in a metaproteomic sequence database showcased the limitation of the target-decoy-controlled false discovery rate approach in combination with large sequence databases. The selection of varying search engine parameters and the application of de novo sequencing represented useful methods to increase the reliability of the results. Based on our findings, we provide recommendations for the data analysis that help researchers to establish or improve analysis workflows in metaproteomics.
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Affiliation(s)
- Thilo Muth
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Carolin A Kolmeder
- Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland
| | - Jarkko Salojärvi
- Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland
| | - Salla Keskitalo
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Markku Varjosalo
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Froukje J Verdam
- Department of General Surgery, NUTRIM, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sander S Rensen
- Department of General Surgery, NUTRIM, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Udo Reichl
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.,Otto-von-Guericke University, Bioprocess Engineering, Magdeburg, Germany
| | - Willem M de Vos
- Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland.,Department of Bacteriology and Immunology, University of Helsinki, Helsinki, Finland.,Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands
| | - Erdmann Rapp
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Lennart Martens
- Department of Biochemistry, Ghent University, Ghent, Belgium.,Department of Medical Protein Research, VIB, Ghent, Belgium
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46
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Gonnelli G, Stock M, Verwaeren J, Maddelein D, De Baets B, Martens L, Degroeve S. A Decoy-Free Approach to the Identification of Peptides. J Proteome Res 2015; 14:1792-8. [DOI: 10.1021/pr501164r] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Giulia Gonnelli
- Department
of Medical Protein Research, VIB, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Michiel Stock
- Department
of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium
| | - Jan Verwaeren
- Department
of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium
| | - Davy Maddelein
- Department
of Medical Protein Research, VIB, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Bernard De Baets
- Department
of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium
| | - Lennart Martens
- Department
of Medical Protein Research, VIB, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
| | - Sven Degroeve
- Department
of Medical Protein Research, VIB, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
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47
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Lee DCH, Jones AR, Hubbard SJ. Computational phosphoproteomics: from identification to localization. Proteomics 2015; 15:950-63. [PMID: 25475148 PMCID: PMC4384807 DOI: 10.1002/pmic.201400372] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 10/31/2014] [Accepted: 11/26/2014] [Indexed: 01/08/2023]
Abstract
Analysis of the phosphoproteome by MS has become a key technology for the characterization of dynamic regulatory processes in the cell, since kinase and phosphatase action underlie many major biological functions. However, the addition of a phosphate group to a suitable side chain often confounds informatic analysis by generating product ion spectra that are more difficult to interpret (and consequently identify) relative to unmodified peptides. Collectively, these challenges have motivated bioinformaticians to create novel software tools and pipelines to assist in the identification of phosphopeptides in proteomic mixtures, and help pinpoint or "localize" the most likely site of modification in cases where there is ambiguity. Here we review the challenges to be met and the informatics solutions available to address them for phosphoproteomic analysis, as well as highlighting the difficulties associated with using them and the implications for data standards.
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Affiliation(s)
- Dave C H Lee
- Faculty of Life Sciences, University of ManchesterManchester, UK
| | - Andrew R Jones
- Institute of Integrative Biology, University of LiverpoolLiverpool, UK
| | - Simon J Hubbard
- Faculty of Life Sciences, University of ManchesterManchester, UK
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48
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Nesvizhskii AI. Proteogenomics: concepts, applications and computational strategies. Nat Methods 2015; 11:1114-25. [PMID: 25357241 DOI: 10.1038/nmeth.3144] [Citation(s) in RCA: 505] [Impact Index Per Article: 56.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 09/22/2014] [Indexed: 12/19/2022]
Abstract
Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry-based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.
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Affiliation(s)
- Alexey I Nesvizhskii
- 1] Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA. [2] Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
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Crappé J, Ndah E, Koch A, Steyaert S, Gawron D, De Keulenaer S, De Meester E, De Meyer T, Van Criekinge W, Van Damme P, Menschaert G. PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration. Nucleic Acids Res 2014; 43:e29. [PMID: 25510491 PMCID: PMC4357689 DOI: 10.1093/nar/gku1283] [Citation(s) in RCA: 109] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. However, several factors, including extensive regulation of mRNA translation and the need for three- or six-frame-translation, impede the use of mRNA-seq data for the construction of a protein sequence search database. With that in mind, we developed the PROTEOFORMER tool that automatically processes data of the recently developed ribosome profiling method (sequencing of ribosome-protected mRNA fragments), resulting in genome-wide visualization of ribosome occupancy. Our tool also includes a translation initiation site calling algorithm allowing the delineation of the open reading frames (ORFs) of all translation products. A complete protein synthesis-based sequence database can thus be compiled for mass spectrometry-based identification. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse, respectively, and enables proteome-wide detection of 5′-extended proteoforms, upstream ORF translation and near-cognate translation start sites. The PROTEOFORMER tool is available as a stand-alone pipeline and has been implemented in the galaxy framework for ease of use.
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Affiliation(s)
- Jeroen Crappé
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Elvis Ndah
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium Department of Medical Protein Research, Flemish Institute of Biotechnology, Ghent, Belgium Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Alexander Koch
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Sandra Steyaert
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Daria Gawron
- Department of Medical Protein Research, Flemish Institute of Biotechnology, Ghent, Belgium Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Sarah De Keulenaer
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Ellen De Meester
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Tim De Meyer
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Wim Van Criekinge
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Petra Van Damme
- Department of Medical Protein Research, Flemish Institute of Biotechnology, Ghent, Belgium Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Gerben Menschaert
- Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
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Kucharova V, Wiker HG. Proteogenomics in microbiology: taking the right turn at the junction of genomics and proteomics. Proteomics 2014; 14:2360-675. [PMID: 25263021 DOI: 10.1002/pmic.201400168] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Revised: 08/18/2014] [Accepted: 09/23/2014] [Indexed: 12/14/2022]
Abstract
High-accuracy and high-throughput proteomic methods have completely changed the way we can identify and characterize proteins. MS-based proteomics can now provide a unique supplement to genomic data and add a new level of information to the interpretation of genomic sequences. Proteomics-driven genome annotation has become especially relevant in microbiology where genomes are sequenced on a daily basis and limitations of an in silico driven annotation process are well recognized. In this review paper, we outline different strategies on how one can design a proteogenomic experiment, for example on genome-sequenced (synonymous proteogenomics) versus unsequenced organisms (ortho-proteogenomics) or with the aid of other "omic" data such as RNA-seq. We touch upon many challenges that are encountered during a typical proteogenomic study, mostly concerning bioinformatics methods and downstream data analysis, but also related to creation and use of sequence databases. A large list of proteogenomic case studies of different microorganisms is provided to illustrate the mapping of MS/MS-derived peptide spectra to genomic DNA sequences. These investigations have led to accurate determination of translational initiation sites, pointed out eventual read-throughs or programmed frameshifts, detected signal peptide processing or other protein maturation events, removed questionable annotation assignments, and provided evidence for predicted hypothetical proteins.
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Affiliation(s)
- Veronika Kucharova
- Department of Clinical Science, The Gade Research Group for Infection and Immunity, University of Bergen, Norway
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