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Athanasopoulou K, Daneva GN, Boti MA, Dimitroulis G, Adamopoulos PG, Scorilas A. The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing. LIFE (BASEL, SWITZERLAND) 2022; 12:life12122010. [PMID: 36556377 PMCID: PMC9785810 DOI: 10.3390/life12122010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/25/2022] [Accepted: 11/30/2022] [Indexed: 12/05/2022]
Abstract
Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.
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2
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Lam KHB, Faust K, Yin R, Fiala C, Diamandis P. The Brain Protein Atlas: A conglomerate of proteomics datasets of human neural tissue. Proteomics 2022; 22:e2200127. [PMID: 35971647 DOI: 10.1002/pmic.202200127] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/09/2022] [Accepted: 08/03/2022] [Indexed: 11/06/2022]
Abstract
The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global -OMICS analysis. To address some of these barriers, our group has recently focused on using mass-spectrometry workflows compatible with small amounts of formalin-fixed paraffin-embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches. Given the similar theme and approaches, we introduce an integrated online portal, "The Brain Protein Atlas (BPA)" (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer-reviewed mass-spectrometry-based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry-based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built-in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies.
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Affiliation(s)
- K H Brian Lam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, United States of America
| | - Kevin Faust
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Richard Yin
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Clare Fiala
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.,Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
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3
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Brady MM, Meyer AS. Cataloguing the proteome: Current developments in single-molecule protein sequencing. BIOPHYSICS REVIEWS 2022; 3:011304. [PMID: 38505228 PMCID: PMC10903494 DOI: 10.1063/5.0065509] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/13/2022] [Indexed: 03/21/2024]
Abstract
The cellular proteome is complex and dynamic, with proteins playing a critical role in cell-level biological processes that contribute to homeostasis, stimuli response, and disease pathology, among others. As such, protein analysis and characterization are of extreme importance in both research and clinical settings. In the last few decades, most proteomics analysis has relied on mass spectrometry, affinity reagents, or some combination thereof. However, these techniques are limited by their requirements for large sample amounts, low resolution, and insufficient dynamic range, making them largely insufficient for the characterization of proteins in low-abundance or single-cell proteomic analysis. Despite unique technical challenges, several single-molecule protein sequencing (SMPS) technologies have been proposed in recent years to address these issues. In this review, we outline several approaches to SMPS technologies and discuss their advantages, limitations, and potential contributions toward an accurate, sensitive, and high-throughput platform.
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Affiliation(s)
- Morgan M. Brady
- Department of Biology, University of Rochester, Rochester, New York 14627, USA
| | - Anne S. Meyer
- Department of Biology, University of Rochester, Rochester, New York 14627, USA
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4
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Zhang Y, Wright MA, Saar KL, Challa P, Morgunov AS, Peter QAE, Devenish S, Dobson CM, Knowles TPJ. Machine learning-aided protein identification from multidimensional signatures. LAB ON A CHIP 2021; 21:2922-2931. [PMID: 34109955 PMCID: PMC8314522 DOI: 10.1039/d0lc01148g] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 03/16/2021] [Indexed: 06/12/2023]
Abstract
The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of a novel method for protein identification in a microfluidic format.
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Affiliation(s)
- Yuewen Zhang
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Maya A Wright
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Kadi L Saar
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. and Cavendish Laboratory, Department of Physics, University of Cambridge, J J Thomson Ave, Cambridge CB3 0HE, UK
| | - Pavankumar Challa
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Alexey S Morgunov
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. and Fluidic Analytics Ltd., Cambridge, UK
| | - Quentin A E Peter
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | | | - Christopher M Dobson
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | - Tuomas P J Knowles
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. and Cavendish Laboratory, Department of Physics, University of Cambridge, J J Thomson Ave, Cambridge CB3 0HE, UK
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5
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Barzine MP, Freivalds K, Wright JC, Opmanis M, Rituma D, Ghavidel FZ, Jarnuczak AF, Celms E, Čerāns K, Jonassen I, Lace L, Antonio Vizcaíno J, Choudhary JS, Brazma A, Viksna J. Using Deep Learning to Extrapolate Protein Expression Measurements. Proteomics 2020; 20:e2000009. [PMID: 32937025 PMCID: PMC7757209 DOI: 10.1002/pmic.202000009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 08/27/2020] [Indexed: 01/23/2023]
Abstract
Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R 2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be "transferred" across experiments and species. For instance, the model derived from human tissues gave a R 2 = 0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.
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Affiliation(s)
- Mitra Parissa Barzine
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteEMBL‐EBIWellcome Trust Genome CampusHinxtonCB10 1SDUK
| | - Karlis Freivalds
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | | | - Mārtiņš Opmanis
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
| | - Darta Rituma
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | | | - Andrew F. Jarnuczak
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteEMBL‐EBIWellcome Trust Genome CampusHinxtonCB10 1SDUK
| | - Edgars Celms
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | - Kārlis Čerāns
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | - Inge Jonassen
- Computational Biology UnitInformatics DepartmentUniversity of BergenBergenNO5020Norway
| | - Lelde Lace
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
| | - Juan Antonio Vizcaíno
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteEMBL‐EBIWellcome Trust Genome CampusHinxtonCB10 1SDUK
| | | | - Alvis Brazma
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteEMBL‐EBIWellcome Trust Genome CampusHinxtonCB10 1SDUK
| | - Juris Viksna
- Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLV1459Latvia
- Faculty of ComputingUniversity of LatviaRigaLV1586Latvia
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6
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Wei X, Ma D, Jing L, Wang LY, Wang X, Zhang Z, Lenhart BJ, Yin Y, Wang Q, Liu C. Enabling nanopore technology for sensing individual amino acids by a derivatization strategy. J Mater Chem B 2020; 8:6792-6797. [PMID: 32495805 PMCID: PMC7429270 DOI: 10.1039/d0tb00895h] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Nanopore technology holds remarkable promise for sequencing proteins and peptides. To achieve this, it is necessary to establish a characteristic profile for each individual amino acid through the statistical description of its translocation process. However, the subtle molecular differences among all twenty amino acids along with their unpredictable conformational changes at the nanopore sensing region result in very low distinguishability. Here we report the electrical sensing of individual amino acids using an α-hemolysin nanopore based on a derivatization strategy. Using derivatized amino acids as detection surrogates not only prolongs their interactions with the sensing region, but also improves their conformational variation. Furthermore, we show that distinct characteristics including current blockades and dwell times can be observed among all three classes of amino acids after 2,3-naphthalenedicarboxaldehyde (NDA)- and 2-naphthylisothiocyanate (NITC)-derivatization, respectively. These observable characteristics were applied towards the identification and differentiation of 9 of the 20 natural amino acids using their NITC derivatives. The method demonstrated herein will pave the way for the identification of all amino acids and further protein and peptide sequencing.
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Affiliation(s)
- Xiaojun Wei
- Biomedical Engineering Program, University of South Carolina, Columbia, SC 20208, USA
- Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Dumei Ma
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China
| | - Lihong Jing
- Key Laboratory of Colloid, Interface and Chemical Thermodynamics, Institute of Chemistry, Chinese Academy of Sciences, Bei Yi Jie 2, Zhong Guan Cun, Beijing 100190, China
| | - Leon Y. Wang
- Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Xiaoqin Wang
- Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Zehui Zhang
- Biomedical Engineering Program, University of South Carolina, Columbia, SC 20208, USA
| | - Brian J. Lenhart
- Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Yingwu Yin
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China
| | - Qian Wang
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA
| | - Chang Liu
- Biomedical Engineering Program, University of South Carolina, Columbia, SC 20208, USA
- Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208, USA
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7
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Shao X, Lu X, Liao J, Chen H, Fan X. New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data. Protein Cell 2020; 11:866-880. [PMID: 32435978 PMCID: PMC7719148 DOI: 10.1007/s13238-020-00727-5] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 04/12/2020] [Indexed: 12/13/2022] Open
Abstract
For multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.
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Affiliation(s)
- Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xiaoyan Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Huajun Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.,The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, China
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. .,The Save Sight Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2000, Australia.
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8
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Timp W, Timp G. Beyond mass spectrometry, the next step in proteomics. SCIENCE ADVANCES 2020; 6:eaax8978. [PMID: 31950079 PMCID: PMC6954058 DOI: 10.1126/sciadv.aax8978] [Citation(s) in RCA: 167] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/19/2019] [Indexed: 05/08/2023]
Abstract
Proteins can be the root cause of a disease, and they can be used to cure it. The need to identify these critical actors was recognized early (1951) by Sanger; the first biopolymer sequenced was a peptide, insulin. With the advent of scalable, single-molecule DNA sequencing, genomics and transcriptomics have since propelled medicine through improved sensitivity and lower costs, but proteomics has lagged behind. Currently, proteomics relies mainly on mass spectrometry (MS), but instead of truly sequencing, it classifies a protein and typically requires about a billion copies of a protein to do it. Here, we offer a survey that illuminates a few alternatives with the brightest prospects for identifying whole proteins and displacing MS for sequencing them. These alternatives all boast sensitivity superior to MS and promise to be scalable and seem to be adaptable to bioinformatics tools for calling the sequence of amino acids that constitute a protein.
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Affiliation(s)
- Winston Timp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Gregory Timp
- Departments of Electrical Engineering and Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
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9
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Vizcaíno JA, Kubiniok P, Kovalchik KA, Ma Q, Duquette JD, Mongrain I, Deutsch EW, Peters B, Sette A, Sirois I, Caron E. The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases. Mol Cell Proteomics 2020; 19:31-49. [PMID: 31744855 PMCID: PMC6944237 DOI: 10.1074/mcp.r119.001743] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 11/18/2019] [Indexed: 12/11/2022] Open
Abstract
The science that investigates the ensembles of all peptides associated to human leukocyte antigen (HLA) molecules is termed "immunopeptidomics" and is typically driven by mass spectrometry (MS) technologies. Recent advances in MS technologies, neoantigen discovery and cancer immunotherapy have catalyzed the launch of the Human Immunopeptidome Project (HIPP) with the goal of providing a complete map of the human immunopeptidome and making the technology so robust that it will be available in every clinic. Here, we provide a long-term perspective of the field and we use this framework to explore how we think the completion of the HIPP will truly impact the society in the future. In this context, we introduce the concept of immunopeptidome-wide association studies (IWAS). We highlight the importance of large cohort studies for the future and how applying quantitative immunopeptidomics at population scale may provide a new look at individual predisposition to common immune diseases as well as responsiveness to vaccines and immunotherapies. Through this vision, we aim to provide a fresh view of the field to stimulate new discussions within the community, and present what we see as the key challenges for the future for unlocking the full potential of immunopeptidomics in this era of precision medicine.
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Affiliation(s)
- Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Peter Kubiniok
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | | | - Qing Ma
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada; School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | | | - Ian Mongrain
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, QC, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington, 98109
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, California, 92037
| | - Alessandro Sette
- La Jolla Institute for Allergy and Immunology, La Jolla, California, 92037
| | - Isabelle Sirois
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Etienne Caron
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada; Department of Pathology and Cellular Biology, Faculty of Medicine, Université de Montréal, QC H3T 1J4, Canada.
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