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2. GO enrichment analysis for differential proteomics using ProteoRE

Author

Yves Vandenbrouck, Florence Combes, Valentin Loux, INSERM U1292, Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility (MIGALE), Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-10-INBS-0008,ProFI,Infrastructure Française de Protéomique(2010), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), BioSanté (UMR BioSanté), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)

Subjects
Proteomics, Service (systems architecture), Web server, Computer science, [SDV]Life Sciences [q-bio], Gene annotation, Reuse, computer.software_genre, Set (abstract data type), 03 medical and health sciences, 0302 clinical medicine, Protocol (object-oriented programming), 030304 developmental biology, 0303 health sciences, Enrichment analysis, Functional annotation, Data science, Online research methods, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Galaxy, Workflow, Gene ontology, Computer application, User interface, computer, 030217 neurology & neurosurgery
Abstract

International audience; With the increased simplicity of producing proteomics data, the bottleneck has now shifted to the functional analysis of large lists of proteins to translate this primary level of information into meaningful biological knowledge. Tools implementing such approach are a powerful way to gain biological insights related to their samples, provided that biologists/clinicians have access to computational solutions even when they have little programming experience or bioinWith the increased simplicity of producing proteomics data, the bottleneck has now shifted to the functional analysis of large lists of proteins to translate this primary level of information into meaningful biological knowledge. Tools implementing such approach are a powerful way to gain biological insights related to their samples, provided that biologists/clinicians have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed ProteoRE (Proteomics Research Environment), a unified online research service that provides end-users with a set of tools to interpret their proteomics data in a collaborative and reproducible manner. ProteoRE is built upon the Galaxy framework, a workflow system allowing for data and analysis persistence, and providing user interfaces to facilitate the interaction with tools dedicated to the functional and the visual analysis of proteomics datasets. A set of tools relying on computational methods selected for their complementarity in terms of functional analysis was developed and made accessible via the ProteoRE web portal. In this chapter, a step-by-step protocol linking these tools is designed to perform a functional annotation and GO-based enrichment analyses applied to a set of differentially expressed proteins as a use case. Analytical practices, guidelines as well as tips related to this strategy are also provided. Tools, datasets, and results are freely available at http://www.proteore.org, allowing researchers to reuse themformatics support. To achieve this goal, we designed ProteoRE (Proteomics Research Environment), a unified online research service that provides end-users with a set of tools to interpret their proteomics data in a collaborative and reproducible manner. ProteoRE is built upon the Galaxy framework, a workflow system allowing for data and analysis persistence, and providing user interfaces to facilitate the interaction with tools dedicated to the functional and the visual analysis of proteomics datasets. A set of tools relying on computational methods selected for their complementarity in terms of functional analysis was developed and made accessible via the ProteoRE web portal. In this chapter, a step-by-step protocol linking these tools is designed to perform a functional annotation and GO-based enrichment analyses applied to a set of differentially expressed proteins as a use case. Analytical practices, guidelines as well as tips related to this strategy are also provided. Tools, datasets, and results are freely available at http://www.proteore.org, allowing researchers to reuse them.

Published
2021

3. GO Enrichment Analysis for Differential Proteomics Using ProteoRE

Author

Florence, Combes, Valentin, Loux, and Yves, Vandenbrouck

Subjects
Proteomics, Internet, Proteins, Software, Workflow
Abstract

With the increased simplicity of producing proteomics data, the bottleneck has now shifted to the functional analysis of large lists of proteins to translate this primary level of information into meaningful biological knowledge. Tools implementing such approach are a powerful way to gain biological insights related to their samples, provided that biologists/clinicians have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed ProteoRE (Proteomics Research Environment), a unified online research service that provides end-users with a set of tools to interpret their proteomics data in a collaborative and reproducible manner. ProteoRE is built upon the Galaxy framework, a workflow system allowing for data and analysis persistence, and providing user interfaces to facilitate the interaction with tools dedicated to the functional and the visual analysis of proteomics datasets. A set of tools relying on computational methods selected for their complementarity in terms of functional analysis was developed and made accessible via the ProteoRE web portal. In this chapter, a step-by-step protocol linking these tools is designed to perform a functional annotation and GO-based enrichment analyses applied to a set of differentially expressed proteins as a use case. Analytical practices, guidelines as well as tips related to this strategy are also provided. Tools, datasets, and results are freely available at http://www.proteore.org , allowing researchers to reuse them.

Published
2021

4. Comprehensive and comparative exploration of the Atp7b

Author

Maud, Lacombe, Michel, Jaquinod, Lucid, Belmudes, Yohann, Couté, Claire, Ramus, Florence, Combes, Thomas, Burger, Elisabeth, Mintz, Justine, Barthelon, Vincent, Leroy, Aurélia, Poujois, Alain, Lachaux, France, Woimant, and Virginie, Brun

Subjects
Adult, Male, Proteome, Ceruloplasmin, Blood Proteins, Middle Aged, Mice, Mutant Strains, Mice, Inbred C57BL, Disease Models, Animal, Mice, Hepatolenticular Degeneration, Liver, Copper-Transporting ATPases, Animals, Humans, Female, Copper
Abstract

Wilson's disease (WD), a rare genetic disease caused by mutations in the ATP7B gene, is associated with altered expression and/or function of the copper-transporting ATP7B protein, leading to massive toxic accumulation of copper in the liver and brain. The Atp7b

Published
2019

5. Comprehensive and comparative exploration of the Atp7b(-/-) mouse plasma proteome

Author

Lucid Belmudes, Alain Lachaux, Michel Jaquinod, Maud Lacombe, Yohann Couté, Virginie Brun, Florence Combes, Vincent Leroy, Elisabeth Mintz, Justine Barthelon, Claire Ramus, Aurélia Poujois, Thomas Burger, Etude de la dynamique des protéomes (EDyP ), Laboratoire de Biologie à Grande Échelle (BGE - UMR S1038), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA), Biologie des Métaux (BioMet), Laboratoire de Chimie et Biologie des Métaux (LCBM - UMR 5249), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Clinique Universitaire d'Hépato-Gastroentérologie, CHU Grenoble, National reference Centre for Wilson's Disease [Paris] (CRMR Wilson), Service de neurologie [Univ. Paris VII], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Diderot - Paris 7 (UPD7)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Diderot - Paris 7 (UPD7), National Reference Centre for Wilson's Disease, Hôpital Femme Mère Enfant, ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), Biologie des Métaux (BioMet ), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Institut de recherche en astrophysique et planétologie (IRAP), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS), Institut de biologie et chimie des protéines [Lyon] (IBCP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Matière et Systèmes Complexes (MSC (UMR_7057)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hospices Civils de Lyon (HCL), Service de radiologie et imagerie médicale [Rennes], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-CHU Pontchaillou [Rennes], CarMeN, laboratoire, MIAI @ Grenoble Alpes - - MIAI2019 - ANR-19-P3IA-0003 - P3IA - VALID, Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon

Subjects
0301 basic medicine, [SDV]Life Sciences [q-bio], Biophysics, Proteomics, Biochemistry, Biomaterials, 03 medical and health sciences, 0302 clinical medicine, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Gene, biology, Metals and Alloys, Phenotype, Blood proteins, 3. Good health, Cell biology, [SDV] Life Sciences [q-bio], 030104 developmental biology, Chemistry (miscellaneous), 030220 oncology & carcinogenesis, Proteome, biology.protein, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Liver function, Ceruloplasmin, Function (biology)
Abstract

Wilson's disease (WD), a rare genetic disease caused by mutations in the ATP7B gene, is associated with altered expression and/or function of the copper-transporting ATP7B protein, leading to massive toxic accumulation of copper in the liver and brain. The Atp7b−/− mouse, a genetic and phenotypic model of WD, was developed to provide new insights into the pathogenic mechanisms of WD. Many plasma proteins are secreted by the liver, and impairment of liver function can trigger changes to the plasma proteome. High standard proteomics workflows can identify such changes. Here, we explored the plasma proteome of the Atp7b−/− mouse using a mass spectrometry (MS)-based proteomics workflow combining unbiased discovery analysis followed by targeted quantification. Among the 367 unique plasma proteins identified, 7 proteins were confirmed as differentially abundant between Atp7b−/− mice and wild-type littermates, and were directly linked to WD pathophysiology (regeneration of liver parenchyma, plasma iron depletion, etc.). We then adapted our targeted proteomics assay to quantify human orthologues of these proteins in plasma from copper-chelator-treated WD patients. The plasma proteome changes observed in the Atp7b−/− mouse were not confirmed in these samples, except for alpha-1 antichymotrypsin, levels of which were decreased in WD patients compared to healthy individuals. Plasma ceruloplasmin was investigated in both the Atp7b−/− mouse model and human patients; it was significantly decreased in the human form of WD only. In conclusion, MS-based proteomics is a method of choice to identify proteome changes in murine models of disrupted metal homeostasis, and allows their validation in human cohorts.

Published
2019

6. Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.

Author

David M Mutch, M Ramzi Temanni, Corneliu Henegar, Florence Combes, Véronique Pelloux, Claus Holst, Thorkild I A Sørensen, Arne Astrup, J Alfredo Martinez, Wim H M Saris, Nathalie Viguerie, Dominique Langin, Jean-Daniel Zucker, and Karine Clément

Subjects
Medicine, Science
Abstract

BackgroundThe ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot.Methodology/principal findingsThe present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (ConclusionAdipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

Published
2007
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7. Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR

Author

Samuel, Wieczorek, Florence, Combes, Hélène, Borges, and Thomas, Burger

Subjects
Proteomics, User-Computer Interface, Gene Ontology, Proteome, Data Interpretation, Statistical, Computational Biology, Software
Abstract

ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.

Published
2019

8. Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR

Author

Florence Combes, Samuel Wieczorek, Hélène Borges, Thomas Burger, Etude de la dynamique des protéomes (EDyP ), Laboratoire de Biologie à Grande Échelle (BGE - UMR S1038), Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Protocol (science), 0303 health sciences, Statistical software, business.industry, Computer science, [SDV]Life Sciences [q-bio], Sample (material), 030302 biochemistry & molecular biology, Quantitative proteomics, Differential analysis, Pattern recognition, Relative quantification, Missing data, Mass spectrometry, Proteomics, Data processing, 03 medical and health sciences, Label-free proteomics, Artificial intelligence, business, 030304 developmental biology
Abstract

International audience; ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provide a detailed protocol on how to perform these eight processing steps with ProStaR.

Published
2019

9. Bioinformatics tools and workflow to select blood biomarkers for early cancer diagnosis: an application to pancreatic cancer

Author

Valentin Loux, Yves Vandenbrouck, David Christiany, Florence Combes, Virginie Brun, Laboratoire de Biologie à Grande Échelle (BGE - UMR S1038), Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Institut National de la Santé et de la Recherche Médicale (INSERM), Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Université Paris Saclay (COMUE), Investissement d‟Avenir Infrastructures Nationales en Biologie et Santé' program (ProFI project, ANR-10-INBS-08 and French Bioinformatics Infrastructure grant ANR-11-INBS-0013)., Etude de la dynamique des protéomes (EDyP ), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG)

Subjects
Early cancer, Proteome, web server, Computer science, diagnosis, [SDV]Life Sciences [q-bio], pancreatic cancer, Bioinformatics, Proteomics, Biochemistry, Workflow, proteomics biomarker discovery, Business process discovery, 03 medical and health sciences, proteomics, Pancreatic cancer, medicine, Biomarkers, Tumor, Humans, Molecular Biology, computer application, Early Detection of Cancer, 030304 developmental biology, 0303 health sciences, 030302 biochemistry & molecular biology, Cancer, Computational Biology, bioinformatics, Omics, medicine.disease, 3. Good health, Pancreatic Neoplasms, secretome, Cancer biomarkers, galaxy, Software
Abstract

Secretome proteomics for the discovery of cancer biomarkers holds great potential to improve early cancer diagnosis. In this context, a knowledge‐based approach relying on mechanistic criteria related to the type of cancer should help to identify candidates from available “omics” information. Numerous bioinformatics tools, databases and “omics” datasets are available, but are often widely disseminated. In addition biomedical researchers with little programming experience or no in‐house bioinformatics support can find these tools difficult to access and use. With the aim of accelerating the discovery process for novel biomarkers, we have developed a set of tools we made available via a Galaxy‐based instance thereby providing a centralized access to a unified framework to assist end‐users biologists. These tools we implemented proceed by a step‐by‐step strategy to mine transcriptomics and proteomics databases for information relating to tissue‐specificity, allow the selection of proteins that are part of the secretome, and combine this information with proteomics datasets to rank the most promising candidate biomarkers for early cancer diagnosis. Using pancreatic cancer as a case study, this strategy produced a list of 24 candidate biomarkers suitable for experimental assessment by MS‐based proteomics. Among these proteins, three (SYCN, REG1B and PRSS2) were previously reported as circulating candidate biomarkers of pancreatic cancer. Further refinement of this list allowed us to prioritize 14 candidate biomarkers along with their associated proteotypic peptides for further investigation, using targeted MS‐based proteomics. The bioinformatics tools and the workflow implementing this strategy for the selection of candidate biomarkers are freely accessible at http://www.proteore.org for researchers who wish to reuse them in their own quests for biomarker discovery.

Published
2019

10. Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments

Author

Yohann Couté, Christophe Bruley, Thomas Burger, Florence Combes, Quentin Giai Gianetto, and Claire Ramus

Subjects
0301 basic medicine, False discovery rate, Computer science, Property (programming), Calibration (statistics), Quantitative proteomics, computer.software_genre, Proteomics, 01 natural sciences, Biochemistry, Pipeline (software), Computer graphics, Identifier, 010104 statistics & probability, 03 medical and health sciences, 030104 developmental biology, Data mining, 0101 mathematics, Molecular Biology, computer
Abstract

In MS-based quantitative proteomics, the FDR control (i.e. the limitation of the number of proteins that are wrongly claimed as differentially abundant between several conditions) is a major postanalysis step. It is classically achieved thanks to a specific statistical procedure that computes the adjusted p-values of the putative differentially abundant proteins. Unfortunately, such adjustment is conservative only if the p-values are well-calibrated; the false discovery control being spuriously underestimated otherwise. However, well-calibration is a property that can be violated in some practical cases. To overcome this limitation, we propose a graphical method to straightforwardly and visually assess the p-value well-calibration, as well as the R codes to embed it in any pipeline. All MS data have been deposited in the ProteomeXchange with identifier PXD002370 (http://proteomecentral.proteomexchange.org/dataset/PXD002370).

Published
2016

12. Identification of a novel <scp>BET</scp> bromodomain inhibitor‐sensitive, gene regulatory circuit that controls Rituximab response and tumour growth in aggressive lymphoid cancers

Author

Benoit Bernay, Dominique Leroux, Elena Ferri, Florence Combes, Mary Callanan, Remy Gressin, Jérôme Garin, Patricia Betton, Juliana Bruder‐Costa, Sieme Hamaidia, Sophie Rousseaux, Charles E. McKenna, Carlo Petosa, Myriam Ferro, Claire Rome, Anouk Emadali, Samuel Duley, Christophe Bruley, Sylvie Kieffer-Jaquinod, Alexandra Debernardi, and Saadi Khochbin

Subjects
0303 health sciences, Gene knockdown, Cellular differentiation, Biology, medicine.disease, BCL10, 3. Good health, Bromodomain, Lymphoma, 03 medical and health sciences, 0302 clinical medicine, immune system diseases, hemic and lymphatic diseases, 030220 oncology & carcinogenesis, Immunology, medicine, Cancer research, Molecular Medicine, Gene silencing, Rituximab, Epigenetic therapy, 030304 developmental biology, medicine.drug
Abstract

Immuno-chemotherapy elicit high response rates in B-cell non-Hodgkin lymphoma but heterogeneity in response duration is observed, with some patients achieving cure and others showing refractory disease or relapse. Using a transcriptome-powered targeted proteomics screen, we discovered a gene regulatory circuit involving the nuclear factor CYCLON which characterizes aggressive disease and resistance to the anti-CD20 monoclonal antibody, Rituximab, in high-risk B-cell lymphoma. CYCLON knockdown was found to inhibit the aggressivity of MYC-overexpressing tumours in mice and to modulate gene expression programs of biological relevance to lymphoma. Furthermore, CYCLON knockdown increased the sensitivity of human lymphoma B cells to Rituximab in vitro and in vivo. Strikingly, this effect could be mimicked by in vitro treatment of lymphoma B cells with a small molecule inhibitor for BET bromodomain proteins (JQ1). In summary, this work has identified CYCLON as a new MYC cooperating factor that autonomously drives aggressive tumour growth and Rituximab resistance in lymphoma. This resistance mechanism is amenable to next-generation epigenetic therapy by BET bromodomain inhibition, thereby providing a new combination therapy rationale for high-risk lymphoma.

Published
2013

13. Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments

Author

Quentin, Giai Gianetto, Florence, Combes, Claire, Ramus, Christophe, Bruley, Yohann, Couté, and Thomas, Burger

Subjects
Proteomics, Calibration, Computer Graphics, Proteins, Mass Spectrometry
Abstract

In MS-based quantitative proteomics, the FDR control (i.e. the limitation of the number of proteins that are wrongly claimed as differentially abundant between several conditions) is a major postanalysis step. It is classically achieved thanks to a specific statistical procedure that computes the adjusted p-values of the putative differentially abundant proteins. Unfortunately, such adjustment is conservative only if the p-values are well-calibrated; the false discovery control being spuriously underestimated otherwise. However, well-calibration is a property that can be violated in some practical cases. To overcome this limitation, we propose a graphical method to straightforwardly and visually assess the p-value well-calibration, as well as the R codes to embed it in any pipeline. All MS data have been deposited in the ProteomeXchange with identifier PXD002370 (http://proteomecentral.proteomexchange.org/dataset/PXD002370).

Published
2015

14. Uranium perturbs signaling and iron uptake response in Arabidopsis thaliana roots

Author

Olivier Bastien, Véronique Hugouvieux, Nathalie Leonhardt, Iker Aranjuelo, Jean-Pierre Renou, Corinne Rivasseau, Fany Doustaly, Marie Carrière, Yves Vandenbrouck, Florence Combes, Jacques Bourguignon, Serge Berthet, Julie B. Fiévet, Alain Vavasseur, Laboratoire de physiologie cellulaire végétale (LPCV), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Recherche Agronomique (INRA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire de Biologie à Grande Échelle (BGE - UMR S1038), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Instituto de Agrobiotecnologıa, Universidad de Navarra [Pamplona] (UNAV), Biologie végétale et microbiologie environnementale - UMR7265 (BVME), Institut de Biosciences et Biotechnologies d'Aix-Marseille (ex-IBEB) (BIAM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Institut Nanosciences et Cryogénie (INAC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire Lésions des Acides Nucléiques (LAN), Service de Chimie Inorganique et Biologique (SCIB - UMR E3), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Institut Nanosciences et Cryogénie (INAC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS), Unité de recherche en génomique végétale (URGV), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Biologie, Informatique et Mathématiques, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Biosciences et de Biotechnologies de Grenoble (ex-IRTSV) (BIG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Recherche Agronomique (INRA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), CEA Toxicology Program, CMIRA grant from the Rhone-Alpes region, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Recherche Agronomique (INRA)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Recherche Agronomique (INRA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut Nanosciences et Cryogénie (INAC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Université Grenoble Alpes (UGA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Université Grenoble Alpes (UGA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université Grenoble Alpes (UGA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Institut Nanosciences et Cryogénie (INAC), and Université Grenoble Alpes (UGA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
signaling pathway, Arabidopsis thaliana, Iron, hydroponic culture, Arabidopsis, Biophysics, plant, medicine.disease_cause, Models, Biological, Plant Roots, Biochemistry, uranium speciation, Biomaterials, Transcriptome, Cell wall, stress, Gene Expression Regulation, Plant, uranyl, Botany, Gene expression, medicine, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, FIT1, phosphate, biology, Arabidopsis Proteins, FR02, iron uptake, Metals and Alloys, biology.organism_classification, Cell biology, Chemistry (miscellaneous), radionucleide, gene expression, Uranium, Signal transduction, DNA microarray, IRT1, Oxidative stress, Signal Transduction
Abstract

Uranium is a natural element which is mainly redistributed in the environment due to human activity, including accidents and spillages. Plants may be useful in cleaning up after incidents, although little is yet known about the relationship between metal speciation and plant response. Here, J-Chess modeling was used to predict U speciation and exposure conditions affecting U bioavailability for plants. The model was confirmed by exposing Arabidopsis thaliana plants to U under hydroponic conditions. The early root response was characterized using complete Arabidopsis transcriptome microarrays (CATMA). Expression of 111 genes was modified at the three timepoints studied. The associated biological processes were further examined by real-time quantitative RT-PCR. Annotation revealed that oxidative stress, cell wall and hormone biosynthesis, and signaling pathways (including phosphate signaling) were affected by U exposure. The main actors in iron uptake and signaling (IRT1, FRO2, AHA2, AHA7 and FIT1) were strongly down-regulated upon exposure to uranyl. A network calculated using IRT1, FRO2 and FIT1 as bait revealed a set of genes whose expression levels change under U stress. Hypotheses are presented to explain how U perturbs the iron uptake and signaling response. These results give preliminary insights into the pathways affected by uranyl uptake, which will be of interest for engineering plants to help clean areas contaminated with U. © 2014 The Royal Society of Chemistry.

Published
2014

15. Unbalanced expression of CK2 kinase subunits is sufficient to drive epithelial-to-mesenchymal transition by Snail1 induction

Author

Claude Cochet, Yohann Couté, Odile Filhol, Colette Charpin, Sophie Giusiano, E Duchemin-Pelletier, Ivan Mikaelian, Alexandre Deshiere, Yves Vandenbrouck, Delphine Ciais, Florence Combes, E Spreux, Etude de la dynamique des protéomes (EDyP ), Laboratoire de Biologie à Grande Échelle (BGE - UMR S1038), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Registre Multicentrique à Vocation Nationale des Mésothéliomes Pleuraux (MESONAT), CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN), Invasion mechanisms in angiogenesis and cancer (IMAC), Biologie du Cancer et de l'Infection (BCI ), Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire d'étude de la dynamique des protéomes (LEDyP), and Université Joseph Fourier - Grenoble 1 (UJF)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Cancer Research, Epithelial-Mesenchymal Transition, Protein subunit, [SDV]Life Sciences [q-bio], Breast Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, Bioinformatics, Models, Biological, Gene Expression Regulation, Enzymologic, 03 medical and health sciences, 0302 clinical medicine, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Genetics, Humans, Epithelial–mesenchymal transition, Casein Kinase II, Protein kinase A, Molecular Biology, Cells, Cultured, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Regulation of gene expression, 0303 health sciences, Kinase, Microarray analysis techniques, Gene Expression Profiling, Carcinoma, Microarray Analysis, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Up-Regulation, Cell biology, Gene Expression Regulation, Neoplastic, Isoenzymes, Gene expression profiling, Protein Subunits, Tissue Array Analysis, 030220 oncology & carcinogenesis, embryonic structures, Phosphorylation, Female, Snail Family Transcription Factors, Transcription Factors
Abstract

Epithelial-to-mesenchymal transition (EMT) is closely linked to conversion of early-stage tumours into invasive malignancies. Many signalling pathways are involved in EMT, but the key regulatory kinases in this important process have not been clearly identified. Protein kinase CK2 is a multi-subunit protein kinase, which, when overexpressed, has been linked to disease progression and poor prognosis in various cancers. Specifically, overexpression of CK2α in human breast cancers is correlated with metastatic risk. In this article, we show that an imbalance of CK2 subunits reflected by a decrease in the CK2β regulatory subunit in a subset of breast tumour samples is correlated with induction of EMT-related markers. CK2β-depleted epithelial cells displayed EMT-like morphological changes, enhanced migration, and anchorage-independent growth, all of which require Snail1 induction. In epithelial cells, Snail1 stability is negatively regulated by CK2 and GSK3β through synergistic hierarchal phosphorylation. This process depends strongly on CK2β, thus confirming that CK2 functions upstream of Snail1. In primary breast tumours, CK2β underexpression also correlates strongly with expression of EMT markers, emphasizing the link between asymmetric expression of CK2 subunits and EMT in vivo. Our results therefore highlight the importance of CK2β in controlling epithelial cell plasticity. They show that CK2 holoenzyme activity is essential to suppress EMT, and that it contributes to maintaining a normal epithelial morphology. This study also suggests that unbalanced expression of CK2 subunits may drive EMT, thereby contributing to tumour progression.

Published
2013

16. Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders

Author

Claus Holst, Wim H. M. Saris, Corneliu Henegar, Nathalie Viguerie, Arne Astrup, Dominique Langin, Jean-Daniel Zucker, J. Alfredo Martínez, Véronique Pelloux, David M. Mutch, Thorkild I. A. Sørensen, M. Ramzi Temanni, Florence Combes, Karine Clément, Unité de modélisation mathématique et informatique des systèmes complexes [Bondy] (UMMISCO), Institut de Recherche pour le Développement (IRD [France-Nord])-Institut de la francophonie pour l'informatique-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Université Gaston Bergé (Saint-Louis, Sénégal)-Université Cadi Ayyad [Marrakech] (UCA)-Université de Yaoundé I-Sorbonne Université (SU), Humane Biologie, RS: NUTRIM School of Nutrition and Translational Research in Metabolism, and RS: NUTRIM - R1 - Metabolic Syndrome

Subjects
Weight loss, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Science, Adipose tissue, Gene Expression, 030209 endocrinology & metabolism, Single gene, Biology, Bioinformatics, 03 medical and health sciences, 0302 clinical medicine, Intervention (counseling), Nutrition/Obesity, Gene expression, Weight Loss, medicine, Humans, 030304 developmental biology, Oligonucleotide Array Sequence Analysis, Nutrition, 0303 health sciences, Multidisciplinary, Reverse Transcriptase Polymerase Chain Reaction, Adipose tissue metabolism, Genetics and Genomics, Genetics and Genomics/Gene Expression, Genetics and Genomics/Bioinformatics, [SDV.MHEP.EM]Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, medicine.disease, Obesity, 3. Good health, Diet, Multicenter study, Adipose Tissue, Medicine, Female, Public Health and Epidemiology/Epidemiology, medicine.symptom, Energy Intake, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Algorithms, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Research Article, Computational Biology/Genomics
Abstract

BACKGROUND: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (

Published
2007

17. Goulphar: rapid access and expertise for standard two-color microarray normalization methods

Author

Nicolas Servant, Stéphane Le Crom, Florence Combes, Sophie Lemoine, Autard, Delphine, GenomiqueENS (Genomique ENS), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), This work was partly supported by the French RNG (Genopole National Network)., Plateforme Génomique de l'IBENS, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), IFR36, Génétique moléculaire du développement, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-IFR36-Institut National de la Santé et de la Recherche Médicale (INSERM), and sophie, lemoine

Subjects
Normalization (statistics), Computer science, MESH: Algorithms, Expert Systems, lcsh:Computer applications to medicine. Medical informatics, computer.software_genre, Sensitivity and Specificity, Biochemistry, MESH: Calibration, Database normalization, Bioconductor, MESH: Software, User-Computer Interface, 03 medical and health sciences, 0302 clinical medicine, Structural Biology, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], MESH: In Situ Hybridization, Fluorescence, lcsh:QH301-705.5, Molecular Biology, In Situ Hybridization, Fluorescence, MESH: User-Computer Interface, Oligonucleotide Array Sequence Analysis, 030304 developmental biology, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Expert Systems, Microarray analysis techniques, Applied Mathematics, Reproducibility of Results, Two Color Microarray, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Sensitivity and Specificity, Computer Science Applications, MESH: Reproducibility of Results, Microscopy, Fluorescence, Multiphoton, lcsh:Biology (General), MESH: Oligonucleotide Array Sequence Analysis, Calibration, MESH: Microscopy, Fluorescence, Multiphoton, lcsh:R858-859.7, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Data mining, DNA microarray, Raw data, computer, Algorithms, Software, 030217 neurology & neurosurgery
Abstract

Background Raw data normalization is a critical step in microarray data analysis because it directly affects data interpretation. Most of the normalization methods currently used are included in the R/BioConductor packages but it is often difficult to identify the most appropriate method. Furthermore, the use of R commands for functions and graphics can introduce mistakes that are difficult to trace. We present here a script written in R that provides a flexible means of access to and monitoring of data normalization for two-color microarrays. This script combines the power of BioConductor and R analysis functions and reduces the amount of R programming required. Results Goulphar was developed in and runs using the R language and environment. It combines and extends functions found in BioConductor packages (limma and marray) to correct for dye biases and spatial artifacts. Goulphar provides a wide range of optional and customizable filters for excluding incorrect signals during the pre-processing step. It displays informative output plots, enabling the user to monitor the normalization process, and helps adapt the normalization method appropriately to the data. All these analyses and graphical outputs are presented in a single PDF report. Conclusion Goulphar provides simple, rapid access to the power of the R/BioConductor statistical analysis packages, with precise control and visualization of the results obtained. Complete documentation, examples and online forms for setting script parameters are available from http://transcriptome.ens.fr/goulphar/.

Published
2006

18. Designing an In Silico Strategy to Select Tissue-Leakage Biomarkers Using the Galaxy Framework

Author

Valentin Loux, Virginie Brun, Lien Nguyen, Yves Vandenbrouck, Florence Combes, Laboratoire de Biologie à Grande Échelle (BGE - UMR S1038), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Etude de la dynamique des protéomes (EDyP), BioSanté (UMR BioSanté), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Unité Mathématique, Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Etude de la dynamique des protéomes (EDyP ), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Proteomics, 0301 basic medicine, Computer science, [SDV]Life Sciences [q-bio], In silico, Disease, Machine learning, computer.software_genre, Biomarker selection, 03 medical and health sciences, 0302 clinical medicine, Web server, Biomarker discovery, ComputingMilieux_MISCELLANEOUS, business.industry, Experimental data, Tissue-leakage biomarkers, Tissue injury, Omics, Pathophysiology, Galaxy, ComputingMethodologies_PATTERNRECOGNITION, 030104 developmental biology, 030220 oncology & carcinogenesis, Human plasma, Biomarker (medicine), Computer application, Artificial intelligence, business, computer
Abstract

International audience; Knowledge-based approaches using large-scale biological ("omics") data are a powerful way to identify mechanistic biomarkers, provided that scientists have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed a set of tools under the Galaxy framework to allow biologists to define their own strategy for reproducible biomarker selection. These tools rely on retrieving experimental data from public databases, and applying successive filters derived from information relating to disease pathophysiology. A step-by-step protocol linking these tools was implemented to select tissue-leakage biomarker candidates of myocardial infarction. A list of 24 candidates suitable for experimental assessment by MS-based proteomics is proposed. These tools have been made publicly available at http://www.proteore.org , allowing researchers to reuse them in their quest for biomarker discovery

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