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4. SugarBindDB

Author

Mariethoz, Julien, Khatib, Khaled, Mannic, Tiphaine, Alocci, Davide, Campbell, Matthew P., Packer, Nicolle H., Mullen, Elaine H., Lisacek, Frederique, and Aoki-Kinoshita, Kiyoko F., editor

Published
2017
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8. This is GlycoQL

Author

Hayes, Catherine, primary, Daponte, Vincenzo, additional, Mariethoz, Julien, additional, and Lisacek, Frederique, additional

Published
2022
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10. SugarBindDB

Author

Mariethoz, Julien, primary, Khatib, Khaled, additional, Mannic, Tiphaine, additional, Alocci, Davide, additional, Campbell, Matthew P., additional, Packer, Nicolle H., additional, Mullen, Elaine H., additional, and Lisacek, Frederique, additional

Published
2016
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11. Databases and Associated Tools for Glycomics and Glycoproteomics

Author

Lisacek, Frederique, primary, Mariethoz, Julien, additional, Alocci, Davide, additional, Rudd, Pauline M., additional, Abrahams, Jodie L., additional, Campbell, Matthew P., additional, Packer, Nicolle H., additional, Ståhle, Jonas, additional, Widmalm, Göran, additional, Mullen, Elaine, additional, Adamczyk, Barbara, additional, Rojas-Macias, Miguel A., additional, Jin, Chunsheng, additional, and Karlsson, Niclas G., additional

Published
2016
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12. What is a consistent glycan composition dataset?

Author

Saba, Federico, Mariethoz, Julien, and Lisacek, Frederique

Subjects
SOFTWARE development tools, GLYCANS, DATA modeling, STATISTICAL correlation, EVALUATION methodology, GENE ontology
Abstract

Introduction: One of the main challenges in bioinformatics has been and still is, the comparison of entities through the development of algorithms for similarity scoring and data clustering according to biologically relevant aspects. Glycoinformatics also faces this challenge, in particular regarding the automated comparison of protein and/or tissue glycomes, that remains a relatively uncharted territory. Methods: Low and high throughput experimental glycomic and glycoproteomic results were collected, revealing a bias toward N-linked glycomes. Then, Nglycomes were considered and represented as networks of related glycan compositions as opposed to lists of glycans. They were processed and compared through a java application generating graphs and another producing a similarity matrix based on graph content. Several scoring schemes (e.g., Jaccard index or cosine) were tested and evaluated using the Matthews Correlation Coefficient, in order to capture a meaningful protein and tissue N-glycome similarity. Results: Assuming that a glycome corresponds to a well-connected graph of glycan compositions, graph comparison has revealed gaps that can be interpreted as inconsistencies. The outcome of systematic graph comparison is both formal and practical. In principle, it is shown that the idiosyncrasy of current glycome data limits the definition of appropriate estimates for systematically comparing Nglycomes. Yet, several potentially interesting criteria could be identified in a series of use cases detailed in the study. Discussion: Differentially expressed glycomes are usually compared manually, but the resulting work tends to remain in publications due to the lack of dedicated tools. Even manually, cross-comparison is challenging mostly because different sets of features are used from one study to the other. The work presented here enables laying down guidelines for developing a software tool comparing glycomes based on appropriate definitions of similarity and suitable methods for its evaluation and implementation. [ABSTRACT FROM AUTHOR]

Published
2023
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13. Heterogeneous Data Processing for Information and Knowledge Management in Glycomics and Glycoproteomics

Author

Mariethoz, Julien and Lisacek, Frédérique

Subjects
Data, ddc:025.063, Glycomics, Reproducibility, Standardization, Representation
Abstract

Glycans or carbohydrates represent a major class of biomolecules. They are also the observable result of the most common Post-Translational Modification, known to play a crucial role in the host-pathogen interactions as well as in signaling processes or disease states, among others. Glycoinformatics is the dedicated bioinformatics field supporting glycobiology and its experimental parts, including glycomics and glycoproteomics and any other glycan-oriented studies. The generated data and resulting information drive the discipline. Glycans molecules have tree structures with a characterized topology. This structural complexity induces glycoinformatics problems relative to their representation, encoding formats, processing, and interpretation. Furthermore, the lack of consensus in this respect significantly limits reproducibility and comparability in glycobiology. We suggest that common descriptions, application guidelines, and best practices are solutions to overcome these issues. Over the recent years, Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and minimum information required for a glycomics experiment (MIRAGE) initiatives have driven and ruled data standardization processes. Subsequently, adopting these principles and guidelines improves reproducibility, interoperability, and data reuse, increasing its value.

Published
2022
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24. Evolutionary Glycomics: A Comprehensive Study of Vertebrate Host Serum/Plasma Glycome Using Orthogonal Glycomics Techniques

Author

Murugan, Abarna V. M., primary, Oliveira, Tiago, additional, Alagesan, Kathirvel, additional, Mojtahedinyazdi, Yasin, additional, Mariethoz, Julien, additional, Hayes, Catherine, additional, Lisacek, Frédérique, additional, Karlsson, Niclas, additional, Nothaft, Harold, additional, Szymanski, Christine, additional, Finlayson, Kimberly, additional, Merwe, Jason, additional, Richardson, Samantha, additional, and Kolarich, Daniel, additional

Published
2021
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30. A pipeline to translate glycosaminoglycan sequences into 3D models. Application to the exploration of glycosaminoglycan conformational space

Author

Karamanos, Nikos, Piperigkou, Zoi, Theocharis, Achilleas, Watanabe, Hideto, Franchi, Marco, BAUD, Stéphanie, Brezillon, Stephane, Götte, Martin, Passi, Alberto, Vigetti, Davide, Sanderson, Ralph, Neill, Thomas, Iozzo, Thomas, Clerc, Olivier, Mariethoz, Julien, Rivet, Alain, Lisacek, Frederique, Pérez, Serge, Ricard-Blum, Sylvie, Matrice extracellulaire et dynamique cellulaire - UMR 7369 (MEDyC), Université de Reims Champagne-Ardenne (URCA)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biochimie Médicale et Biologie Moléculaire, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Department of Obstetrics and Gynecology, University Hospital Münster - Universitaetsklinikum Muenster [Germany] (UKM), Lieux, Identités, eSpaces, Activités (LISA), Université Pascal Paoli (UPP)-Centre National de la Recherche Scientifique (CNRS), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL), Centre de Recherches sur l'Entreprise, les Organisations et le Patrimoine (CREOP), Gouvernance des Institutions et des Organisations (GIO), Université de Limoges (UNILIM)-Université de Limoges (UNILIM), Facultad de Matemática, Astronomía y Física [Cordoba] (FaMAF), Universidad Nacional de Córdoba [Argentina], Assemblages Supramoléculaires Péricellulaires et Extracellulaires (ASPE), Institut de Chimie et Biochimie Moléculaires et Supramoléculaires (ICBMS), 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 de Chimie du CNRS (INC)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut de Chimie du CNRS (INC)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)

Subjects
Models, Molecular, Glycan, Keratan sulfate, Computer science, Sequence (biology), Computational biology, Biochemistry, Dermatan sulfate, 03 medical and health sciences, chemistry.chemical_compound, ddc:570, Carbohydrate Conformation, Animals, Humans, MatrixDB, Chondroitin sulfate, ddc:025.063, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Glycosaminoglycans, 0303 health sciences, biology, 030302 biochemistry & molecular biology, Construct (python library), Heparan sulfate, 3. Good health, chemistry, biology.protein, Software
Abstract

Mammalian glycosaminoglycans are linear complex polysaccharides comprising heparan sulfate, heparin, dermatan sulfate, chondroitin sulfate, keratan sulfate and hyaluronic acid. They bind to numerous proteins and these interactions mediate their biological activities. GAG–protein interaction data reported in the literature are curated mostly in MatrixDB database (http://matrixdb. univ-lyon1.fr/). However, a standard nomenclature and a machine-readable format of GAGs together with bioinformatics tools for mining these interaction data are lacking. We report here the building of an automated pipeline to (i) standardize the format of GAG sequences interacting with proteins manually curated from the literature, (ii) translate them into the machine-readable GlycoCT format and into SNFG (Symbol Nomenclature For Glycan) images and (iii) convert their sequences into a format processed by a builder generating three-dimensional structures of polysaccharides based on a repertoire of conformations experimentally validated by data extracted from crystallized GAG–protein complexes. We have developed for this purpose a converter (the CT23D converter) to automatically translate the GlycoCT code of a GAG sequence into the input file required to construct a three-dimensional model.

Published
2018

31. Towards a standardized bioinformatics infrastructure for N- and O-glycomics

Author

Rojas-Macias, Miguel A., primary, Mariethoz, Julien, additional, Andersson, Peter, additional, Jin, Chunsheng, additional, Venkatakrishnan, Vignesh, additional, Aoki, Nobuyuki P., additional, Shinmachi, Daisuke, additional, Ashwood, Christopher, additional, Madunic, Katarina, additional, Zhang, Tao, additional, Miller, Rebecca L., additional, Horlacher, Oliver, additional, Struwe, Weston B., additional, Watanabe, Yu, additional, Okuda, Shujiro, additional, Levander, Fredrik, additional, Kolarich, Daniel, additional, Rudd, Pauline M., additional, Wuhrer, Manfred, additional, Kettner, Carsten, additional, Packer, Nicolle H., additional, Aoki-Kinoshita, Kiyoko F., additional, Lisacek, Frédérique, additional, and Karlsson, Niclas G., additional

Published
2019
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34. Glycomics@ExPASy: Bridging the Gap

Author

Mariethoz, Julien, primary, Alocci, Davide, additional, Gastaldello, Alessandra, additional, Horlacher, Oliver, additional, Gasteiger, Elisabeth, additional, Rojas-Macias, Miguel, additional, Karlsson, Niclas G., additional, Packer, Nicolle H., additional, and Lisacek, Frédérique, additional

Published
2018
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35. Glycoforest 1.0

Author

Horlacher, Oliver, Jin, Chunsheng, Alocci, Davide, Mariethoz, Julien, Müller, Markus, Karlsson, Niclas G., and Lisacek, Frédérique

Abstract

Tandem mass spectrometry, when combined with liquid chromatography and applied to complex mixtures produces large amounts of raw data, which needs to be analysed to identify molecular structures. This technique is widely used, particularly in glycomics. Due to a lack of high throughput glycan sequencing software, glycan spectra are predominantly sequenced manually. A challenge for writing glycan-sequencing software is that there is no direct template that can be used to infer structures detectable in an organism. To help alleviate this bottleneck we present Glycoforest 1.0, a partial de-novo algo-rithm for sequencing glycan structures based on MS/MS spectra. Glycoforest was tested on two datasets (human gastric and salmon mucosa O-linked glycomes) for which MS/MS spectra were annotated manually. Glycoforest generated the human validated structure for 92% of test cases. The correct structure was found as the best scoring match for 70% and among the top 3 matches for 83% of test cases. In addition, the Glycoforest algorithm detected glycan structures from MS/MS spectra missing a manual annotation. In total 1,532 MS/MS previously unannotated spectra were annotated by Glycoforest. A por-tion containing 521 spectra was manually checked confirming that Glycoforest annotated an additional 50 MS/MS spectra overlooked during manual annotation

Published
2024
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38. e-workflow for recording of glycomic mass spectrometric data in compliance with reporting guidelines

Author

Rojas-Macias, Miguel A., primary, Mariethoz, Julien, additional, Andersson, Peter, additional, Jin, Chunsheng, additional, Venkatakrishnan, Vignesh, additional, Aoki, Nobuyuki P., additional, Shinmachi, Daisuke, additional, Ashwood, Christopher, additional, Madunic, Katarina, additional, Zhang, Tao, additional, Miller, Rebecca L., additional, Horlacher, Oliver, additional, Struwe, Weston B., additional, Levander, Fredrik, additional, Kolarich, Daniel, additional, Rudd, Pauline M., additional, Wuhrer, Manfred, additional, Kettner, Carsten, additional, Packer, Nicolle H., additional, Aoki-Kinoshita, Kiyoko F., additional, Lisacek, Frédérique, additional, and Karlsson, Niclas G., additional

Published
2018
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40. Glycoforest 1.0

Author

Horlacher, Oliver, primary, Jin, Chunsheng, additional, Alocci, Davide, additional, Mariethoz, Julien, additional, Müller, Markus, additional, Karlsson, Niclas G., additional, and Lisacek, Frederique, additional

Published
2017
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43. A pipeline to translate glycosaminoglycan sequences into 3D models. Application to the exploration of glycosaminoglycan conformational space.

Author

Clerc, Olivier, Mariethoz, Julien, Rivet, Alain, Lisacek, Frédérique, Pérez, Serge, and Ricard-Blum, Sylvie

Subjects
*GLYCOSAMINOGLYCANS, *MOLECULAR conformation, *HEPARAN sulfate, *CHONDROITIN, *POLYSACCHARIDES, *THREE-dimensional modeling
Abstract

Mammalian glycosaminoglycans are linear complex polysaccharides comprising heparan sulfate, heparin, dermatan sulfate, chondroitin sulfate, keratan sulfate and hyaluronic acid. They bind to numerous proteins and these interactions mediate their biological activities. GAG–protein interaction data reported in the literature are curated mostly in MatrixDB database (http://matrixdb.univ-lyon1.fr/). However, a standard nomenclature and a machine-readable format of GAGs together with bioinformatics tools for mining these interaction data are lacking. We report here the building of an automated pipeline to (i) standardize the format of GAG sequences interacting with proteins manually curated from the literature, (ii) translate them into the machine-readable GlycoCT format and into SNFG (Symbol Nomenclature For Glycan) images and (iii) convert their sequences into a format processed by a builder generating three-dimensional structures of polysaccharides based on a repertoire of conformations experimentally validated by data extracted from crystallized GAG–protein complexes. We have developed for this purpose a converter (the CT23D converter) to automatically translate the GlycoCT code of a GAG sequence into the input file required to construct a three-dimensional model. [ABSTRACT FROM AUTHOR]

Published
2019
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47. The importance of layout and configuration data for flexibility during commissionning and operation of the LHC machine protection systems

Author

Mariethoz, Julien, Peryt, Maciej, Le Roux, Pascal, Bernard, Frederic, Harrison, Robert, and Zerlauth, Markus

Subjects
Accelerators and Storage Rings
Abstract

Due to the large stored energies in both magnets and particle beams, the Large Hadron Collider (LHC) requires a large inventory of machine protection systems, as e.g. powering interlock systems, based on a series of distributed industrial controllers for the protection of the more than 10'000 normal and superconducting magnets. Such systems are required to be at the same time fast, reliable and secure but also flexible and configurable to allow for automated commissioning, remote monitoring and optimization during later operation. Based on the generic hardware architecture of the LHC machine protection systems presented at EPAC 2002 [2] and ICALEPS 2003, the use of configuration data for protection systems in view of the required reliability and safety is discussed. To achieve the very high level of reliability, it is required to use a coherent description of the layout of the accelerator components and of the associated machine protection architecture and their logical interconnections. Mechanisms to guarantee coherency of data and repositories and secure configuration of safety critical systems are presented. This paper focuses on the first system being commissioned, the complex magnet powering system, to become fully operational before first injection of beam into the LHC.

Published
2006

48. Toolboxes for a standardised and systematic study of glycans

Author

Campbell, Matthew P, primary, Ranzinger, René, additional, Lütteke, Thomas, additional, Mariethoz, Julien, additional, Hayes, Catherine A, additional, Zhang, Jingyu, additional, Akune, Yukie, additional, Aoki-Kinoshita, Kiyoko F, additional, Damerell, David, additional, Carta, Giorgio, additional, York, Will S, additional, Haslam, Stuart M, additional, Narimatsu, Hisashi, additional, Rudd, Pauline M, additional, Karlsson, Niclas G, additional, Packer, Nicolle H, additional, and Lisacek, Frédérique, additional

Published
2014
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50. Corrigendum to: The glycoconjugate ontology (GlycoCoO) for standardizing the annotation of glycoconjugate data and its application.

Author

Yamada, Issaku, Campbell, Matthew P, Edwards, Nathan, Castro, Leyla Jael, Lisacek, Frederique, Mariethoz, Julien, Ono, Tamiko, Ranzinger, Rene, Shinmachi, Daisuke, and Aoki-Kinoshita, Kiyoko F

Subjects
ACADEMIC medical centers, LIFE sciences, ONTOLOGY
Abstract

Issaku Yamada SP 1,2 sp , Matthew P. Campbell SP 3 sp , Nathan Edwards SP 3 sp , Leyla Jael Castro SP 4 sp , Frederique Lisacek SP 5 sp , Julien Mariethoz SP 6 sp , Tamiko Ono SP 7 sp , Rene Ranzinger SP 8 sp , Daisuke Shinmachi SP 9 sp and Kiyoko F. Aoki-Kinoshita SP 10 sp SP 2 sp Research Depertment, The Noguchi Institute, 1-9-7 Kaga, Itabashi, Tokyo 173-0003, Japan, SP 3 sp Institute for Glycomics, Griffith University at Gold Coast, Southport, QLD 4215, Australia, SP 4 sp Department of Biochemistry, Molecular and Cellular Biology, Georgetown University Medical Center, Washington, D.C. 20007, USA, SP 5 sp ZB MED Information Centre for Life Sciences, Gleueler Str. 60, 50931 Cologne, Germany, SP 6 sp Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Computer Science Department, University of Geneva, route de Drize 7, CH - 1227 Geneva Switzerland, and also Section of Biology, University of Geneva, Geneva, Switzerland, SP 7 sp Proteome Informatics Group, SIB Swiss Institute of Bio informatics, 7 Route de Drize, 1227 Geneva, Switzerland, SP 8 sp Faculty of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan, SP 9 sp Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA, SP 10 sp R&D Department, SparqLite LLC. [Extracted from the article]

Published
2022
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