Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing.

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a 'hybrid ML' or 'white box ML' to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

GlycoStore: A Platform for H/UPLC and Capillary Electrophoresis Glycan Data.

GlycoStore ( http://www.glycostore.org ) is an open access chromatographic and electrophoretic retention database of glycans characterized from glycoproteins, glycolipids, and biotherapeutics. It is a continuation of the GlycoBase project (Oxford Glycobiology Institute and National Institute for Bioprocessing Research and Training, Ireland) but addresses many of the technological limitations that impacted the growth of GlycoBase, in particular, improvements to the bioinformatics architecture, enhancing data annotations and coverage, and improving connectivity with external resources. The first release of GlycoStore (October 2017) contains over 850 glycan entries accompanied by 8500+ retention positions including data from: (1) fluorescently labelled released glycans determined using hydrophilic interaction chromatography (HILIC) ultrahigh-performance liquid chromatography (U/HPLC) and reversed phase (RP)-U/HPLC; (2) porous graphitized carbon chromatography (PGC) interfaced with ESI-MS/MS; and (3) capillary electrophoresis with laser induced fluorescence detection (CE-LIF). In this chapter, we outline the objectives of GlycoStore, and describe a selection of step-by-step workflows for navigating and browsing the information available. We also provide a short description of informatics tools available to query the database using Semantic technologies. The information presented in this chapter supplements our documentation knowledge base that describes interface improvements, new features and tools, and content updates ( https://unicarbkb.freshdesk.com/ ).

Glycoinformatics Tools for Comprehensive Characterization of Glycans Enzymatically Released from Proteins.

Glycosylation is important in biology, contributing to both protein conformation and function. Structurally, glycosylation is complex and diverse. This complexity is reflected in the topology, composition, monosaccharide linkages, and isomerism of each oligosaccharide. Glycoanalytics is a discipline that addresses the understanding and characterization of this complexity and its correlation with biology. It includes analytical steps such as sample preparation, instrument measurements, and data analyses. Of these, data analysis has emerged as a critical bottleneck because data collection has increasingly become high-throughput. This has resulted in data-rich workflows that lack rapid and automated data analytics. To address this issue, the field has been developing software for interpretation of quantitative glycomics studies. Here, we describe a protocol using available informatics tools for analysis of data from analysis of released glycans using high-/ultraperformance liquid chromatography (H/UPLC) coupled with mass spectrometry (MS).

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of Monoclonal Antibody Biologics.

The glycosylation of antibody-based proteins is vital in translating the right therapeutic outcomes of the patient. Despite this, significant infrastructure is required to analyse biologic glycosylation in various unit operations from biologic development, process development to QA/QC in bio-manufacturing. Simplified mass spectrometers offer ease of operation as well as the portability of method development across various operations. Furthermore, data analysis would need to have a degree of automation to relay information back to the manufacturing line. We set out to investigate the applicability of using a semiautomated data analysis workflow to investigate glycosylation in different biologic development test cases. The workflow involves data acquisition using a BioAccord LC-MS system with a data-analytical tool called GlycopeptideGraphMS along with Progenesis QI to semi-automate glycoproteomic characterisation and quantitation with a LC-MS1 dataset of a glycopeptides and peptides. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 min with minimal user intervention. To demonstrate the effectiveness of the antibody and biologic glycopeptide assignment in various scenarios akin to biologic development activities, we demonstrate the effectiveness in the filtering of IgG1 and IgG2 subclasses from human serum IgG as well as innovator drugs trastuzumab and adalimumab and glycoforms by virtue of their glycosylation pattern. We demonstrate a high correlation between conventional released glycan analysis with fluorescent tagging and glycopeptide assignment derived from GraphMS. GraphMS workflow was then used to monitor the glycoform of our in-house trastuzumab biosimilar produced in fed-batch cultures. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.

Multiplexed engineering glycosyltransferase genes in CHO cells via targeted integration for producing antibodies with diverse complex-type N-glycans.

Therapeutic antibodies are decorated with complex-type N-glycans that significantly affect their biodistribution and bioactivity. The N-glycan structures on antibodies are incompletely processed in wild-type CHO cells due to their limited glycosylation capacity. To improve N-glycan processing, glycosyltransferase genes have been traditionally overexpressed in CHO cells to engineer the cellular N-glycosylation pathway by using random integration, which is often associated with large clonal variations in gene expression levels. In order to minimize the clonal variations, we used recombinase-mediated-cassette-exchange (RMCE) technology to overexpress a panel of 42 human glycosyltransferase genes to screen their impact on antibody N-linked glycosylation. The bottlenecks in the N-glycosylation pathway were identified and then released by overexpressing single or multiple critical genes. Overexpressing B4GalT1 gene alone in the CHO cells produced antibodies with more than 80% galactosylated bi-antennary N-glycans. Combinatorial overexpression of B4GalT1 and ST6Gal1 produced antibodies containing more than 70% sialylated bi-antennary N-glycans. In addition, antibodies with various tri-antennary N-glycans were obtained for the first time by overexpressing MGAT5 alone or in combination with B4GalT1 and ST6Gal1. The various N-glycan structures and the method for producing them in this work provide opportunities to study the glycan structure-and-function and develop novel recombinant antibodies for addressing different therapeutic applications.

Comprehensive N-glycosylation analysis of the influenza A virus proteins HA and NA from adherent and suspension MDCK cells.

Glycosylation is considered as a critical quality attribute for the production of recombinant biopharmaceuticals such as hormones, blood clotting factors, or monoclonal antibodies. In contrast, glycan patterns of immunogenic viral proteins, which differ significantly between the various expression systems, are hardly analyzed yet. The influenza A virus (IAV) proteins hemagglutinin (HA) and neuraminidase (NA) have multiple N-glycosylation sites, and alteration of N-glycan micro- and macroheterogeneity can have strong effects on virulence and immunogenicity. Here, we present a versatile and powerful glycoanalytical workflow that enables a comprehensive N-glycosylation analysis of IAV glycoproteins. We challenged our workflow with IAV (A/PR/8/34 H1N1) propagated in two closely related Madin-Darby canine kidney (MDCK) cell lines, namely an adherent MDCK cell line and its corresponding suspension cell line. As expected, N-glycan patterns of HA and NA from virus particles produced in both MDCK cell lines were similar. Detailed analysis of the HA N-glycan microheterogeneity showed an increasing variability and a higher complexity for N-glycosylation sites located closer to the head region of the molecule. In contrast, NA was found to be exclusively N-glycosylated at site N73. Almost all N-glycan structures were fucosylated. Furthermore, HA and NA N-glycan structures were exclusively hybrid- and complex-type structures, to some extent terminated with alpha-linked galactose(s) but also with blood group H type 2 and blood group A epitopes. In contrast to the similarity of the overall glycan pattern, differences in the relative abundance of individual structures were identified. This concerned, in particular, oligomannose-type, alpha-linked galactose, and multiantennary complex-type N-glycans.

Utility of Ion-Mobility Spectrometry for Deducing Branching of Multiply Charged Glycans and Glycopeptides in a High-Throughput Positive ion LC-FLR-IMS-MS Workflow.

High-throughput glycan analysis has become an important part of biopharmaceutical production and quality control. However, it is still a significant challenge in the field of glycomics to easily deduce isomeric glycan structures, especially in a high-throughput manner. Ion mobility spectrometry (IMS) is an excellent tool for differentiating isomeric glycan structures. However, demonstrations of the utility of IMS in high-throughput workflows such as liquid chromatography-fluorescence-mass spectrometry (LC-FLR-MS) workflows have been limited with only a small amount of collision cross section (CCS) data available. In particular, IMS data of glycan fragments obtained in positive ion mode are limited in comparison to those obtained in negative ion mode despite positive ion mode being widely used for glycomics. Here, we describe IMS TWCCSN2 data obtained from a high-throughput LC-FLR-IMS-MS workflow in positive ion mode. We obtained IMS data from a selection of RapiFluor-MS (RFMS) labeled N-glycans and also glycopeptides. We describe how IMS is able to distinguish isomeric N-glycans and glycopeptides using both intact IMS and fragment-based IMS glycan sequencing experiments in positive ion mode, without significantly altering the high-throughput nature of the analysis. For the first time, we were able to successfully use IMS in positive ion mode to determine the branching of isomeric glycopeptides and RFMS labeled glycans. Further, we highlight that IMS glycan sequencing of fragments obtained from RFMS labeled glycans was similar to that of glycopeptides. Finally, we show that the IMS glycan sequencing approach can highlight shared structural features of nonisomeric glycans in a high-throughput LC-FLR-IMS-MS workflow.

Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations.

The accurate assessment of antibody glycosylation during bioprocessing requires the high-throughput generation of large amounts of glycomics data. This allows bioprocess engineers to identify critical process parameters that control the glycosylation critical quality attributes. The advances made in protocols for capillary electrophoresis-laser-induced fluorescence (CE-LIF) measurements of antibody N-glycans have increased the potential for generating large datasets of N-glycosylation values for assessment. With large cohorts of CE-LIF data, peak picking and peak area calculations still remain a problem for fast and accurate quantitation, despite the presence of internal and external standards to reduce misalignment for the qualitative analysis. The peak picking and area calculation problems are often due to fluctuations introduced by varying process conditions resulting in heterogeneous peak shapes. Additionally, peaks with co-eluting glycans can produce peaks of a non-Gaussian nature in some process conditions and not in others. Here, we describe an approach to quantitatively and qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to 'divide and conquer' highly heterogeneous electropherograms into similar groups, making it easier to define peaks manually. Open-source software is then used to determine peak areas of the manually defined peaks. We successfully applied this semi-automated method to a dataset (containing 391 glycoprofiles) of monoclonal antibody biosimilars from a bioreactor optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method.

Cysteine Aminoethylation Enables the Site-Specific Glycosylation Analysis of Recombinant Human Erythropoietin using Trypsin.

Recombinant human erythropoietin (rhEPO) is an important biopharmaceutical for which glycosylation is a critical quality attribute. Therefore, robust analytical methods are needed for the in-depth characterization of rhEPO glycosylation. Currently, the protease GluC is widely established for the site-specific glycosylation analysis of rhEPO. However, this enzyme shows disadvantages, such as its specificity and the characteristics of the resulting (glyco)peptides. The use of trypsin, the gold standard protease in proteomics, as the sole protease for rhEPO is compromised, as no natural tryptic cleavage site is located between the glycosylation sites Asn24 and Asn38. Here, cysteine aminoethylation using 2-bromoethylamine was applied as an alternative alkylation strategy to introduce artificial tryptic cleavage sites at Cys29 and Cys33 in rhEPO. The (glyco)peptides resulting from a subsequent digestion using trypsin were analyzed by reverse-phase liquid chromatography-mass spectrometry. The new trypsin-based workflow was easily implemented by adapting the alkylation step in a conventional workflow and was directly compared to an established approach using GluC. The new method shows an improved specificity, a significantly reduced chromatogram complexity, allows for shorter analysis times, and simplifies data evaluation. Furthermore, the method allows for the monitoring of additional attributes, such as oxidation and deamidation at specific sites in parallel to the site-specific glycosylation analysis of rhEPO.

Exploiting the Disialyl Galactose Activity of α2,6-Sialyltransferase from Photobacterium damselae To Generate a Highly Sialylated Recombinant α-1-Antitrypsin.

Sialic acids are sugars present in many animal glycoproteins and are of particular interest in biopharmaceuticals, where a lack of sialylation can reduce bioactivity. Here, we describe how α-2,6-sialyltransferase from Photobacterium damselae can be used to markedly increase the level of sialylation of CHO-produced α-1-antitrypsin. Detailed analysis of the sialylation products showed that in addition to the expected α-2,6-sialylation of galactose, a second disialyl galactose motif Neu5Ac-α2,3(Neu5Ac-α2,6)Gal was produced, which, to our knowledge, had never been detected on a mammalian glycoprotein. We exploited this disialyl galactose activity of the P. damselae in a multienzyme reaction to produce a highly sialylated α-1-antitrypsin. The influence of this unique disialylation on the in vitro activity of α-1-antitrypsin was studied, and a toolkit of mass spectrometry methods for identifying this new disialyl galactose motif in complex mixtures was developed.

A human expression system based on HEK293 for the stable production of recombinant erythropoietin.

Mammalian host cell lines are the preferred expression systems for the manufacture of complex therapeutics and recombinant proteins. However, the most utilized mammalian host systems, namely Chinese hamster ovary (CHO), Sp2/0 and NS0 mouse myeloma cells, can produce glycoproteins with non-human glycans that may potentially illicit immunogenic responses. Hence, we developed a fully human expression system based on HEK293 cells for the stable and high titer production of recombinant proteins by first knocking out GLUL (encoding glutamine synthetase) using CRISPR-Cas9 system. Expression vectors using human GLUL as selection marker were then generated, with recombinant human erythropoietin (EPO) as our model protein. Selection was performed using methionine sulfoximine (MSX) to select for high EPO expression cells. EPO production of up to 92700 U/mL of EPO as analyzed by ELISA or 696 mg/L by densitometry was demonstrated in a 2 L stirred-tank fed batch bioreactor. Mass spectrometry analysis revealed that N-glycosylation of the produced EPO was similar to endogenous human proteins and non-human glycan epitopes were not detected. Collectively, our results highlight the use of a human cellular expression system for the high titer and xenogeneic-free production of EPO and possibly other complex recombinant proteins.

Combining Glucose Units, m/z, and Collision Cross Section Values: Multiattribute Data for Increased Accuracy in Automated Glycosphingolipid Glycan Identifications and Its Application in Triple Negative Breast Cancer.

Glycan head-groups attached to glycosphingolipids (GSLs) found in the cell membrane bilayer can alter in response to external stimuli and disease, making them potential markers and/or targets for cellular disease states. To identify such markers, comprehensive analyses of glycan structures must be undertaken. Conventional analyses of fluorescently labeled glycans using hydrophilic interaction high-performance liquid chromatography (HILIC) coupled with mass spectrometry (MS) provides relative quantitation and has the ability to perform automated glycan assignments using glucose unit (GU) and mass matching. The use of ion mobility (IM) as an additional level of separation can aid the characterization of closely related or isomeric structures through the generation of glycan collision cross section (CCS) identifiers. Here, we present a workflow for the analysis of procainamide-labeled GSL glycans using HILIC-IM-MS and a new, automated glycan identification strategy whereby multiple glycan attributes are combined to increase accuracy in automated structural assignments. For glycan matching and identification, an experimental reference database of GSL glycans containing GU, mass, and CCS values for each glycan was created. To assess the accuracy of glycan assignments, a distance-based confidence metric was used. The assignment accuracy was significantly better compared to conventional HILIC-MS approaches (using mass and GU only). This workflow was applied to the study of two Triple Negative Breast Cancer (TNBC) cell lines and revealed potential GSL glycosylation signatures characteristic of different TNBC subtypes.

GlycopeptideGraphMS: Improved Glycopeptide Detection and Identification by Exploiting Graph Theoretical Patterns in Mass and Retention Time.

The leading proteomic method for identifying N-glycosylated peptides is liquid chromatography coupled with tandem fragmentation mass spectrometry (LCMS/MS) followed by spectral matching of MS/MS fragment masses to a database of possible glycan and peptide combinations. Such database-dependent approaches come with challenges such as needing high-quality informative MS/MS spectra, ignoring unexpected glycan or peptide sequences, and making incorrect assignments because some glycan combinations are equivalent in mass to amino acids. To address these challenges, we present GlycopeptideGraphMS, a graph theoretical bioinformatic approach complementary to the database-dependent method. Using the AXL receptor tyrosine kinase (AXL) as a model glycoprotein with multiple N-glycosylation sites, we show that those LCMS features that could be grouped into graph networks on the basis of glycan mass and retention time differences were actually N-glycopeptides with the same peptide backbone but different N-glycan compositions. Conversely, unglycosylated peptides did not exhibit this grouping behavior. Furthermore, MS/MS sequencing of the glycan and peptide composition of just one N-glycopeptide in the graph was sufficient to identify the rest of the N-glycopeptides in the graph. By validating the identifications with exoglycosidase cocktails and MS/MS fragmentation, we determined the experimental false discovery rate of identifications to be 2.21%. GlycopeptideGraphMS detected more than 500 unique N-glycopeptides from AXL, triple the number found by a database search with Byonic software, and detected incorrect assignments due to a nonspecific protease cleavage. This method overcomes some limitations of the database approach and is a step closer to comprehensive automated glycoproteomics.

Post-Column Make-Up Flow (PCMF) Enhances the Performance of Capillary-Flow PGC-LC-MS/MS-Based Glycomics.

Deep characterization of biologically relevant glycans remains challenging. Porous graphitized carbon-liquid chromatography tandem mass spectrometry (PGC-LC-MS/MS) enables the quantitative elucidation of glycan fine structures. However, the early PGC-LC elution of smaller glycans (tri-, tetra-, and pentasaccharides) at low organic solvent content hampers their detection. In efforts to improve the glycan profiling sensitivity and accuracy, we present a new capillary-flow PGC-LC-MS/MS-based configuration comprising a post-column make-up flow (PCMF) that supplies an ion-promoting organic solvent to separated glycans prior to their detection by MS. The analytical performance of this setup was systematically evaluated against our existing capillary-flow PGC-LC-MS/MS platform (Jensen et al., Nat. Protoc. 2012, 7, 1299). Specifically, the ion intensities and signal-to-noise ratios of various classes of nonderivatized glycans from N- and O-glycoproteins and fructooligosaccharide mixtures were compared using methanol (MeOH)-, isopropanol (IPA)-, and acetonitrile (ACN)-based PCMF at various concentrations. In particular, ACN- and IPA-based PCMF dramatically increased the signal response across all glycan types (30- to 100-fold), improved the MS/MS spectral quality, and reduced the quantitative glycoprofile variation between replicates. In particular, the detection of the early eluting glycans benefitted from the PCMF. The highest sensitivity gains were achieved with the supplements of 100% ACN and IPA (equating to 57% (v/v) net concentration at the ion source) while neither compromising the favorable PGC-LC properties including the high peak capacity and glycan isomer separation nor changing the MS detection behavior. In conclusion, PCMF-based PGC-LC-MS/MS dramatically improves the glycomics sensitivity, coverage, and quantitative accuracy not least for the difficult-to-detect early eluting and low-abundance glycans detached from N- and O-glycoproteins.

GlycanAnalyzer: software for automated interpretation of N-glycan profiles after exoglycosidase digestions.

SUMMARY: Many eukaryotic proteins are modified by N-glycans. Liquid chromatography (ultra-performance -UPLC and high-performance-HPLC) coupled with mass spectrometry (MS) is conventionally used to characterize N-glycan structures. Software can automatically assign glycan structures by matching their observed retention times and masses with standardized values in reference databases. However, more precise confirmation of N-glycan structures can be derived using exoglycosidases, enzymes that remove specific monosaccharides from glycans. Exoglycosidase removal of monosaccharides results in signature peak shifts, in both UPLC and MS1, yielding an effective way to verify N-glycan structure with high detail (down to the position and isomeric linkage of each monosaccharide). Because manual interpretation of exoglycosidase data is complex and time consuming, we developed GlycanAnalyzer, a web application that pattern matches N-glycan peak shifts following exoglycosidase digestion and automates structure assignments. GlycanAnalyzer significantly improves assignment accuracy over other auto-assignment methods on tests with a monoclonal antibody and four glycan standards (100% versus 82% for the next best software). By automating data interpretation, GlycanAnalyzer enables the easier use of exoglycosidases to precisely define N-glycan structure. AVAILABILITY AND IMPLEMENTATION: http://glycananalyzer.neb.com. Datasets available online. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improvement of electrospray stability in negative ion mode for nano-PGC-LC-MS glycoanalysis via post-column make-up flow.

Analysis of glycans via a porous graphitized carbon liquid chromatography (PGC-LC) coupled with electrospray ionization (tandem) mass spectrometry (ESI-MS(/MS)) is a powerful analytical method in the field of glycomics. Isobaric glycan structures can be identified reliably with the help of PGC-LC separation and subsequent identification by ESI-MS(/MS) in negative ion mode. In an effort to adapt PGC-LC-ESI-MS(/MS) to the nano-scale operation, spray instability along the nano-PGC-LC gradient was repeatedly observed on an LTQ Orbitrap Elite mass spectrometer equipped with a standard nano-electrospray ionization source. A stable electrospray was achieved with the implementation of a post-column make-up flow (PCMF). Thereby, acetonitrile was used to supplement the eluate from the nano-PGC-LC column. The improved spray stability enhanced detection and resolution of glycans during the analysis. This was in particular the case for smaller O-glycans which elute early in the high aqueous content regime of the nano-PGC-LC elution gradient. This study introduces PCMF as an easy-to-use instrumental adaptation to significantly improve spray stability in negative ion mode nano-PGC-LC-ESI-MS(/MS)-based analysis of glycans.

GlycoStore: a database of retention properties for glycan analysis.

Summary: GlycoStore is a curated chromatographic, electrophoretic and mass-spectrometry composition database of N-, O-, glycosphingolipid (GSL) glycans and free oligosaccharides associated with a range of glycoproteins, glycolipids and biotherapeutics. The database is built on publicly available experimental datasets from GlycoBase developed in the Oxford Glycobiology Institute and then the National Institute for Bioprocessing Research and Training (NIBRT). It has now been extended to include recently published and in-house data collections from the Bioprocessing Technology Institute (BTI) A*STAR, Macquarie University and Ludger Ltd. GlycoStore provides access to approximately 850 unique glycan structure entries supported by over 8500 retention positions determined by: (i) hydrophilic interaction chromatography (HILIC) ultra-high performance liquid chromatography (U/HPLC) and reversed phase (RP)-U/HPLC with fluorescent detection; (ii) porous graphitized carbon (PGC) chromatography in combination with ESI-MS/MS detection; and (iii) capillary electrophoresis with laser induced fluorescence detection (CE-LIF). GlycoStore enhances many features previously available in GlycoBase while addressing the limitations of the data collections and model of this popular resource. GlycoStore aims to support detailed glycan analysis by providing a resource that underpins current workflows. It will be regularly updated by expert annotation of published data and data obtained from the project partners. Availability and implementation: http://www.glycostore.org. Supplementary information: Supplementary data are available at Bioinformatics online.