In response to the need for greater cohesion among regional conferences, the Asia Pacific Bioinformatics Network (APBioNET) set out in 2015 to realize a long-held aspiration-a single, unifying bioinformatics "super conference" for the Asia & Pacific community. Nearly a decade of persistence, coordination, and coalition-building led to the inaugural Asia & Pacific Bioinformatics Joint Congress (APBJC2024) in Okinawa, Japan. Now established as a triennial event, APBJC stands as a testament to the power of collective vision and shared purpose, offering a unifying platform for regional collaboration and scientific exchange. Tagline: Bringing a Region Together: The Making of APBJC.

In recent times, tree-structured data has been rapidly spreading with the advent of the World Wide Web and Bioinformatics. It is employed as a common format for the storage and depiction of data. Glycans, essential substances in all organisms, are biomolecules that play diverse functional roles in the body. They are commonly modeled as tree-structured data and analyzed using machine learning techniques. Nevertheless, more detailed information is imperative to comprehensively understand the biological attributes and functionalities of gly-cans. In this research, we aim to propose a new model called profileOTMM (profile Ordered Tree Markov Model) for the complete automation of glycan analysis. As an initial step, feature engineering was conducted using the OTMM model, which serves as the foundational model for profileOTMM. By improving the accuracy of glycan data input through OTMM, we integrated monosaccharides, the elemental constituents of glycans, along with their linkage types into tree-structured glycan data. This enhanced approach enabled the extraction of biologically meaningful structures with a high degree of precision and accuracy, surpassing the results of previous research efforts. This research will encourage the understanding of glycan structures and their functions.

BACKGROUND: Glycan-related genes play a fundamental role in various processes for energy acquisition and homeostasis maintenance while adapting to the environment in which the organism exists; however, their role in the microbiome in the environment is unclear. METHODS: Sequence alignment was performed between known glycan-related genes and complete genomes of microorganisms, and optimal parameters for identifying glycan-related genes were determined based on the alignments. Using the constructed scheme (> 90% of identity and > 25 aa of alignment length), glycan-related genes in various environments were identified from 198 different metagenome data. RESULTS: As a result, we identified 86.73 million glycan-related genes from the metagenome data. Among the 12 environments classified in this study, the percentage of glycan-related genes was high in the human-associated environment, suggesting that these environments utilize glycan metabolism better than other environments. On the other hand, the relative abundances of both glycoside hydrolases and glycosyltransferases surprisingly had a coverage of over 80% in all the environments. These glycoside hydrolases and glycosyltransferases were classified into two groups of (1) general enzyme families identified in various environments and (2) specific enzymes found only in certain environments. The general enzyme families were mostly from genes involved in monosaccharide metabolism, and most of the specific enzymes were polysaccharide degrading enzymes. CONCLUSION: These findings suggest that environmental microorganisms could change the composition of their glycan-related genes to adapt the processes involved in acquiring energy from glycans in their environments. Our functional glyco-metagenomics approach has made it possible to clarify the relationship between the environment and genes from the perspective of carbohydrates, and the existence of glycan-related genes that exist specifically in the environment.

SUMMARY: Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources. AVAILABILITY AND IMPLEMENTATION: GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Glycans are biomolecules that take an important role in the biological processes of living organisms. They form diverse, complicated structures such as branched and cyclic forms. Web3 Unique Representation of Carbohydrate Structures (WURCS) was proposed as a new linear notation for uniquely representing glycans during the GlyTouCan project. WURCS defines rules for complex glycan structures that other text formats did not support, and so it is possible to represent a wide variety glycans. However, WURCS uses a complicated nomenclature, so it is not human-readable. Therefore, we aimed to support the interpretation of WURCS by converting WURCS to the most basic and widely used format IUPAC. RESULTS: In this study, we developed GlycanFormatConverter and succeeded in converting WURCS to the three kinds of IUPAC formats (IUPAC-Extended, IUPAC-Condensed and IUPAC-Short). Furthermore, we have implemented functionality to import IUPAC-Extended, KEGG Chemical Function (KCF) and LinearCode formats and to export WURCS. We have thoroughly tested our GlycanFormatConverter and were able to show that it was possible to convert all the glycans registered in the GlyTouCan repository, with exceptions owing only to the limitations of the original format. The source code for this conversion tool has been released as an open source tool. AVAILABILITY AND IMPLEMENTATION: https://github.com/glycoinfo/GlycanFormatConverter.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: A glycan consists of monosaccharides linked by glycosidic bonds, has branches and forms complex molecular structures. Databases have been developed to store large amounts of glycan-binding experiments, including glycan arrays with glycan-binding proteins. However, there are few bioinformatics techniques to analyze large amounts of data for glycans because there are few tools that can handle the complexity of glycan structures. Thus, we have developed the MCAW (Multiple Carbohydrate Alignment with Weights) tool that can align multiple glycan structures, to aid in the understanding of their function as binding recognition molecules. Results: We have described in detail the first algorithm to perform multiple glycan alignments by modeling glycans as trees. To test our tool, we prepared several data sets, and as a result, we found that the glycan motif could be successfully aligned without any prior knowledge applied to the tool, and the known recognition binding sites of glycans could be aligned at a high rate amongst all our datasets tested. We thus claim that our tool is able to find meaningful glycan recognition and binding patterns using data obtained by glycan-binding experiments. The development and availability of an effective multiple glycan alignment tool opens possibilities for many other glycoinformatics analysis, making this work a big step towards furthering glycomics analysis. Availability and Implementation: http://www.rings.t.soka.ac.jp. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Complex carbohydrates play a central role in cellular communication and in disease development. O- and N-glycans, which are post-translationally attached to proteins and lipids, are sugar chains that are rooted, tree structures. Independent efforts to develop computational tools for analyzing complex carbohydrate structures have been designed to exploit specific databases requiring unique formatting and limited transferability. Attempts have been made at integrating these resources, yet it remains difficult to communicate and share data across several online resources. A disadvantage of the lack of coordination between development efforts is the inability of the user community to create reproducible analyses (workflows). The latter results in the more serious unreliability of glycomics metadata. RESULTS: In this paper, we realize the significance of connecting multiple online glycan resources that can be used to design reproducible experiments for obtaining, generating and analyzing cell glycomes. To address this, a suite of tools and utilities, have been integrated into the analytic functionality of the Galaxy bioinformatics platform to provide a Glycome Analytics Platform (GAP).Using this platform, users can design in silico workflows to manipulate various formats of glycan sequences and analyze glycomes through access to web data and services. We illustrate the central functionality and features of the GAP by way of example; we analyze and compare the features of the N-glycan glycome of monocytic cells sourced from two separate data depositions.This paper highlights the use of reproducible research methods for glycomics analysis and the GAP presents an opportunity for integrating tools in glycobioinformatics. AVAILABILITY AND IMPLEMENTATION: This software is open-source and available online at https://bitbucket.org/scientificomputing/glycome-analytics-platform CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Abnormalities in glycan biosynthesis have been conclusively related to various diseases, whereas the complexity of the glycosylation process has impeded the quantitative analysis of biochemical experimental data for the identification of glycoforms contributing to disease. To overcome this limitation, the automatic construction of glycosylation reaction networks in silico is a critical step. RESULTS: In this paper, a framework K2014 is developed to automatically construct N-glycosylation networks in MATLAB with the involvement of the 27 most-known enzyme reaction rules of 22 enzymes, as an extension of previous model KB2005. A toolbox named Glycosylation Network Analysis Toolbox (GNAT) is applied to define network properties systematically, including linkages, stereochemical specificity and reaction conditions of enzymes. Our network shows a strong ability to predict a wider range of glycans produced by the enzymes encountered in the Golgi Apparatus in human cell expression systems. CONCLUSIONS: Our results demonstrate a better understanding of the underlying glycosylation process and the potential of systems glycobiology tools for analyzing conventional biochemical or mass spectrometry-based experimental data quantitatively in a more realistic and practical way.

MOTIVATION: Over the last decades several glycomics-based bioinformatics resources and databases have been created and released to the public. Unfortunately, there is no common standard in the representation of the stored information or a common machine-readable interface allowing bioinformatics groups to easily extract and cross-reference the stored information. RESULTS: An international group of bioinformatics experts in the field of glycomics have worked together to create a standard Resource Description Framework (RDF) representation for glycomics data, focused on glycan sequences and related biological source, publications and experimental data. This RDF standard is defined by the GlycoRDF ontology and will be used by database providers to generate common machine-readable exports of the data stored in their databases. AVAILABILITY AND IMPLEMENTATION: The ontology, supporting documentation and source code used by database providers to generate standardized RDF are available online (http://www.glycoinfo.org/GlycoRDF/).

MOTIVATION: It is well known influenza viruses recognize and bind terminal sialic acid (SA) on glycans that are found on the cell surface. In this work, we used a data mining technique to analyze the glycan array data of influenza viruses to find novel glycan structures other than SA that may be involved in viral infection. RESULTS: In addition to SA structures noted previously, we noted the sulfated structures in the mining results. For verification, we overexpressed the sulfotransferase that is involved in synthesizing these structures, and we performed a viral infection experiment to assess changes in infection in these cells. In our results, we found that there is a 70-fold increase in these cells compared with the control. Thus, we have found a novel pattern in glycan structures that may be involved in viral infection. AVAILABILITY AND IMPLEMENTATION: The Glycan Miner Tool is available from the RINGS resource at http://www.rings.t.soka.ac.jp.

BACKGROUND: Recent progress in method development for characterising the branched structures of complex carbohydrates has now enabled higher throughput technology. Automation of structure analysis then calls for software development since adding meaning to large data collections in reasonable time requires corresponding bioinformatics methods and tools. Current glycobioinformatics resources do cover information on the structure and function of glycans, their interaction with proteins or their enzymatic synthesis. However, this information is partial, scattered and often difficult to find to for non-glycobiologists. METHODS: Following our diagnosis of the causes of the slow development of glycobioinformatics, we review the "objective" difficulties encountered in defining adequate formats for representing complex entities and developing efficient analysis software. RESULTS: Various solutions already implemented and strategies defined to bridge glycobiology with different fields and integrate the heterogeneous glyco-related information are presented. CONCLUSIONS: Despite the initial stage of our integrative efforts, this paper highlights the rapid expansion of glycomics, the validity of existing resources and the bright future of glycobioinformatics.

MOTIVATION: The development of glycomics technologies in recent years has produced a sufficient amount of data to begin analyzing the glycan structures present in various organisms and tissues. In particular, glycan profiling using mass spectrometry (MS) and tandem MS has generated a large amount of data that are waiting to be analyzed. The Consortium for Functional Glycomics (CFG) has provided a web resource for obtaining such glycan profiling data easily. Although an interactive spectrum viewer is provided on the website as a Java applet, it is not necessarily easy to search for particular glycans or to find commonalities between different tissues in a single organism, for example. Therefore, to allow users to better take advantage of the valuable glycome data that can be obtained from mass spectra and other leading technologies, we have developed a tool called Glycome Atlas which is pre-loaded with the data from the CFG and is also able to visualize local glycan profiling data for human and mouse. RESULTS: We have developed a tool to allow users to visualize and perform queries of glycome data. This tool, called GlycomeAtlas, is pre-loaded with glycome data as provided by the CFG. Moreover, users can load their own local glycome data into this tool to visualize and perform queries on their own data. AVAILABILITY: This tool is available at the following URL: http://www.rings.t.soka.ac.jp/GlycomeAtlas/GUI.html.

BACKGROUND: Glycobiology pertains to the study of carbohydrate sugar chains, or glycans, in a particular cell or organism. Many computational approaches have been proposed for analyzing these complex glycan structures, which are chains of monosaccharides. The monosaccharides are linked to one another by glycosidic bonds, which can take on a variety of comformations, thus forming branches and resulting in complex tree structures. The q-gram method is one of these recent methods used to understand glycan function based on the classification of their tree structures. This q-gram method assumes that for a certain q, different q-grams share no similarity among themselves. That is, that if two structures have completely different components, then they are completely different. However, from a biological standpoint, this is not the case. In this paper, we propose a weighted q-gram method to measure the similarity among glycans by incorporating the similarity of the geometric structures, monosaccharides and glycosidic bonds among q-grams. In contrast to the traditional q-gram method, our weighted q-gram method admits similarity among q-grams for a certain q. Thus our new kernels for glycan structure were developed and then applied in SVMs to classify glycans. RESULTS: Two glycan datasets were used to compare the weighted q-gram method and the original q-gram method. The results show that the incorporation of q-gram similarity improves the classification performance for all of the important glycan classes tested. CONCLUSION: The results in this paper indicate that similarity among q-grams obtained from geometric structure, monosaccharides and glycosidic linkage contributes to the glycan function classification. This is a big step towards the understanding of glycan function based on their complex structures.

Carbohydrates are considered the thirdclass of information-encoding biologicalmacromolecules. ‘‘Glycomics,’’ the scientificattempt to characterize and study carbohy-drates, is a rapidly emerging branch ofscience, for which informatics is just begin-ning. Glycomics requires sophisticated algo-rithmic approaches. Several algorithms andmodels have been developed for glycobiol-ogy research in the past several years. Thistutorial will provide a brief introduction tothe field of glycome informatics, which willinclude a primer on glycobiology as well asdescriptions of the algorithms and modelsthat have been developed in this field.The four essential molecular buildingblocks of cells are nucleic acids, proteins,lipids, and carbohydrates, often referred toas glycans. Nucleotide and protein sequenc-es are at the heart of nearly all bioinfor-matics applications and research, whereasglycan and lipid structures have been widelyneglected in bioinformatics. However, gly-cans are the most abundant and structurallydiverse biopolymers formed in nature.Bound to proteins, as glycoproteins, theyare known to affectthefunctions of proteins.More than half of all protein sequencesdeposited in the SWISS-PROT databankinclude potential glycosylation sites and thusmay be glycoproteins. Based on an analysisof well-annotated and characterized glyco-proteins inSWISS-PROT,itwas concludedthat more than half of all proteins areglycosylated [1].The development and use of informaticstools and databases for glycobiology andglycomics research has increased consider-ably in recent years. However, the generaldevelopment in this field can still beconsidered as being in its infancy whencompared to the genomics and proteomicsareas. In terms of bioinformatics in glyco-biology, there are several paths of researchthat are currently in progress. The develop-ment of algorithms to reliably support thecharacterization of glycan structures forhigh-throughput applications is the mostimmediate demand of the glycomics com-munity. Additionally, several major glyco-related projects (Consortium for FunctionalGlycomics [2], KEGG Glycan [3], GLY-COSCIENCES.de [4]) are maturing andprovide well-structured glyco-related datathat are awaiting data mining and analysis.With the exciting new developments incarbohydrate arrays and automated MSannotation, the analysis of the glycome hasreached a new level of sophistication, whichrequires broader informatics support. Thistutorial aims to give an overview of thecurrent status of carbohydrate databases, thenewest analytical techniques, as well as theinformatics needed for rapid progress inglycomics research.

Mining frequent patterns from large datasets is an important issue in data mining. Recently, complex and unstructured (or semi-structured) datasets have appeared as targets for major data mining applications, including text mining, web mining and bioinformatics. Our work focuses on labeled ordered trees, which are typically semi-structured datasets. In bioinformatics, carbohydrate sugar chains, or glycans, can be modeled as labeled ordered trees. Glycans are the third major class of biomolecules, having important roles in signaling and recognition. For mining labeled ordered trees, we propose a new probabilistic model and its efficient learning scheme which significantly improves the time and space complexity of an existing probabilistic model for labeled ordered trees. We evaluated the performance of the proposed model, comparing it with those of other probabilistic models, using synthetic as well as real datasets from glycobiology. Experimental results showed that the proposed model drastically reduced the computation time of the competing model, keeping the predictive power and avoiding overfitting to the training data. Finally, we assessed our results on real data from a variety of biological viewpoints, verifying known facts in glycobiology.

Mining frequent patterns is a general and important issue in data mining. Complex and unstructured (or semi-structured) datasets have appeared in major data mining applications, including text mining, web mining and bioinformatics. Mining patterns from these datasets is the focus of many of the current data mining approaches. We focus on labeled ordered trees, typical datasets of semi-structured data in data mining, and propose a new probabilistic model and its efficient learning scheme for mining labeled ordered trees. The proposed approach significantly improves the time and space complexity of an existing probabilistic modeling for labeled ordered trees, while maintaining its expressive power. We evaluated the performance of the proposed model, comparing it with that of the existing model, using synthetic as well as real datasets from the field of glycobiology. Experimental results showed that the proposed model drastically reduced the computation time of the competing model, keeping the predictive power and avoiding overfitting to the training data. Finally, we assessed our results using the proposed model on real data from a variety of biological viewpoints, verifying known facts in glycobiology.

MOTIVATION: Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules. RESULTS: We experimented on the HLA-A family with the binding nonamers of A1 supertype (HLA-A*0101, A*2601, A*2902, A*3002), A2 supertype (A*0201, A*0202, A*0203, A*0206, A*6802), A3 supertype (A*0301, A*1101, A*3101, A*3301, A*6801) and A24 supertype (A*2301 and A*2402), whose data were collected from six publicly available peptide databases and two private sources. The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype. Our approach can thus be used to help find new binders for MHC molecules.

MOTIVATION: Glycans are the third major class of biomolecules following DNA and proteins. They are extremely vital for the functioning of multicellular organisms. However, comparing the fast development of sequence analysis techniques, informatics work on glycans have a long way to go. Alignment algorithms for glycan tree structures are one of the foremost concerns. In addition, the statistical analysis of these algorithms in terms of biological significance needs to be addressed. RESULTS: We developed a tree-structure alignment algorithm for glycans and performed a statistical analysis of these alignment scores such that biologically interesting features could be captured into a score matrix for glycans. We generated our score matrix in a manner similar to BLOSUM, but with slight variations to accomodate our glycan data, including the incorporation of linkage information. We verified the effectiveness of our new glycan score matrix by illustrating how well the resulting score matrix entries correspond with biological knowledge. Future work for even better improvements with the use of a variety of score matrices for different subclasses of glycans due to their complexity is also discussed. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: The glycan score matrix can be downloaded from http://kanehisa.kuicr.kyoto-u.ac.jp/Paper/kcam/glycanMatrix0.1.txt.

Glycans, or carbohydrate sugar chains, which play a number of important roles in the development and functioning of multicellular organisms, can be regarded as labeled ordered trees. A recent increase in the documentation of glycan structures, especially in the form of database curation, has made mining glycans important for the understanding of living cells. We propose a probabilistic model for mining labeled ordered trees, and we further present an efficient learning algorithm for this model, based on an EM algorithm. The time and space complexities of this algorithm are rather favorable, falling within the practical limits set by a variety of existing probabilistic models, including stochastic context-free grammars. Experimental results have shown that, in a supervised problem setting, the proposed method outperformed five other competing methods by a statistically significant factor in all cases. We further applied the proposed method to aligning multiple glycan trees, and we detected biologically significant common subtrees in these alignments where the trees are automatically classified into subtypes already known in glycobiology.

We propose a new probabilistic model for mining labeled ordered trees. A noteworthy feature of the proposed model is to consider ordered siblings by modeling the dependencies of a node in a tree on the elder sibling as well as the parent. This model is reasonably extended from a variety of existing probabilistic models for strings and trees. We further propose a new learning/mining method to estimate the parameters of this model, based on an EM algorithm. This is also an extension of those for various simpler probabilistic models, such as hidden Markov models and hidden tree Markov models. We evaluated the effectiveness of our proposed method using both synthetic and real-world data sets, comparing the results with those of several simpler probabilistic models. Experimental results have shown that our proposed method outperforms the other methods compared, being statistically significant in all cases tested. This result tells us that the proposed methodology is highly effective for mining labeled ordered trees, which have recently emerged as one of the typical data structures in numerous data mining domains, including the web, text mining and bioinformatics.