Kiyoko F. Aoki-Kinoshita

From genomics to chemical genomics: new developments in KEGG

The increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with the environment, as well as for medical, industrial and other practical applications. The KEGG resource () provides a reference knowledge base for linking genomes to biological systems, categorized as building blocks in the genomic space (KEGG GENES) and the chemical space (KEGG LIGAND), and wiring diagrams of interaction networks and reaction networks (KEGG PATHWAY). A fourth component, KEGG BRITE, has been formally added to the KEGG suite of databases. This reflects our attempt to computerize functional interpretations as part of the pathway reconstruction process based on the hierarchically structured knowledge about the genomic, chemical and network spaces. In accordance with the new chemical genomics initiatives, the scope of KEGG LIGAND has been significantly expanded to cover both endogenous and exogenous molecules. Specifically, RPAIR contains curated chemical structure transformation patterns extracted from known enzymatic reactions, which would enable analysis of genome-environment interactions, such as the prediction of new reactions and new enzyme genes that would degrade new environmental compounds. Additionally, drug information is now stored separately and linked to new KEGG DRUG structure maps.

Symbol Nomenclature for Graphical Representations of Glycans

Author(s): Varki, Ajit; Cummings, Richard D; Aebi, Markus; Packer, Nicole H; Seeberger, Peter H; Esko, Jeffrey D; Stanley, Pamela; Hart, Gerald; Darvill, Alan; Kinoshita, Taroh; Prestegard, James J; Schnaar, Ronald L; Freeze, Hudson H; Marth, Jamey D; Bertozzi, Carolyn R; Etzler, Marilynn E; Frank, Martin; Vliegenthart, Johannes Fg; Lutteke, Thomas; Perez, Serge; Bolton, Evan; Rudd, Pauline; Paulson, James; Kanehisa, Minoru; Toukach, Philip; Aoki-Kinoshita, Kiyoko F; Dell, Anne; Narimatsu, Hisashi; York, William; Taniguchi, Naoyuki; Kornfeld, Stuart

Gene Annotation and Pathway Mapping in KEGG

KEGG is a database resource (http://www.genome.jp/kegg/) that provides all knowledge about genomes and their relationships to biological systems such as cells and whole organisms as well as their interactions with the environment. KEGG is categorized in terms of building blocks in the genomic space, known as KEGG GENES, the chemical space, KEGG LIGAND, as well as wiring diagrams of interaction and reaction networks, known as KEGG PATHWAY. A fourth database called KEGG BRITE was also recently incorporated to provide computerized annotations and pathway reconstruction based on the current KEGG knowledgebase. KEGG BRITE contains KEGG Orthology (KO), a classification of ortholog and paralog groups based on highly confident sequence similarity scores, and the reaction classification system for biochemical reaction classification, along with other classifications for compounds and drugs. BRITE is also the basis for the KEGG Automatic Annotation Server (KAAS), which automatically annotates a given set of genes and correspondingly generates pathway maps. This chapter introduces KEGG and its various tools for genomic analyses, focusing on the usage of the KEGG GENES, PATHWAY, and BRITE resources and the KAAS tool (see Note 1).

UniCarbKB: building a knowledge platform for glycoproteomics

The UniCarb KnowledgeBase (UniCarbKB; http://unicarbkb.org) offers public access to a growing, curated database of information on the glycan structures of glycoproteins. UniCarbKB is an international effort that aims to further our understanding of structures, pathways and networks involved in glycosylation and glyco-mediated processes by integrating structural, experimental and functional glycoscience information. This initiative builds upon the success of the glycan structure database GlycoSuiteDB, together with the informatic standards introduced by EUROCarbDB, to provide a high-quality and updated resource to support glycomics and glycoproteomics research. UniCarbKB provides comprehensive information concerning glycan structures, and published glycoprotein information including global and site-specific attachment information. For the first release over 890 references, 3740 glycan structure entries and 400 glycoproteins have been curated. Further, 598 protein glycosylation sites have been annotated with experimentally confirmed glycan structures from the literature. Among these are 35 glycoproteins, 502 structures and 60 publications previously not included in GlycoSuiteDB. This article provides an update on the transformation of GlycoSuiteDB (featured in previous NAR Database issues and hosted by ExPASy since 2009) to UniCarbKB and its integration with UniProtKB and GlycoMod. Here, we introduce a refactored database, supported by substantial new curated data collections and intuitive user-interfaces that improve database searching.

An Introduction to Bioinformatics for Glycomics Research

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.

UniCarbKB: Putting the pieces together for glycomics research

Despite the success of several international initiatives the glycosciences still lack a managed infrastructure that contributes to the advancement of research through the provision of comprehensive structural and experimental glycan data collections. UniCarbKB is an initiative that aims to promote the creation of an online information storage and search platform for glycomics and glycobiology research. The knowledgebase will offer a freely accessible and information‐rich resource supported by querying interfaces, annotation technologies and the adoption of common standards to integrate structural, experimental and functional data. The UniCarbKB framework endeavors to support the growth of glycobioinformatics and the dissemination of knowledge through the provision of an open and unified portal to encourage the sharing of data. In order to achieve this, the framework is committed to the development of tools and procedures that support data annotation, and expanding interoperability through cross‐referencing of existing databases. Database URL: http://www.unicarbkb.org.

MIRAGE: The minimum information required for a glycomics experiment

The MIRAGE (minimum information required for a glycomics experiment) initiative was founded in Seattle, WA, in November 2011 in order to develop guidelines for reporting the qualitative and quantitative results obtained by diverse types of glycomics analyses, including the conditions and techniques that were applied to prepare the glycans for analysis and generate the primary data along with the tools and parameters that were used to process and annotate this data. These guidelines must address a broad range of issues, as glycomics data are inherently complex and are generated using diverse methods, including mass spectrometry (MS), chromatography, glycan array-binding assays, nuclear magnetic resonance (NMR) and other rapidly developing technologies. The acceptance of these guidelines by scientists conducting research on biological systems in which glycans have a significant role will facilitate the evaluation and reproduction of glycomics experiments and data that is reported in scientific journals and uploaded to glycomics databases. As a first step, MIRAGE guidelines for glycan analysis by MS have been recently published (Kolarich D, Rapp E, Struwe WB, Haslam SM, Zaia J., et al. 2013. The minimum information required for a glycomics experiment (MIRAGE) project – Improving the standards for reporting mass spectrometry-based glycoanalytic data. Mol. Cell Proteomics. 12:991–995), allowing them to be implemented and evaluated in the context of real-world glycobiology research. In this paper, we set out the historical context, organization structure and overarching objectives of the MIRAGE initiative.

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

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.

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.

The RINGS Resource for Glycome Informatics Analysis and Data Mining on the Web

In the bioinformatics field, many computer algorithmic and data mining technologies have been developed for gene prediction, protein-protein interaction analysis, sequence analysis, and protein folding predictions, to name a few. This kind of research has branched off from the genomics field, creating the transcriptomics, proteomics, metabolomics, and glycomics research areas in the postgenomic age. In the glycomics field, given the complexity of glycan structures with their branches of monosaccharides in various conformations, new data mining and algorithmic methods have been developed in an attempt to gain a better understanding of glycans. However, these methods have not all been implemented as tools such that the glycobiology community may utilize them in their research. Thus, we have developed RINGS (Resource for INformatics of Glycomes at Soka) as a freely available Web resource for glycobiologists to analyze their data using the latest data mining and algorithmic techniques. It provides a number of tools including a 2D glycan drawing and querying interface called DrawRINGS, a Glycan Pathway Predictor (GPP) tool for dynamically computing the N-glycan biosynthesis pathway from a given glycan structure, and data mining tools Glycan Miner Tool and Profile PSTMM. These tools and other utilities provided by RINGS will be described. The URL for RINGS is http://rings.t.soka.ac.jp/.