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.

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.