Masaaki Matsubara , Mayumi Ishihara , Michael Tiemeyer , Kazuhiro Aoki , René Ranzinger
{"title":"DANGO: An MS data annotation tool for glycolipidomics","authors":"Masaaki Matsubara , Mayumi Ishihara , Michael Tiemeyer , Kazuhiro Aoki , René Ranzinger","doi":"10.1016/j.bbadva.2025.100161","DOIUrl":null,"url":null,"abstract":"<div><div>Glycosphingolipids are essential components of all eukaryotic cells and play a major role in a broad range of cellular and biological processes, including growth, cell signaling, survival, differentiation, and disease. Glycosphingolipid structural diversity arises from heterogeneity in both the glycan and lipid moieties. Most currently available computational tools for annotating mass spectrometry data for glycosphingolipids primarily focus on glycan structure analysis, although a tool for annotating intact glycosphingolipids has recently been reported. Developing tools that integrate both glycan-centric analytical approaches and dynamic lipid composition changes, which influence functional membrane characteristics, would be highly beneficial. We have developed a glycosphingolipid computational tool, named DANGO (Data ANnotation system for GlycolipidOmics), for the automated annotation of glycolipidomic datasets. DANGO supports processing and annotation of mass spectrometry data to characterize both the glycan and lipid (ceramide) moieties (http://www.ms-dango.org/). DANGO annotates MS datasets using a glycosphingolipid database, which is created from a curated or user-defined glycan and ceramide collection, and proposes candidate structures to the user that match the experimental data. DANGO is implemented as an extension of GRITS Toolbox (http://www.grits-toolbox.org), employing functionalities such as display routines for post-processing and organized annotation of data and relevant metadata. Implementation of a novel filter algorithm in DANGO reduces false-positive identifications, resulting in enhanced reliability and shortened computational time for acquiring glycosphingolipid structural annotation. The labor-intensive manual annotation of mass spectrometry datasets has been the only approach to confident assignment of glycosphingolipid structural identity. DANGO provides intuitive workflows for enhancing the annotation of glycosphingolipidomic data.</div></div>","PeriodicalId":34672,"journal":{"name":"BBA Advances","volume":"7 ","pages":"Article 100161"},"PeriodicalIF":4.5000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BBA Advances","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667160325000249","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/27 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0
Abstract
Glycosphingolipids are essential components of all eukaryotic cells and play a major role in a broad range of cellular and biological processes, including growth, cell signaling, survival, differentiation, and disease. Glycosphingolipid structural diversity arises from heterogeneity in both the glycan and lipid moieties. Most currently available computational tools for annotating mass spectrometry data for glycosphingolipids primarily focus on glycan structure analysis, although a tool for annotating intact glycosphingolipids has recently been reported. Developing tools that integrate both glycan-centric analytical approaches and dynamic lipid composition changes, which influence functional membrane characteristics, would be highly beneficial. We have developed a glycosphingolipid computational tool, named DANGO (Data ANnotation system for GlycolipidOmics), for the automated annotation of glycolipidomic datasets. DANGO supports processing and annotation of mass spectrometry data to characterize both the glycan and lipid (ceramide) moieties (http://www.ms-dango.org/). DANGO annotates MS datasets using a glycosphingolipid database, which is created from a curated or user-defined glycan and ceramide collection, and proposes candidate structures to the user that match the experimental data. DANGO is implemented as an extension of GRITS Toolbox (http://www.grits-toolbox.org), employing functionalities such as display routines for post-processing and organized annotation of data and relevant metadata. Implementation of a novel filter algorithm in DANGO reduces false-positive identifications, resulting in enhanced reliability and shortened computational time for acquiring glycosphingolipid structural annotation. The labor-intensive manual annotation of mass spectrometry datasets has been the only approach to confident assignment of glycosphingolipid structural identity. DANGO provides intuitive workflows for enhancing the annotation of glycosphingolipidomic data.
鞘糖脂是所有真核细胞的重要组成部分,在广泛的细胞和生物过程中发挥重要作用,包括生长、细胞信号传导、存活、分化和疾病。鞘糖脂结构的多样性源于糖基和脂质部分的异质性。目前大多数用于注释鞘糖脂质谱数据的计算工具主要集中在聚糖结构分析上,尽管最近报道了一种用于注释完整鞘糖脂的工具。开发集甘聚糖中心分析方法和影响功能膜特性的动态脂质组成变化为一体的工具将是非常有益的。我们开发了一个糖鞘脂计算工具,名为DANGO (Data ANnotation system for GlycolipidOmics),用于糖脂组学数据集的自动注释。DANGO支持质谱数据的处理和注释,以表征聚糖和脂质(神经酰胺)部分(http://www.ms-dango.org/)。DANGO使用糖鞘脂数据库注释MS数据集,该数据库由策划或用户定义的聚糖和神经酰胺收集创建,并向用户提出与实验数据匹配的候选结构。DANGO是作为GRITS工具箱(http://www.grits-toolbox.org)的扩展实现的,它采用了显示例程等功能来进行后处理,并对数据和相关元数据进行有组织的注释。在DANGO中实现了一种新的过滤算法,减少了假阳性识别,从而提高了获取鞘糖脂结构注释的可靠性并缩短了计算时间。劳动密集型的手工注释质谱数据集一直是唯一的方法来确定鞘糖脂结构的身份。DANGO为增强鞘糖脂组学数据的注释提供了直观的工作流程。