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GlyQ-IQ: Glycomics Quintavariate-Informed Quantificationwith High-Performance Computing and GlycoGrid 4D Visualization

机译:GlyQ-IQ:Glycomics五变量分析定量高性能计算和GlycoGrid 4D可视化

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摘要

Glycomics quintavariate-informed quantification (GlyQ-IQ) is a biologically guided glycomics analysis tool for identifying N-glycans in liquid chromatography–mass spectrometry (LC–MS) data. Glycomics LC–MS data sets have convoluted extracted ion chromatograms that are challenging to deconvolve with existing software tools. LC deconvolution into constituent pieces is critical in glycomics data sets because chromatographic peaks correspond to different intact glycan structural isomers. The biological targeted analysis approach offers several key advantages to traditional LC–MS data processing. A priori glycan information about the individual target’s elemental composition allows for improved sensitivity by utilizing the exact isotope profile information to focus chromatogram generation and LC peak fitting on the isotopic species having the highest intensity. Glycan target annotation utilizes glycan family relationships and in source fragmentation in addition to high specificity feature LC–MS detection to improve the specificity of the analysis. The GlyQ-IQ software was developed in this work and evaluated in the context ofprofiling the N-glycan compositions from human serum LC–MSdata sets. A case study is presented to demonstrate how GlyQ-IQ identifiesand removes confounding chromatographic peaks from high mannose glycanisomers from human blood serum. In addition, GlyQ-IQ was used to generatea broad human serum N-glycan profile from a high resolution nanoelectrospray-liquidchromatography–tandem mass spectrometry (nESI-LC–MS/MS)data set. A total of 156 glycan compositions and 640 glycan isomerswere detected from a single sample. Over 99% of the GlyQ-IQ glycan-featureassignments passed manual validation and are backed with high-resolutionmass spectra.
机译:糖蛋白五变量信息定量(GlyQ-IQ)是一种生物学指导的糖组学分析工具,用于鉴定液相色谱-质谱(LC-MS)数据中的N-聚糖。 Glycomics LC-MS数据集具有复杂的提取离子色谱图,很难与现有软件工具进行卷积解卷积。在色谱数据集中,LC解卷积成为组成部分至关重要,因为色谱峰对应于不同的完整聚糖结构异构体。生物靶向分析方法为传统LC-MS数据处理提供了多个关键优势。有关单个目标元素组成的先验聚糖信息可通过利用确切的同位素分布信息将色谱图生成和LC峰拟合集中在强度最高的同位素上来提高灵敏度。糖靶标注释除了利用高特异性特征LC-MS检测外,还利用聚糖家族关系和源片段化来提高分析的特异性。在这项工作中开发了GlyQ-IQ软件,并在以下情况下对其进行了评估:从人血清LC–MS中分析N-聚糖成分数据集。案例研究表明GlyQ-IQ如何识别并从高甘露糖聚糖中去除混淆的色谱峰人血清中的异构体。此外,GlyQ-IQ用于生成来自高分辨率纳米电喷雾液体的广泛的人血清N-聚糖谱色谱-串联质谱法(nESI-LC-MS / MS)数据集。共有156种聚糖成分和640种异构体从单个样品中检测到。超过99%的GlyQ-IQ聚糖特征作业通过了手动验证,并得到了高分辨率的支持质谱。

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