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糖组学数据处理工具 GlycoExtractor:一个基于网络的用于高效液相色谱-糖组学数据高通量处理的界面。

GlycoExtractor: a web-based interface for high throughput processing of HPLC-glycan data.

机构信息

Dublin-Oxford Glycobiology Laboratory, National Institute for Bioprocessing Research and Training (NIBRT), Conway Institute, University College Dublin, Dublin 4, Ireland.

出版信息

J Proteome Res. 2010 Apr 5;9(4):2037-41. doi: 10.1021/pr901213u.

Abstract

Recently, an automated high-throughput HPLC platform has been developed that can be used to fully sequence and quantify low concentrations of N-linked sugars released from glycoproteins, supported by an experimental database (GlycoBase) and analytical tools (autoGU). However, commercial packages that support the operation of HPLC instruments and data storage lack platforms for the extraction of large volumes of data. The lack of resources and agreed formats in glycomics is now a major limiting factor that restricts the development of bioinformatic tools and automated workflows for high-throughput HPLC data analysis. GlycoExtractor is a web-based tool that interfaces with a commercial HPLC database/software solution to facilitate the extraction of large volumes of processed glycan profile data (peak number, peak areas, and glucose unit values). The tool allows the user to export a series of sample sets to a set of file formats (XML, JSON, and CSV) rather than a collection of disconnected files. This approach not only reduces the amount of manual refinement required to export data into a suitable format for data analysis but also opens the field to new approaches for high-throughput data interpretation and storage, including biomarker discovery and validation and monitoring of online bioprocessing conditions for next generation biotherapeutics.

摘要

最近,开发了一种自动化高通量 HPLC 平台,该平台可用于从糖蛋白中完全测序和定量低浓度的 N-连接糖,该平台得到了实验数据库(GlycoBase)和分析工具(autoGU)的支持。然而,支持 HPLC 仪器操作和数据存储的商业软件包缺乏大容量数据提取的平台。糖组学中资源和商定格式的缺乏是限制生物信息学工具和高通量 HPLC 数据分析自动化工作流程发展的主要限制因素。GlycoExtractor 是一个基于网络的工具,可与商业 HPLC 数据库/软件解决方案接口,以方便提取大量处理过的聚糖图谱数据(峰数、峰面积和葡萄糖单位值)。该工具允许用户将一系列样本集导出到一组文件格式(XML、JSON 和 CSV),而不是一组不相关的文件。这种方法不仅减少了将数据导出到适合数据分析的格式所需的手动细化量,而且还为高通量数据解释和存储开辟了新途径,包括生物标志物的发现和验证以及下一代生物治疗剂在线生物加工条件的监测。

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