在代谢物识别和代谢学,生物信息学,生物信息学中,应对常见的陷
Elva María Novoa-Del-Toro1, Michael Witting2,3
1Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP- Purpan, UPS, 180 chemin de Tournefeuille St-Martin-du-Touch, BP 3, Toulouse Cedex, 31931, France.
Metabolomics : Official journal of the Metabolomic Society
|September 21, 2024
概括
这篇综述弥合了生物信息学家和代谢学分析化学家之间的差距. 它澄清了代谢物识别和生物信息学方法,以促进在这个不断增长的领域成功合作.
科学领域:
- 代谢学和生物信息学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 代谢学,研究小分子,是一个快速进步的领域,数据复杂性越来越高.
- 对于在代谢学数据分析和代谢物识别方面熟练的生物信息学家的需求日益增加.
- 当前的生物信息学课程和分析化学培训往往缺乏全面的代谢组件.
研究的目的:
- 提供一个教育综述,总结了代谢学生物信息学中的关键概念和常见陷.
- 解决生物信息学家和分析化学家之间关于代谢物注释和识别的误解.
- 为了促进生物信息学家进入代谢学的学习,特别是代谢物识别,以便进行有效的合作.
主要方法:
- 总结基于LC-MS/MS的非目标代谢学中的核心概念.
- 将代谢学数据类型与生物信息学家熟悉的数据类型进行比较.
- 突出跨学科合作中遇到的实际挑战和解决方案.
主要成果:
- 确定了代谢物注释和识别过程中的关键知识缺口和误解.
- 证明了代谢学数据和其他数据类型之间的相似性,以帮助生物信息学家理解.
- 为成功整合生物信息学工具在代谢学研究中提供了实际见解.
结论:
- 生物信息学家和分析化学家之间的有效合作对于促进代谢学发展至关重要.
- 需要有针对性的教育努力,以使生物信息学家具备必要的代谢生物信息学技能.
- 本综述作为指南,以提高对in-silico代谢物识别工具的理解和应用.
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