MetCirc: navigating mass spectral similarity in high‐resolution MS/MS metabolomics data
MetCirc: navigating mass spectral similarity in high‐resolution MS/MS metabolomics data
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MetCirc：在高分辨率 MS/MS 代谢组学数据中导航质谱相似性
DOI：
10.1093/bioinformatics/btx159
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发表时间：
2017
期刊：
影响因子：
5.8
通讯作者：
Emmanuel Gaquerel
中科院分区：
文献类型：
--
作者：
Thomas Naake;Emmanuel Gaquerel
总结：代谢组学的主要挑战之一是在一系列生物样品中快速去复制先前表征的代谢物，以及从MS/MS数据中预测未知物的结构。在这里，我们开发了MetCirc来全面对齐和计算MS/MS光谱数据之间的成对相似性得分，并在一系列生物样品中可视化这些。MetCirc包括根据化合物家族分组交互式组织这些数据的功能，并加速发现共享代谢物和未知假设的功能。因此，MetCirc为解决化学多样性发挥作用的领域的生物学问题提供了重大进展。可用性和实施：MetCirc是用开源R语言实现的，它的小插曲可以在Bioconductor项目和www.example.com上找到https://github.com/PlantDefenseMetabolism/MetCirc。联系方式：thomasnaake@googlemail.com或emmanuel.gaquerel@cos. uni-heidelberg.de补充信息：补充数据可在Bioinformatics online获得。
Summary: Among the main challenges in metabolomics are the rapid dereplication of previously characterized metabolites across a range of biological samples and the structural prediction of unknowns from MS/MS data. Here, we developed MetCirc to comprehensively align and calculate pairwise similarity scores among MS/MS spectral data and visualize these across a range of biological samples. MetCirc comprises functionalities to interactively organize these data according to compound familial groupings and to accelerate the discovery of shared metabolites and hypothesis formulation for unknowns. As such, MetCirc provides a significant advance to address biological questions in areas where chemodiversity plays a role. Availability and Implementation: MetCirc, implemented in the open‐source R language, together with its vignette are available in the Bioconductor project and at https://github.com/PlantDefenseMetabolism/MetCirc. Contact: thomasnaake@googlemail.com or emmanuel.gaquerel@cos.uni‐heidelberg.de Supplementary information: Supplementary data are available at Bioinformatics online.
DOI：
10.1073/pnas.1203689109
发表时间：
2012-06-26
影响因子：
11.1
作者：
Watrous, Jeramie;Roach, Patrick;Dorrestein, Pieter C.
通讯作者：
Dorrestein, Pieter C.