BioDeep/metabolomics-report-standards: BioDeep LC-MS Metabolite Identification Demo Report

A Metabolomics unknown feature identification report industry standards from BioNovoGene corporation.

2019.08.16# at Suzhou, China

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, enables the reinterrogation and comparison of data by others, which is also could let us interpret the result in a more clearly way.

This article is mainly address at the unknown metabolite identification in LC-MS experiment, and proposes the reporting standards related to the chemical analysis aspects of metabolomics experiments its metabolite identification.

Some terms in this article that address to:

feature, the term feature in this article is refer to a parent ion in LC-MS experiment result raw data. Where a parent ion feature is a peak in chromatography data, which is consist of mass to charge ratio in ms1 level and its retention time (with a range of lower bound and upper bound) in chromatography experiment result.

annotation, the term annotation in this article is refer to the multidimensional information about the metabolite that assigned to a unknown feature, which such multidimensional information consist with the metabolite its cross reference id in different database, common name, basic chemical data like mass and formula composition and its molecule structure information, etc.

alignment, the term alignment means a kind of operation that use to compare the similarity of the mass spectrum data between user sample and the reference standard library. Such similarity comparison result is the most important evidence that use for unknown feature its identification.

score, the term score is a kind of numeric value that produced by the alignment comparison calculation. Literally, the higher score the alignment it produce, the better the result it is.

Our metabolite identification report consist with two parts of data which present to our user:

Report excel table that contains the raw sample information and the meta annotation information of the metabolite.

Data visual plot for the mass spectrum alignment details.

BioNovoGene公司发布的代谢组学未知特征鉴定报告行业标准

2019年8月16日，中国苏州

学界普遍认为，对大规模代谢组学数据集的元数据或描述信息进行标准化报告是必要的。标准化元数据报告可为数据集提供生物学与实验学背景，支持其他研究者对数据进行复现解析与比对，同时也能让研究结果的阐释更为清晰准确。

本文聚焦液相色谱-质谱（LC-MS）实验中的未知代谢物鉴定工作，提出了与代谢组学实验及其代谢物鉴定相关的化学分析层面报告标准。

本文涉及的相关术语定义如下：
1. 特征（feature）：本文中该术语指代LC-MS实验原始数据中的母离子。母离子特征为色谱数据中的色谱峰，由一级质谱（MS1）层面的质荷比，以及色谱实验中具有上下限范围的保留时间共同构成。
2. 注释（annotation）：本文中该术语指代分配至某一未知特征的代谢物多维信息，此类信息包括该代谢物在不同数据库中的交叉引用标识符、通用名称、质量与分子式组成等基础化学数据，以及分子结构信息等。
3. 比对（alignment）：本文中该术语指代用于比对用户样本与参考标准库的质谱数据相似性的操作，此类相似性比对结果是未知特征鉴定的核心依据。
4. 得分（score）：本文中该术语指代通过比对计算得到的数值。一般而言，比对得到的得分越高，鉴定结果越好。

我们面向用户交付的代谢物鉴定报告包含两部分数据：
- 包含原始样本信息与代谢物元注释信息的Excel报告表格；
- 展示质谱比对细节的数据可视化图表。