Published August 16, 2019 | Version v3.3.375-biodeepMSMS
Dataset Open

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


  • 1. @BioNovoGene


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:

  1. Report excel table that contains the raw sample information and the meta annotation information of the metabolite.
  2. Data visual plot for the mass spectrum alignment details.


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



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Additional details


  • MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition. DOI: 10.1021/acs.analchem.6b02122
  • KAAS: an automatic genome annotation and pathway reconstruction server. DOI: 10.1093/nar/gkm321