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Archives for September 2012

MetiTree: a web application to organize and process high resolution multi-stage mass spectrometry metabolomics data

I am co-author of a new publication in the field of metabolomics and metabolite identification. Its title is “MetiTree: a web application to organize and process high resolution multi-stage mass spectrometry metabolomics data

MetiTree web application organize process high resolution multi stage mass spectrometry metabolomics data My Publications

Miguel,  Rojas-Cherto,; Michael,  van Vliet,; E,  Peironcely, Julio; Ronnie,  van Doorn,; Maarten,  Kooyman,; Te,  Beek, Tim; a,  van Driel, Marc; Thomas,  Hankemeier,; Theo,  Reijmers,

MetiTree: a web application to organize and process high resolution multi-stage mass spectrometry metabolomics data (Article)

Bioinformatics, Page(s): 2–4, 2012.

 

Abstract

SUMMARY: Identification of metabolites using high resolution multistage mass spectrometry (MS(n)) data is a significant challenge demanding access to all sorts of computational infrastructures. MetiTree is a user-friendly, web application dedicated to organize, process, share, visualize, and compare MS(n) data. It integrates several features to export and visualize complex MS(n) data, facilitating the exploration and interpretation of metabolomics experiments.

A dedicated spectral tree viewer allows the simultaneous presentation of three related types of MS(n) data, namely, the spectral data, the fragmentation tree, and the fragmentation reactions. MetiTree stores the data in an internal database to enable searching for similar fragmentation trees and matching against other MS(n) data. As such MetiTree contains much functionality that will make the difficult task of identifying unknown metabolites much easier.

AVAILABILITY: MetiTree is accessible at www.MetiTree.nl The source code is available here.

In Simple Words

MetiTree improves metabolite identification by providing a place where to store, process, and compare MSn data. With MetiTree you can:

  • Process your raw MSn data files and determine the elemental composition of you unknown metabolite using the MEF tool.
  • Compare your MSn data to the other MSn data you stored in MetiTree and find similar trees using our fragmentation tree fingerprint method.
  • Visualize your MSn trees.

Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees

I am co-author of a new publication in the field of metabolomics and metabolite identification. Its title is “Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees

metabolite identification using automated comparison of high resolution multistage mass spectral trees My Publications

Miquel, Rojas-Cherto,; E, Peironcely, Julio; T, Kasper, Piotr; J, van der Hooft, Justin J; H, de Vos, Ric C; Rob, Vreeken,; Thomas, Hankemeier,; Theo, Reijmers,

Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees (Article)

Analytical chemistry, 2012.

Abstract

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data.

Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n).

For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree.

Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.

 In Simple Words

Similar metabolites have similar mass spectral trees. Imagine you have a collection or database of mass spectral trees of known metabolites, i.e. for which you know the chemical structure.

Now you have a mass spectral tree of an unknown metabolite. We propose a method to use the similarity between trees to identify the unknown either:

  • By finding in the database a tree that is 100% similar. Here we could assign the identity to the unknown because we had it in the database.
  • We might find several similar metabolites (not 100%) in the database. If these metabolites belong to a sub-class, let’s say amino acids, we assign the class of the unknown, it should also be a amino acid.
  • These similar metabolites can have a common piece (a ring or a scaffold) which we speculate that is also present in the structure of the unknown metabolite. This piece, the Maximum Common Substructure, can help in metabolite identification and be used in Computer Assisted Structure Elucidation process to propose candidate structures for the unknown that contain such piece.