MOLecular structure GENeration with MOLGEN, new features and future developments

Benecke, C.; Grüner, T.; Kerber, Adalbert; Laue, Reinhard; Wieland, Thomas

doi:10.1007/s002160050530

Cited by 35 publications

(26 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sub-structure generation via exhaustive combinatorial tools, such as MOLGEN ( MOL ecular structure GEN eration) [82], quickly leads to computational performance problems and may not explain all peaks found in experimental or in-silico fragmentation spectra due to the vast number of similar candidate structures and the lack of rich sub-structure information from spectra [83]. Sub-structures and characteristic product ions are searchable using MS2Analyzer [84].…”

Section: Computational Tools For Msn and Fragmentation Treesmentioning

confidence: 99%

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Vaniya

Fiehn

2015

TrAC Trends in Analytical Chemistry

126

View full text Add to dashboard Cite

Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.

show abstract

Section: Computational Tools For Msn and Fragmentation Treesmentioning

confidence: 99%

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Vaniya

Fiehn

2015

TrAC Trends in Analytical Chemistry

126

View full text Add to dashboard Cite

show abstract

“…Scarcely checked, mass spectra were seldom discarded because of measurement deficiencies such as incorrect mass calibration, limited mass range (starting mass too high), electron multiplier saturation (several peaks show 100% base intensity), poor dynamic range, peaks from contaminations, and lacking isotope peaks. Even mass spectra The numbers are given for a mass spectral database (NIST 05), commercially available compounds, registered compounds in the Chemical Abstracts Service (CAS), and exhaustive isomer generation by software MOLGEN [5]. In the meantime, the need for high-quality spectra has been recognized and some attempts were made to evaluate the quality of the existing databases and to eliminate the incorrect and low-quality mass spectra.…”

Section: Quality Of Mass Spectra In Databasesmentioning

confidence: 99%

Mass Spectrometry

Werther¹

2014

Handbook of Spectroscopy

View full text Add to dashboard Cite

“…The versions currently available are MOLGEN 3.5 [2,3] and the newer development, MOLGEN 5.0 [4,5]. Both of these generate structures that match the molecular formula(s) and optional structural restrictions provided by the user, but are implemented differently.…”

Section: Introductionmentioning

confidence: 99%

Small Molecule Identification with MOLGEN and Mass Spectrometry

Meringer

Schymanski

2013

Metabolites

View full text Add to dashboard Cite

This paper details the MOLGEN entries for the 2012 CASMI contest for small molecule identification to demonstrate structure elucidation using structure generation approaches. Different MOLGEN programs were used for different categories, including MOLGEN–MS/MS for Category 1, MOLGEN 3.5 and 5.0 for Category 2 and MOLGEN–MS for Categories 3 and 4. A greater focus is given to Categories 1 and 2, as most CASMI participants entered these categories. The settings used and the reasons behind them are described in detail, while various evaluations are used to put these results into perspective. As one author was also an organiser of CASMI, these submissions were not part of the official CASMI competition, but this paper provides an insight into how unknown identification could be performed using structure generation approaches. The approaches are semi-automated (category dependent) and benefit greatly from user experience. Thus, the results presented and discussed here may be better than those an inexperienced user could obtain with MOLGEN programs.

show abstract

MOLecular structure GENeration with MOLGEN, new features and future developments

Cited by 35 publications

References 21 publications

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Mass Spectrometry

Small Molecule Identification with MOLGEN and Mass Spectrometry

Contact Info

Product

Resources

About