Literature DB >> 26289378

PredRet: prediction of retention time by direct mapping between multiple chromatographic systems.

Jan Stanstrup1, Steffen Neumann2, Urška Vrhovšek1.   

Abstract

Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org , a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.

Entities:  

Mesh:

Year:  2015        PMID: 26289378     DOI: 10.1021/acs.analchem.5b02287

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  29 in total

Review 1.  New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells.

Authors:  Sneha P Couvillion; Ying Zhu; Gabe Nagy; Joshua N Adkins; Charles Ansong; Ryan S Renslow; Paul D Piehowski; Yehia M Ibrahim; Ryan T Kelly; Thomas O Metz
Journal:  Analyst       Date:  2019-01-28       Impact factor: 4.616

2.  Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data.

Authors:  Bertrand Rochat
Journal:  J Am Soc Mass Spectrom       Date:  2017-01-23       Impact factor: 3.109

Review 3.  Challenges in Identifying the Dark Molecules of Life.

Authors:  María Eugenia Monge; James N Dodds; Erin S Baker; Arthur S Edison; Facundo M Fernández
Journal:  Annu Rev Anal Chem (Palo Alto Calif)       Date:  2019-03-18       Impact factor: 10.745

Review 4.  Annotation: A Computational Solution for Streamlining Metabolomics Analysis.

Authors:  Xavier Domingo-Almenara; J Rafael Montenegro-Burke; H Paul Benton; Gary Siuzdak
Journal:  Anal Chem       Date:  2017-11-03       Impact factor: 6.986

5.  Probabilistic metabolite annotation using retention time prediction and meta-learned projections.

Authors:  Constantino A García; Alberto Gil-de-la-Fuente; Coral Barbas; Abraham Otero
Journal:  J Cheminform       Date:  2022-06-07       Impact factor: 8.489

6.  Integrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures.

Authors:  Leah Chibwe; Ivan A Titaley; Eunha Hoh; Staci L Massey Simonich
Journal:  Environ Sci Technol Lett       Date:  2017-01-04

7.  Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics.

Authors:  Paolo Bonini; Tobias Kind; Hiroshi Tsugawa; Dinesh Kumar Barupal; Oliver Fiehn
Journal:  Anal Chem       Date:  2020-05-21       Impact factor: 6.986

8.  High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra.

Authors:  Erandika Karunaratne; Dennis W Hill; Philipp Pracht; José A Gascón; Stefan Grimme; David F Grant
Journal:  Anal Chem       Date:  2021-07-21       Impact factor: 8.008

Review 9.  Data standards can boost metabolomics research, and if there is a will, there is a way.

Authors:  Philippe Rocca-Serra; Reza M Salek; Masanori Arita; Elon Correa; Saravanan Dayalan; Alejandra Gonzalez-Beltran; Tim Ebbels; Royston Goodacre; Janna Hastings; Kenneth Haug; Albert Koulman; Macha Nikolski; Matej Oresic; Susanna-Assunta Sansone; Daniel Schober; James Smith; Christoph Steinbeck; Mark R Viant; Steffen Neumann
Journal:  Metabolomics       Date:  2015-11-17       Impact factor: 4.290

10.  Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification.

Authors:  Eric Bach; Simon Rogers; John Williamson; Juho Rousu
Journal:  Bioinformatics       Date:  2021-07-19       Impact factor: 6.937

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