Liquid-chromatography retention order prediction for metabolite identification

Eric Bach*, Sandor Szedmak, Celine Brouard, Sebastian Boecker, Juho Rousu

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

32 Sitaatiot (Scopus)
102 Lataukset (Pure)

Abstrakti

Motivation: Liquid Chromatography (LC) followed by tandem Mass Spectrometry (MS/MS) is one of the predominant methods for metabolite identification. In recent years, machine learning has started to transform the analysis of tandem mass spectra and the identification of small molecules. In contrast, LC data is rarely used to improve metabolite identification, despite numerous published methods for retention time prediction using machine learning.

Results: We present a machine learning method for predicting the retention order of molecules; that is, the order in which molecules elute from the LC column. Our method has important advantages over previous approaches: We show that retention order is much better conserved between instruments than retention time. To this end, our method can be trained using retention time measurements from different LC systems and configurations without tedious pre-processing, significantly increasing the amount of available training data. Our experiments demonstrate that retention order prediction is an effective way to learn retention behaviour of molecules from heterogeneous retention time data. Finally, we demonstrate how retention order prediction and MS/MS-based scores can be combined for more accurate metabolite identifications when analyzing a complete LC-MS/MS run.

AlkuperäiskieliEnglanti
Sivut875-883
Sivumäärä9
JulkaisuBioinformatics
Vuosikerta34
Numero17
DOI - pysyväislinkit
TilaJulkaistu - 1 syysk. 2018
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu
TapahtumaEuropean Conference on Computational Biology - Athens, Kreikka
Kesto: 8 syysk. 201812 syysk. 2018
Konferenssinumero: 17

Sormenjälki

Sukella tutkimusaiheisiin 'Liquid-chromatography retention order prediction for metabolite identification'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä