In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids

Lars J Kangas; Thomas O Metz; Giorgis Isaac; Brian T Schrom; Bojana Ginovska-Pangovska; Luning Wang; Li Tan; Robert R Lewis; John H Miller

doi:10.1093/bioinformatics/bts194

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In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids

Journal article

Open access

Peer reviewed

In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids

Lars J Kangas, Thomas O Metz, Giorgis Isaac, Brian T Schrom, Bojana Ginovska-Pangovska, Luning Wang, Li Tan, Robert R Lewis and John H Miller

Bioinformatics (Oxford, England), Vol.28(13), pp.1705-1713

07/01/2012

DOI: https://doi.org/10.1093/bioinformatics/bts194

Handle:

https://hdl.handle.net/2376/104257

PMCID: PMC3381961

PMID: 22592377

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https://doi.org/10.1093/bioinformatics/bts194View

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Abstract

Metabolomics

Lipids - chemistry

Algorithms

Artificial Intelligence

Computer Simulation

Sensitivity and Specificity

Software

Lipids - analysis

Tandem Mass Spectrometry - methods

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissociation tandem mass spectrometry. A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity.

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Title: In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids
Creators: Lars J Kangas - Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. lars.kangas@pnnl.gov
Thomas O Metz
Giorgis Isaac
Brian T Schrom
Bojana Ginovska-Pangovska
Luning Wang
Li Tan
Robert R Lewis
John H Miller
Publication Details: Bioinformatics (Oxford, England), Vol.28(13), pp.1705-1713
Academic Unit: Engineering and Applied Sciences (TRIC), School of
Publisher: England
Grant note: HHSN272200800060C / NIAID NIH HHS R21 DK071283 / NIDDK NIH HHS U54 AI081680 / NIAID NIH HHS DK071283 / NIDDK NIH HHS U54AI081680 / NIAID NIH HHS R33 DK071283 / NIDDK NIH HHS
Identifiers: 99900547001801842
Language: English
Resource Type: Journal article