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Cell Syst. 2018 May 23;6(5):621-625.e5. doi: 10.1016/j.cels.2018.03.011. Epub 2018 Apr 25.

LipiDex: An Integrated Software Package for High-Confidence Lipid Identification.

Author information

1
Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA.
2
Morgridge Institute for Research, Madison, WI 53715, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA.
3
Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Morgridge Institute for Research, Madison, WI 53715, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA. Electronic address: jcoon@chem.wisc.edu.

Abstract

State-of-the-art proteomics software routinely quantifies thousands of peptides per experiment with minimal need for manual validation or processing of data. For the emerging field of discovery lipidomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS), comparably mature informatics tools do not exist. Here, we introduce LipiDex, a freely available software suite that unifies and automates all stages of lipid identification, reducing hands-on processing time from hours to minutes for even the most expansive datasets. LipiDex utilizes flexible in silico fragmentation templates and lipid-optimized MS/MS spectral matching routines to confidently identify and track hundreds of lipid species and unknown compounds from diverse sample matrices. Unique spectral and chromatographic peak purity algorithms accurately quantify co-isolation and co-elution of isobaric lipids, generating identifications that match the structural resolution afforded by the LC-MS/MS experiment. During final data filtering, ionization artifacts are removed to significantly reduce dataset redundancy. LipiDex interfaces with several LC-MS/MS software packages, enabling robust lipid identification to be readily incorporated into pre-existing data workflows.

KEYWORDS:

data analysis; lipid fragmentation; lipidomics; mass spectrometry; open-source software; spectral library

PMID:
29705063
PMCID:
PMC5967991
DOI:
10.1016/j.cels.2018.03.011
[Indexed for MEDLINE]
Free PMC Article

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