High resolution LC-MS untargeted lipidomics using data independent acquisition 
(DIA) has the potential to increase lipidome coverage, as it enables the 
continuous and unbiased acquisition of all eluting ions. However, the loss of 
the link between the precursor and the product ions combined with the high 
dimensionality of DIA data sets hinder accurate feature annotation. Here, we 
present LipidMS, an R package aimed to confidently identify lipid species in 
untargeted LC-DIA-MS. To this end, LipidMS combines a coelution score, which 
links precursor and fragment ions with fragmentation and intensity rules. 
Depending on the MS evidence reached by the identification function survey, 
LipidMS provides three levels of structural annotations: (i) "subclass level", 
e.g., PG(34:1); (ii) "fatty acyl level", e.g., PG(16:0_18:1); and (iii) "fatty 
acyl position level", e.g., PG(16:0/18:1). The comparison of LipidMS with freely 
available data dependent acquisition (DDA) and DIA identification tools showed 
that LipidMS provides significantly more accurate and structural informative 
lipid identifications. Finally, to exemplify the utility of LipidMS, we 
investigated the lipidomic serum profile of patients diagnosed with nonalcoholic 
steatohepatitis (NASH), which is the progressive form of nonalcoholic fatty 
liver disease, a disorder underlying a strong lipid dysregulation. As previously 
published, a significant decrease in lysophosphatidylcholines, 
phosphatidylcholines and cholesterol esters and an increase in 
phosphatidylethanolamines were observed in NASH patients. Remarkably, LipidMS 
allowed the identification of a new set of lipids that may be used for NASH 
diagnosis. Altogether, LipidMS has been validated as a tool to assist lipid 
identification in the LC-DIA-MS untargeted analysis of complex biological 
samples.