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Feature identification and post-mass calibration

AV Aina Vaivade
AW Anna Wiberg
PK Payam Emami Khoonsari
HC Henrik Carlsson
SH Stephanie Herman
AA Asma Al-Grety
EF Eva Freyhult
UO Ulla Olsson-Strömberg
JB Joachim Burman
KK Kim Kultima
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For the identification of lipidomics features several approaches were used; an in-house compound library, the LIPID MAPS database [28], and in silico library. These in silico libraries were created using the nf-core workflow metaboigniter [29]. The parameters used for CSIFingerID [30–32] can be found in Supplementary Table S5. When matching features to the libraries +/- 8 ppm and +/- 10 s in retention time was used based on internal standards. In addition, the generated CANAPOUS [33, 34] file from SIRIUS was used for feature classification. Metabolomic features were identified by matching (+/- 10 ppm and +/- 10 s in retention time) with a previously created library. The targeted MS/MS data was identified using CSIFIngerID (Supplementary Table S5). A post-mass calibration on the identified features was performed to evaluate the quality of the matches in the lipidomics data, resulting in a standard deviation (SD) of 1.08 ppm mass error in positive mode and 0.98 in negative mode (Supplementary Figure S1).

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