LipidFinder 2.0: advanced informatics pipeline for lipidomics discovery applications

Abstract

We present LipidFinder 2.0, incorporating four new modules that apply artefact filters, remove lipid and contaminant stacks, in-source fragments and salt clusters, and a new isotope deletion method which is significantly more sensitive than available open-access alternatives. We also incorporate a novel false discovery rate (FDR) method, utilizing a target-decoy strategy, which allows users to assess data quality. A renewed lipid profiling method is introduced which searches three different databases from LIPID MAPS and returns bulk lipid structures only, and a lipid category scatter plot with color blind friendly pallet. An API interface with XCMS Online is made available on LipidFinder’s online version. We show using real data that LipidFinder 2.0 provides a significant improvement over non-lipid metabolite filtering and lipid profiling, compared to available tools. Availability LipidFinder 2.0 is freely available at https://github.com/ODonnell-Lipidomics/LipidFinder and http://lipidmaps.org/resources/tools/lipidfinder. Supplementary information Supplementary data are available at Bioinformatics online.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

“…LipidFinder [ 58 , 59 , 60 ] is a Python tool and web application available from the LIPID MAPS website that supports untargeted identification of lipids in LC-MS data, using XCMS for initial feature finding and custom filter and post-processing steps specifically tailored to lipidomics. Input formats are those that are also supported by XCMS, but specifically CSV and JSON, to transfer feature data and configuration settings to the application.…”

“…For oxidized phospholipids, LPPTiger [ 60 ] is an option for data-dependent LC-MS/MS data. It is implemented in Python and uses in-silico generated spectral libraries together with a composite score based on individual similarity, rank, fingerprint, isotope matching and specificity scores.…”

Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER

“…LC-MS/MS data analysis typically involves raw data pre-processing, feature annotation, and statistical analysis. 1 There are many existing software and algorithms to perform different steps in the analysis pipeline, including XCMS, 2 OpenMS, 3 MS-DIAL, 4 LipidFinder, 5 and MZmine2 6 for MS data processing, LipidMS, 7 LipidMatch from LipidMatch Flow, 8 METLIN, 9 and metID 10 for feature annotation, ProteoMM 11 and CRMN 12 for data normalization, and limma 13 and MetaboAnalyst 14 for statistical analysis and data visualization. However, these tools often operate as standalone programs for specific tasks such that there is a lack of a single workflow from raw data to statistical analysis.…”

ADViSELipidomics: a workflow for analyzing lipidomics data

“…LipidSearch is a well-known commercial software for preprocessing both LC-MS and LC-MS/MS data, providing accurate peak-picking identification and integration, and visualization of results. LipidFinder is an open-source Python workflow that combines several different databases to obtain the putative identification of lipids by LC-MS ( Alvarez-Jarreta et al , 2021 ). Lipid Quantification and Identification (LIQUID) is a C# open-source software that allows semi-automated processing of data against a customizable target library, and visualization of LC-MS/MS lipidomic data ( Kyle et al , 2017 ).…”