Lipidomics increasingly describes the quantitation using mass spectrometry of all lipids present in a biological sample.  As the power of lipidomics protocols increase, thousands of lipid molecular species from multiple categories can now be profiled in a single experiment.  Observed changes due to biological differences often encompass large numbers of structurally-related lipids, with these being regulated by enzymes from well-known metabolic pathways.  As lipidomics datasets increase in complexity, the interpretation of their results becomes more challenging.  BioPAN addresses this by enabling the researcher to visualise quantitative lipidomics data in the context of known biosynthetic pathways.  BioPAN provides a list of genes, which could be involved in the activation or suppression of enzymes catalysing lipid metabolism in mammalian tissues.

Lipidomics increasingly describes the quantification using mass spectrometry of all lipids present in a biological sample.  As the power of lipidomics protocols increase, thousands of lipid molecular species from multiple categories can now be profiled in a single experiment.  Observed changes due to biological differences often encompass large numbers of structurally-related lipids, with these being regulated by enzymes from well-known metabolic pathways.  As lipidomics datasets increase in complexity, the interpretation of their results becomes more challenging.  BioPAN addresses this by enabling the researcher to visualise quantitative lipidomics data in the context of known biosynthetic pathways.  BioPAN provides a list of genes, which could be involved in the activation or suppression of enzymes catalysing lipid metabolism in mammalian tissues.

links, descriptions, video tutorials, and contact details for software developers. The new tool comprehensively covers the seven major areas in lipidomics data processing, as follows: (1) lipid-oriented databases; (2) MS data repositories; (3) analysis of targeted lipidomics datasets; (4) lipid identification and (5) quantification from untargeted lipidomics datasets; (6) statistical analysis and visualization; and (7) data integration solutions (Fig. 2). To support informed decision making by lipidomics analysts, for each software, a short description is provided, highlighting the main functionalities and the areas of applications, followed by the specific features listed under 'Technical information' and 'Task specific information' tabs (Figs. 3 and 4). Additionally, users can review simplified, tabular representations of the available functions for each tool in a given section by using the 'Tools Overview' tab.In the 'Technical information' section, users can view the type of the license under which the tool is distributed, the availability of desktop and/or web-based interfaces, data input/output formats, and compatibility with different operating systems (for example, Windows, Linux, macOS). There is also information accessible via clickable links, which allow the downloading of the tool together with related documentation, user guides, and training datasets. Additional fields list how to use the tool through the command line or via API interfaces for advanced users wishing to construct their own customized pipelines. 'Task specific information' tabs navigate users to pages describing functionalities of the software for particular tasks covering the seven areas outlined above (Figs. 1 and 2). Some comprehensive tools have multiple functions integrated into one combined package and can be configured for a wide range of workflows. These tools are assigned to each task with associated descriptions accordingly, and the list of tools is shown in Table 1. In the next section, we provide more details about each area and its associated software and tools.

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.

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 LipidFinders 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.