Many bioinformatics approaches in genomics and proteomics aims to detect omics signatures, for instance the collection of genes that significantly change under a certain biological condition or that differ in case-control studies (Huang, Sherman, & Lempicki, 2009). Normally, such omic signatures need a secondary analysis in order to be understood in biological terms and linked to significant pathways (Chagoyen & Pazos, 2011). The methodology used in such cases is called functional enrichment analysis and, since it was originally proposed, a hundred of variations and different implementations have been developed. Nowadays, scientists in many omic fields make intensive use of enrichment analysis tools such as, to name a few, GSEA in genomics (Subramanian et al., 2005), MPEA (Kankainen, Gopalacharyulu, Holm, & Orešič, 2011) and MBRole (Chagoyen & Pazos, 2011) in metabolomics and, GeneTrail2 (Stöckel et al., 2016) in multiomics (transcriptomics, proteomics, miRNomics, genomics).
Lipidomics is an emerging field that aims at the large scale identification and quantification of diverse lipid repertoire in biologic systems that play critical roles in cellular functions (Gross & Holčapek, 2014). Although it is not the most developed omic field, its importance is increasing constantly over the years, particularly nowadays that absolute quantification methods by shotgun mass spectrometry are becoming widely available (Shevchenko & Simons, 2010). Therefore, we have developed and implemented a freely web platform called LIPEA (Lipid Pathway Enrichment Analysis) that can detect from a holistic point of view the pathways and categories that are significantly associated to the multiple lipids provided by the user.
Here, we introduce LIPEA algorithm, for functional analysis of lipids at the system level. LIPEA works with ID of lipid compounds contained in the Kyoto Encyclopedia of Genes and Genomes (KEGG Database; Ogata et al., 1999) and finds significantly perturbed pathways, applying statistical tests. LIPEA adopts the Fisher exact test, where the probability that the random event could happen is given by the hypergeometric distribution.