76
(MSEA) (Xia and Wishart 2011) based on gene set enrichment analysis (GSEA)
can be used in the analysis of gene expression datasets. MSEA is used to investigate
the enrichment of predefined groups of related metabolites instead of individual
metabolites. MSEA starts with a list of metabolites that have been extracted from
the experimental data through statistical approaches such as ANOVA, cluster analysis, PCA, or PLS-DA. Subsequently, overrepresentation of metabolites from a
predefined metabolite set is checked by hypergeometric test (Vaz et al. 2015).
Similar other metabolite enrichment softwares are available such as MetaGeneAlyse,
MetaboAnalyst, MetaMapR, and MPEA (Fukushima and Kusano 2013). The
metabolite sets can be constructed according to the desired aim. The construction
of correlation networks from metabolic profiles to identify metabolites that are
regulated and co-regulated across the conditions can be measured. The alteration of
metabolic fluxes in a network can also be pursued for investigating metabolic adaptations at the molecular level (Han et al. 2012; Vaz et al. 2015; Wenk 2010).
However, managing and organizing lipid-related pathways into useful, interactive pathways and networks present a challenge for lipid bioinformatics. Lipidomic
data-based pathways and/or network reconstruction analysis mainly relies upon
The KEGG, LIPID MAPS consortium, and SphingoMAP databases (Table 4.4).
The KEGG PATHWAY database offers information on most of the metabolic pathways including lipid pathways encompassing FA biosynthesis, FA elongation, FA
metabolism, steroid biosynthesis, glycerolipid, glycerophospholipid, ether lipid,
sphingolipid, AA, LA, ALA metabolism, and biosynthesis of unsaturated FAs.
Additionally, KEGG also provides generic pathways (i.e., species-independent
pathways) to serve as reference pathways for the reconstruction of context- or
organism-specific pathways. The KEGG BRITE (http://www.genome.jp/kegg/
brite.html) specifically maintains a collection of hierarchical classifications of lipid
species whose reactions and pathways can be viewed. LIPID MAPS biopathway
workbench (http://www.biopathwaysworkbench.org/) provides a graphic tool that
facilitates to display, edit, and analyze biochemical pathways of lipids. Open-source
visualization tools such as BioCyc (www.biocyc.org), CYTOSCAPE (http://cytoscape.org), LipidBANK (http://lipidbank.jp/), MAPPFinder (www.genmapp.org/
help_v2/UsingMAPPFinder.htm), MetaCyc (http://metacyc.org/), MarVis-Pathway
(http://marvis.gobics.de), PANTHER (www.pantherdb.org), Pathway Editor (www.
lipidmaps.org/pathways/pathwayeditor.html), Pathway-Express (http://vortex.cs.
wayne.edu/projects.htm), and VANTED (www.vanted.org) enable retrieval and
visualization and editing of lipid signaling and metabolic pathways (Table 4.5).
4.5 Application of Lipidomics to the Seaweed Systems
Biology
Systems biology is rather a new frontier to explore in seaweed biology with the
availability of whole genomes of Ectocarpus, Chondrus, Pyropia, and Saccharina,
which are promising existing and future models for understanding seaweed
P. Kumari
(MSEA) (Xia and Wishart 2011) based on gene set enrichment analysis (GSEA)
can be used in the analysis of gene expression datasets. MSEA is used to investigate
the enrichment of predefined groups of related metabolites instead of individual
metabolites. MSEA starts with a list of metabolites that have been extracted from
the experimental data through statistical approaches such as ANOVA, cluster analysis, PCA, or PLS-DA. Subsequently, overrepresentation of metabolites from a
predefined metabolite set is checked by hypergeometric test (Vaz et al. 2015).
Similar other metabolite enrichment softwares are available such as MetaGeneAlyse,
MetaboAnalyst, MetaMapR, and MPEA (Fukushima and Kusano 2013). The
metabolite sets can be constructed according to the desired aim. The construction
of correlation networks from metabolic profiles to identify metabolites that are
regulated and co-regulated across the conditions can be measured. The alteration of
metabolic fluxes in a network can also be pursued for investigating metabolic adaptations at the molecular level (Han et al. 2012; Vaz et al. 2015; Wenk 2010).
However, managing and organizing lipid-related pathways into useful, interactive pathways and networks present a challenge for lipid bioinformatics. Lipidomic
data-based pathways and/or network reconstruction analysis mainly relies upon
The KEGG, LIPID MAPS consortium, and SphingoMAP databases (Table 4.4).
The KEGG PATHWAY database offers information on most of the metabolic pathways including lipid pathways encompassing FA biosynthesis, FA elongation, FA
metabolism, steroid biosynthesis, glycerolipid, glycerophospholipid, ether lipid,
sphingolipid, AA, LA, ALA metabolism, and biosynthesis of unsaturated FAs.
Additionally, KEGG also provides generic pathways (i.e., species-independent
pathways) to serve as reference pathways for the reconstruction of context- or
organism-specific pathways. The KEGG BRITE (http://www.genome.jp/kegg/
brite.html) specifically maintains a collection of hierarchical classifications of lipid
species whose reactions and pathways can be viewed. LIPID MAPS biopathway
workbench (http://www.biopathwaysworkbench.org/) provides a graphic tool that
facilitates to display, edit, and analyze biochemical pathways of lipids. Open-source
visualization tools such as BioCyc (www.biocyc.org), CYTOSCAPE (http://cytoscape.org), LipidBANK (http://lipidbank.jp/), MAPPFinder (www.genmapp.org/
help_v2/UsingMAPPFinder.htm), MetaCyc (http://metacyc.org/), MarVis-Pathway
(http://marvis.gobics.de), PANTHER (www.pantherdb.org), Pathway Editor (www.
lipidmaps.org/pathways/pathwayeditor.html), Pathway-Express (http://vortex.cs.
wayne.edu/projects.htm), and VANTED (www.vanted.org) enable retrieval and
visualization and editing of lipid signaling and metabolic pathways (Table 4.5).
4.5 Application of Lipidomics to the Seaweed Systems
Biology
Systems biology is rather a new frontier to explore in seaweed biology with the
availability of whole genomes of Ectocarpus, Chondrus, Pyropia, and Saccharina,
which are promising existing and future models for understanding seaweed
P. Kumari