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variety of functional groups (Hider 1984), most of the discovered fungal siderophores belong to the hydroxamate classes (Renshaw et al. 2002). Nevertheless,
exceptions, namely, the siderophores rhizzoferrin and pistillarin that belong to the
carboxylate and catecholate groups, have also been discovered in certain fungal species (Haas 2014).
Almost all known fungal siderophores are synthesized by non-ribosomal peptide
synthetases (NRPSs) (Haas et al. 2008). NRPSs are backbone enzymes facilitating
the synthesis of secondary metabolites inclusive of compounds such as siderophores, pigments, toxins and antibiotics. These products are synthesized by a process independent of ribosomal machinery (Martínez-Núñez and López 2016).
Again, the carboxylate siderophore rhizzoferrin is an exception and is synthesized
in an NRPS-independent manner (Thieken and Winkelmann 1992). The ‘omics’ era
has revolutionized the impact of computational tools and bioinformatics techniques
on the discovery of secondary metabolites and their pre-cursors. In the past two
decades, advances driven by genomic and metabolomic analysis provide powerful
new methods to identify the occurrence of novel siderophores, their putative functions and biosynthetic pathways, albeit more in bacteria (Etchegaray et al. 2004).
The objective of this chapter is to provide a brief overview of current methodologies
and recent progress in the areas of bioinformatics related to the discovery and analysis of fungal siderophores using omics-based approaches.
10.2 Potential of Bioinformatics
The pace of research and development in the field of bioinformatics and computational biology skyrocketed with the advent of rapid genome sequencing techniques.
The potential of bioinformatics analysis lies in the fact that with proper analysis,
thousands of predictions can be made and tested for any organism. It can yield
insight into gene/protein functions as well as metabolism in a manner that dramatically reduces the time of testing when using only the conventional tools in vitro.
Additionally, with the large amount of data that is generated, there are bioinformatic
applications to store, retrieve, share and compare the data. However, it is important
to choose relevant data for analysis and hence to discriminate between meaningful
and noisy data (Quatrini et al. 2007).
The omics analysis primarily includes the use of computational algorithms to
predict genes, proteins and metabolic pathways, predominantly in sequenced
genomes as part of high-throughput protocols. The study of omics involves a global
assessment of a set of molecules such as the genome, proteome, transcriptome or
metabolome. The potential of any study is tremendously increased with a meticulous integration of multiple omics analyses rather than a single one (Hasin et al.
2017; Zhang et al. 2010).
D. Subramanian et al.
variety of functional groups (Hider 1984), most of the discovered fungal siderophores belong to the hydroxamate classes (Renshaw et al. 2002). Nevertheless,
exceptions, namely, the siderophores rhizzoferrin and pistillarin that belong to the
carboxylate and catecholate groups, have also been discovered in certain fungal species (Haas 2014).
Almost all known fungal siderophores are synthesized by non-ribosomal peptide
synthetases (NRPSs) (Haas et al. 2008). NRPSs are backbone enzymes facilitating
the synthesis of secondary metabolites inclusive of compounds such as siderophores, pigments, toxins and antibiotics. These products are synthesized by a process independent of ribosomal machinery (Martínez-Núñez and López 2016).
Again, the carboxylate siderophore rhizzoferrin is an exception and is synthesized
in an NRPS-independent manner (Thieken and Winkelmann 1992). The ‘omics’ era
has revolutionized the impact of computational tools and bioinformatics techniques
on the discovery of secondary metabolites and their pre-cursors. In the past two
decades, advances driven by genomic and metabolomic analysis provide powerful
new methods to identify the occurrence of novel siderophores, their putative functions and biosynthetic pathways, albeit more in bacteria (Etchegaray et al. 2004).
The objective of this chapter is to provide a brief overview of current methodologies
and recent progress in the areas of bioinformatics related to the discovery and analysis of fungal siderophores using omics-based approaches.
10.2 Potential of Bioinformatics
The pace of research and development in the field of bioinformatics and computational biology skyrocketed with the advent of rapid genome sequencing techniques.
The potential of bioinformatics analysis lies in the fact that with proper analysis,
thousands of predictions can be made and tested for any organism. It can yield
insight into gene/protein functions as well as metabolism in a manner that dramatically reduces the time of testing when using only the conventional tools in vitro.
Additionally, with the large amount of data that is generated, there are bioinformatic
applications to store, retrieve, share and compare the data. However, it is important
to choose relevant data for analysis and hence to discriminate between meaningful
and noisy data (Quatrini et al. 2007).
The omics analysis primarily includes the use of computational algorithms to
predict genes, proteins and metabolic pathways, predominantly in sequenced
genomes as part of high-throughput protocols. The study of omics involves a global
assessment of a set of molecules such as the genome, proteome, transcriptome or
metabolome. The potential of any study is tremendously increased with a meticulous integration of multiple omics analyses rather than a single one (Hasin et al.
2017; Zhang et al. 2010).
D. Subramanian et al.