165
Almost all the above implementations are aiming at the study of NPs in general.
However, extension of MS/MS-based approaches to more specific compounds can
happen in the near future. For the characterization of siderophores, in particular, a
workflow based on high-resolution liquid chromatography (LC)-MS/MS was developed (Baars and Perlman 2016). The workflow mines LC-MS/MS data making use
of a database of siderophore structures. An MS/MS auto-convolution technique is
used with MS/MS siderophore networks to discover peptide monomers present in
the siderophores under study. Corresponding families are thus identified, which is
important in assigning structures to the siderophores by spectral reconstruction.
A peptidogenomics approach was established to mine organisms for novel
metabolites establishing a field of research known as natural product peptidogenomics (Kersten et al. 2011). It uses MS/MS to get an amino acid sequence tag
representing a part of the complete peptide and hence can be deduced based on the
mass shift pattern. Subsequently, screening against predicted specificities of substrates is conducted. To automate the process and enable high-throughput detection
of metabolites, it is necessary to match the identified mass spectra to the BGCs
efficiently, both in terms of accuracy as well as time. The software package Pep2Path
was introduced to address this automation problem and found to be paving the way
towards high-throughput identification of peptide NPs. It works based on a Bayesian
probabilistic approach for rapid matching of spectra to the clusters (Medema
et al. 2014).
Parallel to the development of MS-based approaches, improvements in nuclear
magnetic resonance (NMR) techniques are catching up with the objective of highthroughput detection of NPs. Introduction of miniaturized and cryogenic NMR
probes, data-mining techniques and database management are narrowing down the
gap between NMR and NP discovery (Halabalaki et al. 2014).
Similar to the widely used proteomics-based approaches, transcriptomics is also
used to identify BGCs and particularly the conditions under which it is activated for
NP production (Wang et al. 2015). RNA-seq is an efficient alternative to the classical microarray technique with its lowering costs, improvised technology and
sequence-agnostic nature. For instance, investigations after exposing the fungi
Aspergillus niger, Penicillium chrysogenum and Trichoderma reesei to competitive,
co-cultured environments were found to be activating novel BGCs of uncharacterized NPs (Daly et al. 2017). Akin unexplored potentials of transcriptomics can be
used in the identification of siderophores also. The tool FunGeneClusterS was
developed for the identification of fungal BGCs and utilizes both genomic and transcriptomic data unlike the previously mentioned tools in Sect. 10.3.2 using genome
sequence data alone (Vesth et al. 2016)
At the end of the omics string, metabolomics is a tool connecting NPs detected
by MS to their corresponding BGCs. MS-based metabolomic approaches, largely
overlapping the proteomic techniques, have helped to connect the missing links in
the whole genome potential for the secondary metabolite production of an organism. The technique helps to provide clarity on the comparatively lower numbers of
known compounds from the organism (Krug and Müller 2014). Multiple omics
techniques of different NPs produced in an organism are studied together for the
10 Bioinformatics Applications in Fungal Siderophores: Omics Implications
Almost all the above implementations are aiming at the study of NPs in general.
However, extension of MS/MS-based approaches to more specific compounds can
happen in the near future. For the characterization of siderophores, in particular, a
workflow based on high-resolution liquid chromatography (LC)-MS/MS was developed (Baars and Perlman 2016). The workflow mines LC-MS/MS data making use
of a database of siderophore structures. An MS/MS auto-convolution technique is
used with MS/MS siderophore networks to discover peptide monomers present in
the siderophores under study. Corresponding families are thus identified, which is
important in assigning structures to the siderophores by spectral reconstruction.
A peptidogenomics approach was established to mine organisms for novel
metabolites establishing a field of research known as natural product peptidogenomics (Kersten et al. 2011). It uses MS/MS to get an amino acid sequence tag
representing a part of the complete peptide and hence can be deduced based on the
mass shift pattern. Subsequently, screening against predicted specificities of substrates is conducted. To automate the process and enable high-throughput detection
of metabolites, it is necessary to match the identified mass spectra to the BGCs
efficiently, both in terms of accuracy as well as time. The software package Pep2Path
was introduced to address this automation problem and found to be paving the way
towards high-throughput identification of peptide NPs. It works based on a Bayesian
probabilistic approach for rapid matching of spectra to the clusters (Medema
et al. 2014).
Parallel to the development of MS-based approaches, improvements in nuclear
magnetic resonance (NMR) techniques are catching up with the objective of highthroughput detection of NPs. Introduction of miniaturized and cryogenic NMR
probes, data-mining techniques and database management are narrowing down the
gap between NMR and NP discovery (Halabalaki et al. 2014).
Similar to the widely used proteomics-based approaches, transcriptomics is also
used to identify BGCs and particularly the conditions under which it is activated for
NP production (Wang et al. 2015). RNA-seq is an efficient alternative to the classical microarray technique with its lowering costs, improvised technology and
sequence-agnostic nature. For instance, investigations after exposing the fungi
Aspergillus niger, Penicillium chrysogenum and Trichoderma reesei to competitive,
co-cultured environments were found to be activating novel BGCs of uncharacterized NPs (Daly et al. 2017). Akin unexplored potentials of transcriptomics can be
used in the identification of siderophores also. The tool FunGeneClusterS was
developed for the identification of fungal BGCs and utilizes both genomic and transcriptomic data unlike the previously mentioned tools in Sect. 10.3.2 using genome
sequence data alone (Vesth et al. 2016)
At the end of the omics string, metabolomics is a tool connecting NPs detected
by MS to their corresponding BGCs. MS-based metabolomic approaches, largely
overlapping the proteomic techniques, have helped to connect the missing links in
the whole genome potential for the secondary metabolite production of an organism. The technique helps to provide clarity on the comparatively lower numbers of
known compounds from the organism (Krug and Müller 2014). Multiple omics
techniques of different NPs produced in an organism are studied together for the
10 Bioinformatics Applications in Fungal Siderophores: Omics Implications