167
metabolism of nine species of Penicillium was studied to identify thousands of gene
clusters with immense potential (Nielsen et al. 2017).
With the sequence data expanding at a tremendous rate and new omics tools that
are efficient and inexpensive to detect and analyse NP synthesis, systematic pipelines integrating omics studies are being developed using bioinformatics. Genome
sequencing and mining are followed by detection and quantification of metabolites/
transcripts or proteins after which products of interest are screened for biological
activity (Hillman et al. 2017). QuantFung project makes use of similar ideologies
for the production of novel bio-active compounds in fungi (Büttel et al. 2015). The
utility of computational biology tools in NP detection in fungi are mainly concentrated on potential drug discovery. However, they could be adapted to siderophore
identification and research as well with ease.
10.4 Conclusion
With the advent of genome sequencing technologies and concurrent omics analysis,
there has been a vast increase in our knowledge of siderophore biosynthesis over the
past two decades. The first step is the identification of gene clusters acting as the
source of siderophores, which can be realized via bioinformatics too. Prediction of
substrate specificity and combining the known information with algorithms parsing
metabolomic-data to link the clusters to the corresponding compounds constitute
the following steps. For each of the steps, multiple new techniques were developed
in the last few years. In silico genome mining is an efficient high-throughput
approach to uncover potential NRPS genes. As described in previous sections, analytical pipelines linking genomics with other omics data are being developed and
can reveal immense information on the synthesis of such natural products.
Additionally, with the advent of computational tools to mine the genome, the identification of BGCs largely surpasses their characterization. Nevertheless, emerging
techniques of automated synthetic biology are hopeful of accomplishing such
objectives.
Natural product research, as well as siderophore research, has been concentrated
on bacterial species and there is an obvious bias in data availability as well as algorithm development for fungal research. Hence, it is important to consider the differences and test the relevance of already developed tools on fungal data before blind
usage. To reorganize the use for fungal siderophore identification, it is essential to
generate, collect and analyse fungal NRPS data – particularly siderophore- producing
ones. The lack of such curated data is currently a shortcoming in developing and
training prediction/classification models for fungal siderophores. It can be envisioned that algorithms for identification of siderophore-producing BGCs integrated
with high-throughput proteomic and metabolomic product detection techniques can
lead to the discovery and characterization of novel siderophores with novel biological significances.
10 Bioinformatics Applications in Fungal Siderophores: Omics Implications
metabolism of nine species of Penicillium was studied to identify thousands of gene
clusters with immense potential (Nielsen et al. 2017).
With the sequence data expanding at a tremendous rate and new omics tools that
are efficient and inexpensive to detect and analyse NP synthesis, systematic pipelines integrating omics studies are being developed using bioinformatics. Genome
sequencing and mining are followed by detection and quantification of metabolites/
transcripts or proteins after which products of interest are screened for biological
activity (Hillman et al. 2017). QuantFung project makes use of similar ideologies
for the production of novel bio-active compounds in fungi (Büttel et al. 2015). The
utility of computational biology tools in NP detection in fungi are mainly concentrated on potential drug discovery. However, they could be adapted to siderophore
identification and research as well with ease.
10.4 Conclusion
With the advent of genome sequencing technologies and concurrent omics analysis,
there has been a vast increase in our knowledge of siderophore biosynthesis over the
past two decades. The first step is the identification of gene clusters acting as the
source of siderophores, which can be realized via bioinformatics too. Prediction of
substrate specificity and combining the known information with algorithms parsing
metabolomic-data to link the clusters to the corresponding compounds constitute
the following steps. For each of the steps, multiple new techniques were developed
in the last few years. In silico genome mining is an efficient high-throughput
approach to uncover potential NRPS genes. As described in previous sections, analytical pipelines linking genomics with other omics data are being developed and
can reveal immense information on the synthesis of such natural products.
Additionally, with the advent of computational tools to mine the genome, the identification of BGCs largely surpasses their characterization. Nevertheless, emerging
techniques of automated synthetic biology are hopeful of accomplishing such
objectives.
Natural product research, as well as siderophore research, has been concentrated
on bacterial species and there is an obvious bias in data availability as well as algorithm development for fungal research. Hence, it is important to consider the differences and test the relevance of already developed tools on fungal data before blind
usage. To reorganize the use for fungal siderophore identification, it is essential to
generate, collect and analyse fungal NRPS data – particularly siderophore- producing
ones. The lack of such curated data is currently a shortcoming in developing and
training prediction/classification models for fungal siderophores. It can be envisioned that algorithms for identification of siderophore-producing BGCs integrated
with high-throughput proteomic and metabolomic product detection techniques can
lead to the discovery and characterization of novel siderophores with novel biological significances.
10 Bioinformatics Applications in Fungal Siderophores: Omics Implications