significantly from the data generated by metagenomics and metatranscriptomics
(Aguiar-Pulido et al. 2016). Metabolomics identified more than 4776 metabolites
for bioremediation of different sites contaminated with petroleum hydrocarbons
(Bargiela et al. 2015a; Malla et al. 2018; Dong et al. 2019). Biodegradation potential
of the bacterial population in contaminated crude oil (Bargiela et al. 2015b), and
anaerobic biodegradation of hydrocarbons (Gieg and Toth 2016) were also studied
and analyzed using metabolomics.
For accessing biodegradative genes from metagenome of polluted environment,
various metagenomic strategies, such as function-based screening and sequencebased screening, have been implemented (Pushpanathan et al. 2014). Metagenomic
approaches for bioremediation have identified hydrocarbon-degrading genes of
Pseudomonas sp. and Rhodococcus sp. in Arctic soil of Canada (Yergeau et al.
2012). Williams et al. (2014) identified Flavobacterium sp., Enterobacter cloacae,
and Ralstonia sp., through metagenomic techniques and, used them for bioremediation of chromium (VI) present in groundwater. Several eco-friendly and costeffective methods have been explored for arsenic contaminated water. In a study,
Ma et al. (2016) investigated the capacity of rhizosphere microorganisms to enhance
phytoremediation of arsenic-contaminated environments. The scalability of
rhizoremediation in arsenic contamination is known very little. Therefore, several
environmental genomic studies have been carried out to study ecosystems contaminated with arsenic (Huang et al. 2016). Also, the involvement of molecular mechanisms has been investigated in detail (Andres and Bertin 2016). The metagenomic
analysis identified HL18 as the highest occurring methanotroph in the community
for reduction of mercury (Hg) and arsenic (As), revealing all the genes necessary for
the reduction of Hg (II) and As (V).
Metagenomic approaches aided in the identification of multiple enzymes that
have the capability to degrade pollutants, like insecticides, dyes, pesticides, and
plastics, using their bioremediation properties (Ufarté et al. 2015). In the degradation
of oil spills in marine environments, biosurfactants and biosurfactant-producing
strains of microorganisms have been widely used. Metagenomic analysis has led
to the discovery of palmitoyl putrescine and N-acyl amino acids; the two novel
biosurfactants (Jackson et al. 2015; Williams and Trindade 2017). MetaBoot, a
software using a machine learning framework, holds the potential to identify pollution biomarkers based on metagenomic datasets obtained from contaminated ecosystems (Wang et al. 2015). Portable sequencing platforms, like Oxford Nanopore
sequencers, have been developed to overcome the tediousness of transportation of
samples for pollutant and pathogen monitoring (Oulas et al. 2015). The sequencing
data storage, processing, and technologies associated with metagenomics, and also
the pipelines of metagenomics have been improved, which have made it costefficient (van Dijk et al. 2018).
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A. Murmu and M. Sevanan
(Aguiar-Pulido et al. 2016). Metabolomics identified more than 4776 metabolites
for bioremediation of different sites contaminated with petroleum hydrocarbons
(Bargiela et al. 2015a; Malla et al. 2018; Dong et al. 2019). Biodegradation potential
of the bacterial population in contaminated crude oil (Bargiela et al. 2015b), and
anaerobic biodegradation of hydrocarbons (Gieg and Toth 2016) were also studied
and analyzed using metabolomics.
For accessing biodegradative genes from metagenome of polluted environment,
various metagenomic strategies, such as function-based screening and sequencebased screening, have been implemented (Pushpanathan et al. 2014). Metagenomic
approaches for bioremediation have identified hydrocarbon-degrading genes of
Pseudomonas sp. and Rhodococcus sp. in Arctic soil of Canada (Yergeau et al.
2012). Williams et al. (2014) identified Flavobacterium sp., Enterobacter cloacae,
and Ralstonia sp., through metagenomic techniques and, used them for bioremediation of chromium (VI) present in groundwater. Several eco-friendly and costeffective methods have been explored for arsenic contaminated water. In a study,
Ma et al. (2016) investigated the capacity of rhizosphere microorganisms to enhance
phytoremediation of arsenic-contaminated environments. The scalability of
rhizoremediation in arsenic contamination is known very little. Therefore, several
environmental genomic studies have been carried out to study ecosystems contaminated with arsenic (Huang et al. 2016). Also, the involvement of molecular mechanisms has been investigated in detail (Andres and Bertin 2016). The metagenomic
analysis identified HL18 as the highest occurring methanotroph in the community
for reduction of mercury (Hg) and arsenic (As), revealing all the genes necessary for
the reduction of Hg (II) and As (V).
Metagenomic approaches aided in the identification of multiple enzymes that
have the capability to degrade pollutants, like insecticides, dyes, pesticides, and
plastics, using their bioremediation properties (Ufarté et al. 2015). In the degradation
of oil spills in marine environments, biosurfactants and biosurfactant-producing
strains of microorganisms have been widely used. Metagenomic analysis has led
to the discovery of palmitoyl putrescine and N-acyl amino acids; the two novel
biosurfactants (Jackson et al. 2015; Williams and Trindade 2017). MetaBoot, a
software using a machine learning framework, holds the potential to identify pollution biomarkers based on metagenomic datasets obtained from contaminated ecosystems (Wang et al. 2015). Portable sequencing platforms, like Oxford Nanopore
sequencers, have been developed to overcome the tediousness of transportation of
samples for pollutant and pathogen monitoring (Oulas et al. 2015). The sequencing
data storage, processing, and technologies associated with metagenomics, and also
the pipelines of metagenomics have been improved, which have made it costefficient (van Dijk et al. 2018).
230
A. Murmu and M. Sevanan