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Role of Genomics, Metagenomics, and Other Meta-Omics Approaches for Expunging the Environmental Contaminants by Bioremediation
Published in Vineet Kumar, Vinod Kumar Garg, Sunil Kumar, Jayanta Kumar Biswas, Omics for Environmental Engineering and Microbiology Systems, 2023
Atif Khurshid Wani, Daljeet Singh Dhanjal, Nahid Akhtar, Chirag Chopra, Abhineet Goyal, Reena Singh
MG-RAST is an automated open-source tool for the easy phylogenetic and reliable, functional analysis of the metagenomes. This is among the broad metagenomic data repositories and is used in comparing sequences with nucleotide and amino acid databases. Thus, MG-RAST offers comparative analysis, quality control, archiving services, and annotation of amplicon sequences and metagenomics with other computational tools (Kumavath and Deverapalli, 2013). Besides analyzing metagenomic data, it also supports the sequence processing of meta-transcriptomes and amplicons. The pipeline is divided into five levels: data hygiene, feature extraction, feature annotation, profile generation, and data loading (Keegan et al., 2016). The pipeline is applied in Perl by utilizing multiple open-source tools such as SEED, NCBI BLAST, Sun Grid Engine, and SQLite. The users can upload raw sequences in FASTA format, leading to sequence normalization, processing, and finally, the automatic generation of summaries (Kisand et al., 2012). Jung et al. used MG-RAST for the metagenomic functional analysis of bacterial communities in hydrocarbon-contaminated sites for understanding bioremediation. The calculated alpha diversity by MG-RAST showed that, in the control treatment, the 10−2-inoculated samples contained higher OTUs than the 10−5 inoculated samples(Jung et al., 2016). This tool was created by Argonne National Laboratory of Chicago University and can be accessed at http://metagenomics.anl.gov/.
MIC Detection and Assessment
Published in Torben Lund Skovhus, Dennis Enning, Jason S. Lee, Microbiologically Influenced Corrosion in the Upstream Oil and Gas Industry, 2017
Mohita Sharma, Gerrit Voordouw
Syntrophy of methanogens, acetogens, sulfate reducers, and other bacteria involves interspecies electron transfer or successive metabolism of compounds, neither of which can be metabolized in isolation (Usher et al. 2015). This allows microorganisms to grow under thermodynamically limiting conditions. Recycling of carbon, sulfur, and iron can occur between members of microbial communities. Ramos-Padron et al. (2011) found carbon and sulfur cycling by microbial communities as a function of depth in oil sand tailing ponds routinely treated with gypsum. Multispecies biofilms develop as a result of syntrophic interactions, leading to a cascade of biochemical reactions between species, which can accelerate corrosion (Kip and Veen 2015 and references therein). Usher et al. (2015) demonstrated such syntrophy in corrosion with metallic iron as the sole electron donor under nutrient-limiting conditions. Syntrophic microorganisms from groundwater and soil increased the corrosion rate of mild steel by 45.5% and 41.9%, respectively, as compared to sterile controls. Siderite (FeCO3) and magnetite (Fe3O4) iron (II) compounds were identified using XRD and Raman spectroscopy as components of the microbially produced corrosion products. These contributed to iron cycling by direct interspecies electron transfer. Siderite and magnetite are formed by SRB by direct enzymatic reduction of Fe3+, creating galvanic cells on the surface of metals and accelerating corrosion. Syntrophy of manganese and iron oxidizers with SRB allows the former to create oxygen-depleted environments in which the latter can proliferate. Syntrophic community members are often difficult to culture, and hence, 16S rRNA gene–based metagenomic studies can help in understanding their role for better design of corrosion-mitigation strategies (Park et al. 2011). This involves use of ribosomal RNA gene sequence analysis pipeline tools (Soh et al. 2013), metagenome rapid annotation using subsystem technology (MG-RAST, Glass et al. 2010), mothur (Schloss et al. 2009), or quantitative insights into microbial ecology (QIIME, Kuczynski et al. 2012), which all allow handling of the large data sets obtained.
Microbial diversity analysis of wood degrading microbiome and screening of natural consortia for bioalcohol production
Published in Biofuels, 2021
Mary Sanitha, Anwar Aliya Fathima, Mohandass Ramya
In this era, where fossil fuels get depleted at a faster rate, lignocellulosic substrates play a potent role for biofuel applications. Wood is the most abundant lignocellulosic biomass comprising of three major components: Cellulose, hemicellulose and lignin. Cellulose is the principal component of all types of wood and wood pulp [1]. The presence of lignin forming a matrix with cellulose and hemicellulose restricts the microorganisms to utilise the fermentable sugars in wood biomass. A wide range of fungi has been reported with the ability to degrade the polymers of wood biomass including recalcitrant lignin [2]. However, many cellulolytic bacteria present in the degrading wood consortia contribute largely by acting synergistically with fungi to degrade wood biomass or utilise lignin-derived aromatic compounds [3, 4]. Next-generation sequencing (NGS) was combined with metagenomics to determine taxonomy and functions of various culturable organisms in the wood degrading microbiome. This helps us to understand the bacterial community. Bioinformatics pipeline Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST) enables us to analyse NGS data and allows users to upload metagenomes for automated analyses [5].
The application of molecular tools to study the drinking water microbiome – Current understanding and future needs
Published in Critical Reviews in Environmental Science and Technology, 2019
The advance in NGS technology in the last decade serves as the pivotal force to the development of omics tools. The Sanger method is considered as a ‘first-generation’ technology, and newer methods using new sequencing chemistry are referred to as NGS, which includes ‘454’, ‘Illumina’, PacBio SMRT and the Oxford Nanopore MinION. ‘454’ and Illumina platforms can produce a large number of sequences that are low in cost, high in throughput and accuracy, and short in run times (Metzker, 2010). They produce short reads (Ross et al., 2013), which makes downstream bioinformatics analyses difficult. For this reason, technologies such as PacBio SMRT and the Oxford Nanopore MinION were developed to produce long reads (>5 kb) but at a relatively higher error rate (12–14% for PacBio and 8% for Nanopore) than Illumina platforms (Feng et al., 2015; Jain et al., 2016; Roberts et al., 2013). To take advantage of the strength of both short- and long-read technologies, hybrid de novo assembly at the chromosome-level is performed using long-read technologies with sequencing errors corrected by Illumina data (Goodwin et al., 2015; Risse et al., 2015). These NGS technologies has enabled the development of metagenomics and metatranscriptomics to study microbial functions and activities in various microbial ecosystems (Béjà et al., 2000; Giannoukos et al., 2012; Tyson et al., 2004; Urich et al., 2008; Venter et al., 2004; Xiong et al., 2012). NGS platforms generate millions or billions of reads in parallel, which require multiple bioinformatic steps, such as assembly, binning, mapping and quantification to convert into useful information for researchers. While dealing with bioinformatic software is challenging, especially with integrated data from metagenomics, metatranscriptomics, and proteomics, several web-based pipelines are accessible to scientists, including MG-RAST (Meyer et al., 2008), KBase (Arkin et al., 2016), CyVerse/iPlant Discovery Environment (Goff et al., 2011), IMG-ER (Markowitz et al., 2012), and PATRIC (Wattam et al., 2017).