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Unravelling the Oil and Gas Microbiome Using Metagenomics
Published in Kenneth Wunch, Marko Stipaničev, Max Frenzel, Microbial Bioinformatics in the Oil and Gas Industry, 2021
Following assembly, the produced contigs can be grouped into draft genomes (MAGs) using metagenome binning (Kang et al., 2015). Binning methods can be either supervised where individual contigs are first assigned taxonomic information before grouping of those contigs with the same taxonomy, or unsupervised where statistical methods are applied on contigs to uncover similarities that would group contigs together (e.g. GC content, tetranucleotide frequency, etc.). Popular binning tools include MetaBAT2 (Kang et al., 2019), MaxBin 2.0 (Wu, Simmons & Singer, 2016), CONCOCT (Alneberg et al., 2014), and more recently VAMB a binning tool that uses deep learning (Nissen et al., 2018). To assess the quality of the produced MAGs, tools like CheckM (Parks et al., 2015) which use single-copy gene profiling to determine the completeness and contamination of each MAG can be used. In addition to CheckM, other post-binning polishing methods (Chen et al., 2020) and tools (Wang et al., 2017) can be deployed to further improve the quality, accuracy, and completeness of the produced MAGs.
Psychrophilic Microbiomes
Published in Ajar Nath Yadav, Ali Asghar Rastegari, Neelam Yadav, Microbiomes of Extreme Environments, 2021
Machine learning algorithms are used for pattern extraction, which are then further employed for classification/recognition tasks. These techniques help to mine biologically relevant knowledge. Broadly machine learning techniques can be applied in two modes: Supervised learning mode, in which the class labels are known and are used for training the learning algorithms for prediction purposes (Kotsiantis 2007; Witten et al. 2011). The other is the unsupervised learning mode which does not require class labels and aims to find the natural groupings present in the dataset (e.g., clustering algorithms) (Ghahramani 2004; Nath and Subbiah 2016c; Witten et al. 2011). The process of clustering reads from metagenomic data and allocating them to operational taxonomic units is called binning. Unsupervised machine learning algorithms are also used for binning of microbial genomes (Nissen et al. 2018). In case of psychrophilic proteins, machine learning algorithms can be trained to predict psychrophilic proteins from a pool of unknown proteins or for discrimination of psychrophilic proteins from mesophilic/thermophilic proteins. The discrimination of psychrophilic proteins from its mesophilic/thermophilic counterpart can be defined as a two class classification problem (also known as binary classification problem), in which the class of interest is the psychrophilic protein being the positive class and all other non-psychrophilic proteins being the negative class.
Omics Approach to Understanding Microbial Diversity
Published in Jyoti Ranjan Rout, Rout George Kerry, Abinash Dutta, Biotechnological Advances for Microbiology, Molecular Biology, and Nanotechnology, 2022
Shilpee Pal, Arijit Jana, Keshab Chandra Mondal, Suman Kumar Halder
In both approaches, identification and removal of low-quality sequences, as well as host genome sequences, are essential steps, which can be performed by applying computational tools, viz., FastQC (Andrews, 2014), Cutadapt (Martin, 2011) programs. After quality control, reads are assembled into long contiguous sequences called contigs. In case of taxonomic classification, binning is performed, in which every read is grouped into bins as per their taxon ID.
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
Metagenomics or environmental genomics is the genomic analysis of microorganisms in a microbial community that can provide insights into community physiology (Handelsman, 2004; Sharpton, 2014), and enables the discovery of new microbial taxa and genes without cultivation. The procedure involves extracting DNA from all cells in a community, shearing DNA into fragments, sequencing fragmented DNA (Handelsman, 2004; Tyson et al., 2004; Venter et al., 2004), assembling all sequences into an ecosystem genome comprised of many genomes of the innate microbial populations (‘metagenome’) (Handelsman, 2004), and phylogenetically classifying the genomic fragments to specific microorganisms (‘binning’) (McHardy, Martin, Tsirigos, Hugenholtz, & Rigoutsos, 2007). This approach has been greatly improved by using novel assemblers (e.g., metaSPAdes and MEGAHIT) (Li, Liu, Luo, Sadakane, & Lam, 2015; Namiki, Hachiya, Tanaka, & Sakakibara, 2012; Nurk, Meleshko, Korobeynikov, & Pevzner, 2017; Peng, Leung, Yiu, & Chin, 2012) and binning methods (McHardy et al., 2007; Pati, Heath, Kyrpides, & Ivanova, 2011; Patil, Roune, & McHardy, 2012; Wu, Tang, Tringe, Simmons, & Singer, 2014) together with software that integrate information from essential single-copy genes (e.g., MaxBin) and multiple metagenomes of related samples (e.g., MetaBAT and GroopM) (Albertsen et al., 2013; Imelfort et al., 2014; Kang, Froula, Egan, & Wang, 2015; Wu, Simmons, & Singer, 2016). Researchers can now determine individual bins’ phylogeny (‘phylogenomics’) using software such as PhyloPhlAn (Segata, Bornigen, Morgan, & Huttenhower, 2013) and genome completeness/contamination with marker genes using software such as CheckM (Parks, Imelfort, Skennerton, Hugenholtz, & Tyson, 2015). These advancements enable accurate metagenomic assembly, binning, and recovery of genomes for phylogenetically novel organisms without cultivating them (Kang et al., 2015; Wrighton et al., 2012; Wu et al., 2014).