Computational meta'omics for microbial community studies

By Nicola Segata, D. Boernigen, Timothy L. Tickle, Xochitl C. Morgan, W. Garrett, Curtis Huttenhower. Molecular Systems Biology, 2013

326 citations recorded by the source corpus.

Complex microbial communities are an integral part of the Earth's ecosystem and of our bodies in health and disease. In the last two decades, culture‐independent approaches have provided new insights into their structure and function, with the exponentially decreasing cost of high‐throughput sequencing resulting in broadly available tools for microbial surveys. However, the field remains far from reaching a technological plateau, as both computational techniques and nucleotide sequencing platforms for microbial genomic and transcriptional content continue to improve. Current microbiome analyses are thus starting to adopt multiple and complementary meta'omic approaches, leading to unprecedented opportunities to comprehensively and accurately characterize microbial communities and their interactions with their environments and hosts. This diversity of available assays, analysis methods, and public data is in turn beginning to enable microbiome‐based predictive and modeling tools. We thus review here the technological and computational meta'omics approaches that are already available, those that are under active development, their success in biological discovery, and several outstanding challenges.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/08f52f944c5d30f9203b79d6490c117eab24ac76

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Citations in the corpus

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2020, background): “…Project Consortium, 2012) , shotgun metagenomics provide a complementary means of taxonomic profiling to amplicon-based (e.g. 16S rRNA gene) sequencing, as well as functional profiling of genes or biochemical pathways (Morgan et al., 2013; Quince et al., 2017; Segata et al., 2013) .”
Current knowledge of interactions between Drosophila suzukii and microbes, and their potential utility for pest management (2016, methodology): “Targeted rRNA gene sequencing of 16S for bacteria, 18S for eukaryotes, and the internal transcribed spacer region for fungi has been used to profile microbial communities (Bokulich and Mills 2013; Segata et al. 2013).”
The Potential of Metagenomic Approaches for Understanding Soil Microbial Processes (2014, methodology): “A variety of analytical methods have been used, and are being developed, to provide insights into these complex systems and their emergent properties, such as clustering, ordination, and artificial neural networks, to name a few (Cloots and Marchal, 2011; Faust and Raes, 2012; Gianoulis et al., 2009”
Fast and sensitive taxonomic classification for metagenomics with Kaiju (2016, methodology): “This ‘metagenomic’ approach has become a standard method for characterizing the biodiversity, gene contents and metabolic processes of bacterial and archaeal communities and is used on increasingly larger scales 1–3 .”
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible (2013, methodology): “In both simulation types we varied the library and effect sizes across a range of levels that are relevant for recently-published microbiome investigations, and followed with commonly used statistical analyses from the microbiome and/or RNA-Seq literature (Figure 2).”
PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes (2013, methodology): “Variations on this method have already been useful for efficient and high-throughput taxonomic assessment of whole uncultured communities 52 and can be crucial for mining meta’omic datasets 53 .”
The ecologist's field guide to sequence‐based identification of biodiversity (2016, background): “If we can comprehensively characterize entire communities and their biotic/ abiotic interactions, we will be in a position to develop the necessary modelling tools required to make systems ecology predictions associated with change (Segata et al. 2013).”
RefSeq microbial genomes database: new representation and annotation strategy (2013, background): “A metagenome assembly usually represents not a single organism but rather the composition of a bacterial population (10).”
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights (2016, background): “population of study, sample collection approach, DNA extraction protocol, sequencing technology, and analysis strategy [19].”
Metabarcoding: A powerful tool to investigate microbial communities and shape future plant protection strategies (2017, background): “…soil amendments to suppress apple replant disease (Mazzola et al., 2014), the use of biofertilizers to suppress Fusarium wilt disease in banana (Shen et al., 2015), the use of almond shells to promote the suppression of Rosellinia necatrix (Vida et al., 2016), and use of several other composts…”