A Primer on Metagenomics

By John C. Wooley, Adam Godzik, Iddo Friedberg. PLoS Comput. Biol., 2010

726 citations recorded by the source corpus.

Metagenomics is a discipline that enables the genomic study of uncultured microorganisms. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats are expanding and transforming our view of the microbial world. Distilling meaningful information from the millions of new genomic sequences presents a serious challenge to bioinformaticians. In cultured microbes, the genomic data come from a single clone, making sequence assembly and annotation tractable. In metagenomics, the data come from heterogeneous microbial communities, sometimes containing more than 10,000 species, with the sequence data being noisy and partial. From sampling, to assembly, to gene calling and function prediction, bioinformatics faces new demands in interpreting voluminous, noisy, and often partial sequence data. Although metagenomics is a relative newcomer to science, the past few years have seen an explosion in computational methods applied to metagenomic-based research. It is therefore not within the scope of this article to provide an exhaustive review. Rather, we provide here a concise yet comprehensive introduction to the current computational requirements presented by metagenomics, and review the recent progress made. We also note whether there is software that implements any of the methods presented here, and briefly review its utility. Nevertheless, it would be useful if readers of this article would avail themselves of the comment section provided by this journal, and relate their own experiences. Finally, the last section of this article provides a few representative studies illustrating different facets of recent scientific discoveries made using metagenomics.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6c2b30e91795ea9dd444a0d87aee0ad8e0012d68

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Citations in the corpus

The impact of next-generation sequencing on genomics (2011, background): “Currently de novo assembly with NGS data is generally limited to microbial genome projects (Metagenomics) due to the small bacterial genome size (Chistoserdova, 2010; Wooley et al., 2010).”
Fast Identification and Removal of Sequence Contamination from Genomic and Metagenomic Datasets (2011, background): “several years from its origins in environmental microbiology [1–6].”
Experimental analysis of new algorithms for learning ternary classifiers (2015, background): “) data [19], [20], [21] have increased exponentially and in the future medicine will more and more rely on the use of such data to provide personalized medicine [20].”
Microbial Diversity in Caves (2016, methodology): “great popularity was gained by pyrosequencing of amplicons generated by PCR using total DNA collected from a given environment (Wooley et al. 2010).”
Biological aspects of slow sand filtration: past, present and future (2011, background): “More importantly, metagenomics allows scientists to glimpse into fully functional, microbial communities, allowing observations of how individuals interact by exchanging nutrients, metabolites and signaling molecules (Wooley et al., 2010).”
Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource (2010, background): “The data from complexity community sometimes contain more than 10 000 species, but the sequencing reads are sometimes partial fragments of the genomes and low quality reads (2).”
Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data (2012, background): “In just a few years, much work has been done to optimize the processes for collecting microbiome samples, processing the DNA, running the sequencing technology, and generating taxonomies/ phylogenies from these sequences [3].”
DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection (2015, background): “A sparsity of known microbial genomes and genes is the primary bottleneck, results in that a large number of fragments of metagenomic data can ’ t map to the known database [25, 26]; In addition, the alignment of a huge number of contigs is computationally intensive and time-consuming.”
Chapter 12: Human Microbiome Analysis (2012, background): “It is difficult to recommend comprehensive literature in an area that is changing so rapidly, but the bioinformatics of microbial community studies are currently best covered by the reviews in [22,56,165].”
Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes (2014, background): “Hence, a typical metagenomic assembly will result in a large set of independent contigs that are not easily aggregated into biological entities 6 .”
Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression (2014, background): “In general, this is an extremely difficult bioinformatics problem [21] and is likely to be a”
Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research (2017, background): “…bioinformatics front, there have been tremendous efforts to develop systems to process and analyze microbiome data, including QIIME (13) and mothur (14), primarily for 16S rRNA data, while metagenomic data analysis is still often pieced together using various individual bioinformatic methods (15).”
Multi-Layer and Recursive Neural Networks for Metagenomic Classification (2015, background): “M ETAGENOMICS is the study of DNA from microorganisms obtained directly from an environmental sample [1].”
Experimental and analytical tools for studying the human microbiome (2011, background): “Depending on the sequencing strategy, coverage and community complexity, the sequences can either be used directly as short fragments, or they can be assembled into larger contigs for gene calling before annotation (for a review of metagenomic assembly, see Ref”