Metagenomics - a guide from sampling to data analysis

By Torsten Thomas, Jack A. Gilbert, Folker Meyer. Microbial Informatics and Experimentation, 2012

951 citations recorded by the source corpus.

Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a20f7ed408376e27ba6ad63bc1f23f27f161eea5

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Citations in the corpus

The Potential of Metagenomic Approaches for Understanding Soil Microbial Processes (2014, neutral): “The challenges have not, however, disappeared (Thomas et al., 2012).”
Microbial β-Glucosidase: Sources, Production and Applications (2017, background): “[166] Thomas T, Gilbert J, Meyer F: Metagenomics-a guide from sampling to data analysis.”
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, background): “…the genome context (i.e., operons); (2) Increasing the probability of complete genes and genomes reconstruction, arising the conﬁdence of sequence annotation; (3) Analysis simpliﬁcation by mapping long contigs instead of short reads (Thomas et al., 2012; Luo et al., 2013; Segata et al., 2013).”
Current challenges and best-practice protocols for microbiome analysis (2019, background): “In fact, sample collection could be a significant confounding factor that might affect the results and interpretations of a study [49].”
ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples (2019, positive): “The term metagenomics implies complete sequencing and recovering of all non-human genetic material that might be present in a human sample and accordingly, analytical techniques for virus discovery are commonly applied to metagenomic samples[5, 7, 8, 14, 15, 16, 17, 18, 19].”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, methodology): “Quality check of sequences FastQC [90], FASTX-toolkit [91], MultiQC [92] Trimming of adaptors and low-quality bases Trimmomatic [93], Cutadapt [94], fastp [95] Alignment of sequence reads to reference genome BWA [96], Bowtie [97], dragMAP [98]”
DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, background): “…tools and platforms are now tailored to gene sequences derived from environmental samples, particularly for prokaryotic assemblages (Kanehisa et al., 2008; Meyer et al., 2008; Caporaso et al., 2010; Abubucker et al., 2012; Thomas et al., 2012; Kozich et al., 2013; Hunter et al., 2014).”
PhyloSift: phylogenetic analysis of genomes and metagenomes (2014, methodology): “Metagenomics - the sequencing of DNA isolated directly from the environment - has become a routinely 13 used tool with wide applications [57].”
The MG-RAST metagenomics database and portal in 2015 (2015, background): “This number reflects the growing trend in use of DNA-sequencing based assays (amplicon and shotgun metagenomics, metatranscriptomics) (2) to study microbiota in diverse environments.”