Microbiology is experiencing a revolution brought on by recent developments in sequencing technology. The unprecedented volume of microbiome data being generated poses significant challenges that are currently hindering progress in the field. Here, we outline the major bottlenecks and propose a vision to advance microbiome research as a data-driven science.

The Integrated Microbial Genomes and Microbiomes (IMG/M) system is a web-based platform that provides access to the wealth of public sequence data arising from diverse environments and enables the user to answer biological questions. In this review, we explore IMG’s tools and features using genome data for genus Deinococcus isolates as well as metagenome-assembled genomes (MAGs). We use various comparative genomic and visualization tools to investigate this genus and address specific research questions.

The analysis of large microbiome data sets holds great promise for the delineation of the biological and metabolic functioning of living organisms and their role in the environment. In the midst of this genomic puzzle, viruses, especially those that infect microbial communities, represent a major reservoir of genetic diversity with great impact on biogeochemical cycles and organismal health. Overcoming the limitations associated with virus detection directly from microbiomes can provide key insights into how ecosystem dynamics are modulated. Here, we present a computational protocol for accurate detection and grouping of viral sequences from microbiome samples. Our approach relies on an expanded and curated set of viral protein families used as bait to identify viral sequences directly from metagenomic assemblies. This protocol describes how to use the viral protein families catalog (∼7 h) and recommended filters for the detection of viral contigs in metagenomic samples (∼6 h), and it describes the specific parameters for a nucleotide-sequence-identity-based method of organizing the viral sequences into quasi-species taxonomic-level groups (∼10 min).

Our view of microbial diversity has expanded greatly over the past 40 years, primarily through the wide application of PCR-based surveys of the small-subunit ribosomal RNA (SSU rRNA) gene. Yet significant gaps in knowledge remain due to well-recognized limitations of this method. Here, we systematically survey primer fidelity in SSU rRNA gene sequences recovered from over 6,000 assembled metagenomes sampled globally. Our findings show that approximately 10% of environmental microbial sequences might be missed from classical PCR-based SSU rRNA gene surveys, mostly members of the Candidate Phyla Radiation (CPR) and as yet uncharacterized Archaea. These results underscore the extent of uncharacterized microbial diversity and provide fruitful avenues for describing additional phylogenetic lineages.

Correction

Following publication of the original article [1], the authors pointed out that the figure shown as figure S1 is actually figure S2 and vice versa. Figure S1 should show the barcharts, and figure S2 should shows the heatmaps. Figures captions are in the correct order. 1) "...SILVA-only OTUs (37,066 97% OTUs and 1266 85% clusters) (Additional file 3: Figure S2 )..." - > ...SILVA-only OTUs (37,066 97% OTUs and 1266 85% clusters) (Additional file 3: Figure S1 )..." 2) "...in groundwater and soil (Fig. 2, Additional file 2: Figure S1 )." - > "in groundwater and soil (Fig. 2, Additional file 2: Figure S2 )." 3) "...from genomes as training data (Additional file 3: Figure S2 , ..." - > "...from genomes as training data (Additional file 3: Figure S1 , ..." 4) "...were predicted to be chimeric (Additional file 3: Figure S2 )" - > "...were predicted to be chimeric (Additional file 3: Figure S1 )".

Motivation: A typical metagenome dataset generated using a 454 pyrosequencing platform consists of short reads sampled from the collective genome of a microbial community. The amount of sequence in such datasets is usually insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short reads. As a result, analysis of such datasets usually involves comparisons in terms of relative abundances of various protein families. The latter requires assignment of individual reads to protein families, which is hindered by the fact that short reads contain only a fragment, usually small, of a protein. Results: We have considered the assignment of pyrosequencing reads to protein families directly using RPS-BLAST against COG and Pfam databases and indirectly via proxygenes that are identified using BLASTx searches against protein sequence databases. Using simulated metagenome datasets as benchmarks, we show that the proxygene method is more accurate than the direct assignment. We introduce a clustering method which significantly reduces the size of a metagenome dataset while maintaining a faithful representation of its functional and taxonomic content.

Microbial secondary metabolism is a reservoir of bioactive compounds of immense biotechnological and biomedical potential. The biosynthetic machinery responsible for the production of these secondary metabolites (SMs) (also called natural products) is often encoded by collocated groups of genes called biosynthetic gene clusters (BGCs). High-throughput genome sequencing of both isolates and metagenomic samples combined with the development of specialized computational workflows is enabling systematic identification of BGCs and the discovery of novel SMs. In order to advance exploration of microbial secondary metabolism and its diversity, we developed the largest publicly available database of predicted BGCs combined with experimentally verified BGCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc-public). Here we describe the first major content update of the IMG-ABC knowledgebase, since its initial release in 2015, refreshing the BGC prediction pipeline with the latest version of antiSMASH (v5) as well as presenting the data in the context of underlying environmental metadata sourced from GOLD (https://gold.jgi.doe.gov/). This update has greatly improved the quality and expanded the types of predicted BGCs compared to the previous version.