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The taxonomy of viruses is developed and overseen by the International Committee on Taxonomy of Viruses (ICTV), which scrutinizes, approves and ratifies taxonomic proposals, and maintains a list of virus taxa with approved names (https://ictv.global). The ICTV has approximately 180 members who vote by simple majority. Taxon-specific Study Groups established by the ICTV have a combined membership of over 600 scientists from the wider virology community; they provide comprehensive expertise across the range of known viruses and are major contributors to the creation and evaluation of taxonomic proposals. Proposals can be submitted by anyone and will be considered by the ICTV irrespective of Study Group support. Thus, virus taxonomy is developed from within the virology community and realized by a democratic decision-making process. The ICTV upholds the distinction between a virus or replicating genetic element as a physical entity and the taxon category to which it is assigned. This is reflected by the nomenclature of the virus species taxon, which is now mandated by the ICTV to be in a binomial format (genus + species epithet) and is typographically distinct from the names of viruses. Classification of viruses below the rank of species (such as, genotypes or strains) is not within the remit of the ICTV. This article, authored by the ICTV Executive Committee, explains the principles of virus taxonomy and the organization, function, processes and resources of the ICTV, with the aim of encouraging greater understanding and interaction among the wider virology community.

26 pages, 5 figures, supporting information https://doi.org/10.1371/journal.pbio.3002083.-- Data Availability: All data directly relevant are within the paper and its Supporting Information files. Benchmarking analysis were based on the publicly available IMG/VR v3 database (doi 10.1093/nar/gkaa946 - https://genome.jgi.doe.gov/portal/IMG_VR/IMG_VR.home.html), The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses, BED was supported by the European Research Council (ERC) Consolidator grant 865694: DiversiPHI, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2051 – Project-ID 390713860, the Alexander von Humboldt Foundation in the context of an Alexander von Humboldt Professorship funded by the German Federal Ministry of Education and Research, and the European Union’s Horizon 2020 research and innovation program, under the Marie Skłodowska-Curie Actions Innovative Training Networks grant agreement no. 955974 (VIROINF). FHC was supported by a Juan de la Cierva - Incoporación fellowship (Grant IJC2019-039859-I), and had the institutional support of the “Severo Ochoa Centre of Excellence'' accreditation (CEX2019-000928-S). This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Early Career Research Program (SR) awarded under UC-DOE Prime Contract DE-AC02-05CH11231. The work conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231 (SR, APC, SN)

Motivation The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads are valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this article, we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes. Results We show that for small datasets OGRE outperforms other read binners in terms of the number of species included in a cluster, also referred to as cluster purity, and the fraction of all reads that is placed in one of the clusters. Furthermore, OGRE is able to process metagenomic datasets that are too large for other read binners into clusters with high cluster purity. Conclusion OGRE is the only method that can successfully cluster reads in species-specific clusters for large metagenomic datasets without running into computation time- or memory issues. Availabilityand implementation Code is made available on Github (https://github.com/Marleen1/OGRE). Supplementary information Supplementary data are available at Bioinformatics online.

Additional file 4: Figure S4. Comparison of panEFMs across environments. Scatter plot of the average reaction frequency of panEFMs defined across random virtual environments and within each environment.

Additional file 2: Figure S2. Distribution of pan-reactome reaction frequencies and panEFMs reaction frequencies. The x-axis contains the frequency of reactions in sampled panEFMs across random environments, while the y-axis contains the natural frequency coded by the genomes of the taxonomic families.

Additional file 3: Figure S3. Distribution of the environment-driven reaction score (EDS) and panEFMs reaction frequencies.

Additional file 6: Table S1. Reactions in the toy model

Additional file 5: Figure S5. Size distribution of panEFMs sampled across random virtual environments.

Additional file 1: Figure S1. Convergence of the reaction frequencies of panEFMs sampled across 1000 virtual environments (Table S2) to an average. The frequency of reactions was obtained from two random-independent (non-overlapping) groups of panEFMs defined across the env

Biodegradation of pollutants is a sustainable and cost-effective solution to groundwater pollution. Here, we investigate microbial populations involved in biodegradation of poly-contaminants in a pipeline for heavily contaminated groundwater. Groundwater moves from a polluted park to a treatment plant, where an aerated bioreactor effectively removes the contaminants. While the biomass does not settle in the reactor, sediment is collected afterwards and used to seed the new polluted groundwater via a backwash cycle. The pipeline has successfully operated since 1999, but the biological components in the reactor and the contaminated park groundwater have never been described. We sampled seven points along the pipeline, representing the entire remediation process, and characterized the changing microbial communities using genome-resolved metagenomic analysis. We assembled 297 medium- and high-quality metagenome-assembled genome sequences representing on average 46.3% of the total DNA per sample. We found that the communities cluster into two distinct groups, separating the anaerobic communities in the park groundwater from the aerobic communities inside the plant. In the park, the community is dominated by members of the genus Sulfuricurvum, while the plant is dominated by generalists from the order Burkholderiales. Known aromatic compound biodegradation pathways are four times more abundant in the plant-side communities compared to the park-side. Our findings provide a genome-resolved portrait of the microbial community in a highly effective groundwater treatment system that has treated groundwater with a complex contamination profile for two decades.

Additional file 22 Supplementary Figure 6. LefSe analysis: relative abundances association with hrHPV status. Relative abundance counts of G. vaginalis (A), M. genomosp type 1 (B), S. amnii (C), S. sanguinegens (D), P. anaerobius (E), D. micraerophilus (F), A. vaginae (G), P. amnii (H), and P. buccalis (I) were found significantly over-represented in hrHPV positive women whereas Lactobacillus acidophilus (J) was enriched in hrHPV negative women.

Metagenomics, i.e., the sequencing and analysis of genomic information extracted directly from clinical or environmental samples, has become a fundamental tool to explore the viral world. Against the background of an extensive viral diversity revealed by metagenomics across many environments, new sequence assembly approaches that reconstruct complete genome sequences from metagenomes have recently revealed surprisingly cosmopolitan viruses in specific ecological niches. Metagenomics is also applied to clinical samples as a non-targeted diagnostic and surveillance tool. By enabling the study of these uncultivated viruses, metagenomics provides invaluable insights into the virus-host interactions, epidemiology, ecology, and evolution of viruses across all ecosystems.

Additional file 5: Fig. S4. Distribution plot of prediction probabilities for the 2,133,027 pVOG pairs in the target dataset. Positive interactions have a probability greater than 0.5. Stacked bars are colored based on the annotation status of the pVOGs according to the legend

Additional file 12 Supplementary Figure 3. CiRNAseq specificity towards Bifidobacterium and Gardnerella species. 16S rRNA-seq targets two variable regions (VRs) of the 16S rRNA gene using a forward and a reverse primer (e.g., V3 and V4). Alternatively, CiRNAseq targets five VRs of the 16S subunit using five singular smMIPs to differentiate species of B.longum and G. vaginalis. There is a high percentage of similarity (>90%) when comparing the V3-V4 regions of B. longum and G. vaginalis, which, if it is not appropriately amplified, could result in misidentification. In contrast, the CiRNAseq ROIs for both species have different levels of identity. Two out of five ROIs also possess a high percentage of similarity (>90%), with both amplifying the V7 and V8 VRs, and needed to identify these microbes at the family level. The rest remaining three out of five ROIs share less than 45% of similarity, which endorses the resolution and specificity of CiRNAseq in detecting both species. The color red represents similarity in sequences, while the color black represents no similarity.

Additional file 6 Supplementary Figure 1. In silico validation of CiRNAseq specificity towards cervicovaginal microbial species. CiRNAseq targets multiple 16S rRNA gene VRs e.g. V5 ��� V9 for microbiome profiling. For reads assigning and species annotation, the method has a threshold of 95% of similarity between sequences and reference ROIs (A). Phylogenetic analyses of the 16S RNA gene for the species Anaerococcus vaginalis, Anaerococcus tetradius, Peptostreptococcus anaerobius, Gardnerella vaginalis, and Prevotella buccalis shows the similarity between the 16S rRNA genomes (B). Alignment analyses of the ROIs from the closest related species to the least related species, according to (B), demonstrate the specificity of CiRNAseq. For sequencing A. vaginalis and A. tetradius the technique uses the same set of five smMIPs, but two out of five ROIs exhibit

Additional file 9. In silico alignment analyses (.pdf). Results obtained with Clustal Omega v1.2.4 for Supplementary Figures 1 and 3.

Contains fulltext : 241337.pdf (Publisher’s version ) (Open Access) Plasmodium species have a single mitochondrion that is essential for their survival and has been successfully targeted by antimalarial drugs. Most mitochondrial proteins are imported into this organelle, and our picture of the Plasmodium mitochondrial proteome remains incomplete. Many data sources contain information about mitochondrial localization, including proteome and gene expression profiles, orthology to mitochondrial proteins from other species, coevolutionary relationships, and amino acid sequences, each with different coverage and reliability. To obtain a comprehensive, prioritized list of Plasmodium falciparum mitochondrial proteins, we rigorously analyzed and integrated eight data sets using Bayesian statistics into a predictive score per protein for mitochondrial localization. At a corrected false discovery rate of 25%, we identified 445 proteins with a sensitivity of 87% and a specificity of 97%. They include proteins that have not been identified as mitochondrial in other eukaryotes but have characterized homologs in bacteria that are involved in metabolism or translation. Mitochondrial localization of seven Plasmodium berghei orthologs was confirmed by epitope labeling and colocalization with a mitochondrial marker protein. One of these belongs to a newly identified apicomplexan mitochondrial protein family that in P. falciparum has four members. With the experimentally validated mitochondrial proteins and the complete ranked P. falciparum proteome, which we have named PlasmoMitoCarta, we present a resource to study unique proteins of Plasmodium mitochondria. IMPORTANCE The unique biology and medical relevance of the mitochondrion of the malaria parasite Plasmodium falciparum have made it the subject of many studies. However, we actually do not have a comprehensive assessment of which proteins reside in this organelle. Many omics data are available that are predictive of mitochondrial localization, such as proteomics data and expression data. Individual data sets are, however, rarely complete and can provide conflicting evidence. We integrated a wide variety of available omics data in a manner that exploits the relative strengths of the data sets. Our analysis gave a predictive score for the mitochondrial localization to each nuclear encoded P. falciparum protein and identified 445 likely mitochondrial proteins. We experimentally validated the mitochondrial localization of seven of the new mitochondrial proteins, confirming the quality of the complete list. These include proteins that have not been observed mitochondria before, adding unique mitochondrial functions to P. falciparum.

Additional file 21 Supplementary Figure 5. Average analysis of hrHPV cohort. Average analysis of hrHPV negative versus hrHPV positive with CIN2+ demonstrates a strong association of absence and presence of particular microbes. L. acidophilus, L. jensenii and L. crispatus were highly present in hrHPV negative women, but the microbiome seemingly changes to a diverse-anaerobic microbiota in hrHPV positive women with CIN2+.

Additional file 3: Fig. S2. Mean absolute accuracy of all classifiers. Barplots represent the mean number of correct classifications; error bars represent standard deviation. Red dashed line: number of interactions in the holdout set (n =62)

Horizontal gene transfer (HGT) is an essential force in microbial evolution. Despite detailed studies on a variety of systems, a global picture of HGT in the microbial world is still missing. Here, we exploit that HGT creates long identical DNA sequences in the genomes of distant species, which can be found efficiently using alignment-free methods. Our pairwise analysis of 93,481 bacterial genomes identified 138,273 HGT events. We developed a model to explain their statistical properties as well as estimate the transfer rate between pairs of taxa. This reveals that long-distance HGT is frequent: our results indicate that HGT between species from different phyla has occurred in at least 8% of the species. Finally, our results confirm that the function of sequences strongly impacts their transfer rate, which varies by more than three orders of magnitude between different functional categories. Overall, we provide a comprehensive view of HGT, illuminating a fundamental process driving bacterial evolution.

Additional file 4: Fig. S3. ROC curves for the 10 datasets (N1–N10) used for performance evaluation. Each dataset was used as a ground truth set for parameter optimization of a Random Forest classifier (70% training). The resulting best model was used for predictions on the holdout set (30% of the original) and its ROC curve is depicted by a thicker line. The remaining 9 datasets were used as input for the best model obtained for training (70%) and holdout (30%) and their ROC curves are shown as more transparent lines (AUROC = Area Under the Receiver Operating Characteristic)

Additional file 2: Fig. S1. Performance metrics for 10 different RF classifiers obtained from all datasets (N1–N10). Each classifier was optimized with the respective dataset and performance was evaluated using the remaining nine datasets as input. Boxplots show the median, lower and upper quartile with the whiskers extending to 1.5 times the interquartile range; the diamonds are outliers. Y-axis starts from 0.4 for visualization purposes

Contains fulltext : 232020.pdf (Publisher’s version ) (Open Access) Archaea synthesize membranes of isoprenoid lipids that are ether-linked to glycerol-1-phosphate (G1P), while Bacteria/Eukarya produce membranes consisting of fatty acids ester-bound to glycerol-3-phosphate (G3P). This dichotomy in membrane lipid composition (i.e., the 'lipid divide') is believed to have arisen after the Last Universal Common Ancestor (LUCA). A leading hypothesis is that LUCA possessed a heterochiral 'mixed archaeal/bacterial membrane'. However, no natural microbial representatives supporting this scenario have been shown to exist today. Here, we demonstrate that bacteria of the Fibrobacteres-Chlorobi-Bacteroidetes (FCB) group superphylum encode a putative archaeal pathway for ether-bound isoprenoid membrane lipids in addition to the bacterial fatty acid membrane pathway. Key genes were expressed in the environment and their recombinant expression in Escherichia coli resulted in the formation of a 'mixed archaeal/bacterial membrane'. Genomic evidence and biochemical assays suggest that the archaeal-like lipids of members of the FCB group could possess either a G1P or G3P stereochemistry. Our results support the existence of 'mixed membranes' in natural environments and their stability over a long period in evolutionary history, thereby bridging a once-thought fundamental divide in biology.

15 pages, 5 figures, 1 table, supplemental Information https://doi.org/10.1016/j.chom.2019.08.019, Phages are increasingly recognized as important members of host-associated microbiomes, with a vast genomic diversity. The new frontier is to understand how phages may affect higher order processes, such as in the context of host-microbe interactions. Here, we use marine sponges as a model to investigate the interplay between phages, bacterial symbionts, and eukaryotic hosts. Using viral metagenomics, we find that sponges, although massively filtering seawater, harbor species-specific and even individually unique viral signatures that are taxonomically distinct from other environments. We further discover a symbiont phage-encoded ankyrin-domain-containing protein, which is widely spread in phages of many host-associated contexts including human. We confirm in macrophage infection assays that the ankyrin protein (ANKp) modulates the eukaryotic host immune response against bacteria. We predict that the role of ANKp in nature is to facilitate coexistence in the tripartite interplay between phages, symbionts, and sponges and possibly many other host-microbe associations, We acknowledge funding by the DFG CRC1182 to U.H. (TPB1), T.L. (TPA4), P.R. (TPC2), and A.K. (TPC3). M.T.J. was supported by a grant of the German Excellence Initiative to the Graduate School of Life Sciences, University of Würzburg, and the Young Investigator Award of CRC1182. S.M.M. was supported by the Studienstiftung des Deutschen Volkes

Recent work has vastly expanded the known viral genomic sequence space, but the seasonal dynamics of viral populations at the genome level remain unexplored. Here we followed the viral community in a freshwater lake for one year using genome-resolved viral metagenomics, combined with detailed analyses of the viral community structure, associated bacterial populations, and environmental variables. We reconstructed 8,950 complete and partial viral genomes, the majority of which were not persistent in the lake throughout the year, but instead continuously succeeded each other. Temporal analysis of 732 viral genus-level clusters demonstrated that one fifth were undetectable at specific periods of the year. Based on host predictions for a subset of reconstructed viral genomes, we for the first time reveal three distinct patterns of host-pathogen dynamics, where the viruses may peak before, during, or after the peak in their host’s abundance, providing new possibilities for modelling of their interactions. Time-series metagenomics opens up a new dimension in viral profiling, which is essential to understand the full scale of viral diversity and evolution, and the ecological roles of these important players in the global ecosystem.

Contains fulltext : 190650.pdf (Publisher’s version ) (Closed access) The environmental metabolome and metabolic potential of microorganisms are dominant and essential factors shaping microbial community composition. Recent advances in genome annotation and systems biology now allow us to semiautomatically reconstruct genome-scale metabolic models (GSMMs) of microorganisms based on their genome sequence (1) . Next, growth of these models in a defined metabolic environment can be predicted in silico, mechanistically linking the metabolic fluxes of individual microbial populations to the community dynamics. A major advantage of GSMMs is that no training data is needed, besides information about the metabolic capacity of individual genes (genome annotation) and knowledge of the available environmental metabolites that allow the microorganism to grow. However, the composition of the environment is often not fully determined and remains difficult to measure (2) . We hypothesized that the relative abundance of different bacterial species, as measured by metagenomics, can be combined with GSMMs of individual bacteria to reveal the metabolic status of a given biome. Using a newly developed algorithm involving over 1,500 GSMMs of human-associated bacteria, we inferred distinct metabolomes for four human body sites that are consistent with experimental data. Together, we link the metagenome to the metabolome in a mechanistic framework towards predictive microbiome modelling.

Teredinidae are a family of highly adapted wood-feeding and wood-boring bivalves, commonly known as shipworms, whose evolution is linked to the acquisition of cellulolytic gammaproteobacterial symbionts harbored in bacteriocytes within the gills. In the present work we applied metagenomics to characterize microbiomes of the gills and digestive tract of Neoteredo reynei, a mangrove-adapted shipworm species found over a large range of the Brazilian coast. Comparative metagenomics grouped the gill symbiont community of different N. reynei specimens, indicating closely related bacterial types are shared. Similarly, the intestine and digestive gland communities were related, yet were more diverse than and showed no overlap with the gill community. Annotation of assembled metagenomic contigs revealed that the gill symbiotic community of N. reynei encodes a plethora of plant cell wall polysaccharides degrading glycoside hydrolase encoding genes, and Biosynthetic Gene Clusters (BGCs). In contrast, the digestive tract microbiomes seem to play little role in wood digestion and secondary metabolites biosynthesis. Metagenome binning recovered the nearly complete genome sequences of two symbiotic Teredinibacter strains from the gills, a representative of Teredinibacter turnerae “clade I” strain, and a yet to be cultivated Teredinibacter sp. type. These Teredinibacter genomes, as well as un-binned gill-derived gammaproteobacteria contigs, also include an endo-β-1,4-xylanase/acetylxylan esterase multi-catalytic carbohydrate-active enzyme, and a trans-acyltransferase polyketide synthase (trans-AT PKS) gene cluster with the gene cassette for generating β-branching on complex polyketides. Finally, we use multivariate analyses to show that the secondary metabolome from the genomes of Teredinibacter representatives, including genomes binned from N. reynei gills’ metagenomes presented herein, stands out within the Cellvibrionaceae family by size, and enrichments for polyketide, nonribosomal peptide and hybrid BGCs. Results presented here add to the growing characterization of shipworm symbiotic microbiomes and indicate that the N. reynei gill gammaproteobacterial community is a prolific source of biotechnologically relevant enzymes for wood-digestion and bioactive compounds production.

Item does not contain fulltext Phages are complex biomolecular machineries that have to survive in a bacterial world. Phage genomes show many adaptations to their lifestyle such as shorter genes, reduced capacity for redundant DNA sequences, and the inclusion of tRNAs in their genomes. In addition, phages are not free-living, they require a host for replication and survival. These unique adaptations provide challenges for the bioinformatics analysis of phage genomes. In particular, ORF calling, genome annotation, noncoding RNA (ncRNA) identification, and the identification of transposons and insertions are all complicated in phage genome analysis. We provide a road map through the phage genome annotation pipeline, and discuss the challenges and solutions for phage genome annotation as we have implemented in the rapid annotation using subsystems (RAST) pipeline.

Summary: Analyzing the functional profile of a microbial community from unannotated shotgun sequencing reads is one of the important goals in metagenomics. Functional profiling has valuable applications in biological research because it identifies the abundances of the functional genes of the organisms present in the original sample, answering the question what they can do. Currently, available tools do not scale well with increasing data volumes, which is important because both the number and lengths of the reads produced by sequencing platforms keep increasing. Here, we introduce SUPER-FOCUS, SUbsystems Profile by databasE Reduction using FOCUS, an agile homology-based approach using a reduced reference database to report the subsystems present in metagenomic datasets and profile their abundances. SUPER-FOCUS was tested with over 70 real metagenomes, the results showing that it accurately predicts the subsystems present in the profiled microbial communities, and is up to 1000 times faster than other tools. Availability and implementation: SUPER-FOCUS was implemented in Python, and its source code and the tool website are freely available at https://edwards.sdsu.edu/SUPERFOCUS. Contact: redwards@mail.sdsu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

With great interest we read the article of Taylor et al in the Journal of Clinical Pathology regarding the use of guaiac faecal occult blood test (gFOBT) cards for microbiome studies.1 gFOBT cards were found to be an easy to use option for stool collection and gained results comparable to fresh stool, even when cards were stored for up to 3 years at ambient temperature before DNA extraction. We would like to share our experience that even after 10 years of storage, gFOBT cards and faecal immunochemical test (FIT) tubes can be used to study the microbiome. Taylor et al conducted 16S rDNA amplicon sequencing for determination of outcome comparability within and between sample type and storage time.1 Between-subject differences greatly outweighed any differences by sample collection type (fresh vs gFOBT) and time spent at ambient temperature (from 2 weeks up to 3 years after development of the gFOBT cards), which was also reported previously.2–4 No appreciable differences within the three individuals with regard to microbial taxa composition, alpha and beta diversity index and intraclass correlation (ICC) were observed.1 In our analysis, we used gFOBT cards (Hemoccult II; Beckman Coulter) and FIT tubes (OC-sensor; Eiken Chemical) that were collected a decade ago to evaluate the positive predictive value and specificity of these tests in a colorectal cancer screening population.5 Storage times at ambient temperature ranged from 2 to 11 days before samples were developed and frozen at −80°C for long-term storage. From 5 FIT tubes and 12 gFOBT cards DNA was extracted with a protocol adjusted from the Human Microbiome Project including bead beating (see online supplementary file 1). DNA quantity and quality from these 10-year-old samples were evaluated and analysed with quantitative PCR (qPCR) for the most abundant phyla in stool. ### Supplementary file 1 [SP1.docx] FIT tubes that were stored for 2–4 …

Contains fulltext : 136016pub.pdf (Publisher’s version ) (Closed access) The Human Microbiome Project (HMP) is a global initiative undertaken to identify and characterize the collection of human-associated microorganisms at multiple anatomic sites (skin, mouth, nose, colon, vagina), and to determine how intra-individual and inter-individual alterations in the microbiome influence human health, immunity, and different disease states. In this review article, we summarize the key findings and applications of the HMP that may impact pharmacology and personalized therapeutics. We propose a microbiome cloud model, reflecting the temporal and spatial uncertainty of defining an individual's microbiome composition, with examples of how intra-individual variations (such as age and mode of delivery) shape the microbiome structure. Additionally, we discuss how this microbiome cloud concept explains the difficulty to define a core human microbiome and to classify individuals according to their biome types. Detailed examples are presented on microbiome changes related to colorectal cancer, antibiotic administration, and pharmacomicrobiomics, or drug-microbiome interactions, highlighting how an improved understanding of the human microbiome, and alterations thereof, may lead to the development of novel therapeutic agents, the modification of antibiotic policies and implementation, and improved health outcomes. Finally, the prospects of a collaborative computational microbiome research initiative in Africa are discussed.

Ocean microbes drive biogeochemical cycling on a global scale. However, this cycling is constrained by viruses that affect community composition, metabolic activity, and evolutionary trajectories. Owing to challenges with the sampling and cultivation of viruses, genome-level viral diversity remains poorly described and grossly understudied, with less than 1% of observed surface-ocean viruses known. Here we assemble complete genomes and large genomic fragments from both surface- and deep-ocean viruses sampled during the Tara Oceans and Malaspina research expeditions, and analyse the resulting 'global ocean virome' dataset to present a global map of abundant, double-stranded DNA viruses complete with genomic and ecological contexts. A total of 15,222 epipelagic and mesopelagic viral populations were identified, comprising 867 viral clusters (defined as approximately genus-level groups). This roughly triples the number of known ocean viral populations and doubles the number of candidate bacterial and archaeal virus genera, providing a near-complete sampling of epipelagic communities at both the population and viral-cluster level. We found that 38 of the 867 viral clusters were locally or globally abundant, together accounting for nearly half of the viral populations in any global ocean virome sample. While two-thirds of these clusters represent newly described viruses lacking any cultivated representative, most could be computationally linked to dominant, ecologically relevant microbial hosts. Moreover, we identified 243 viral-encoded auxiliary metabolic genes, of which only 95 were previously known. Deeper analyses of four of these auxiliary metabolic genes (dsrC, soxYZ, P-II (also known as glnB) and amoC) revealed that abundant viruses may directly manipulate sulfur and nitrogen cycling throughout the epipelagic ocean. This viral catalog and functional analyses provide a necessary foundation for the meaningful integration of viruses into ecosystem models where they act as key players in nutrient cycling and trophic networks.

Contains fulltext : 136075.pdf (Publisher’s version ) (Open Access) Genomics and metagenomics have revolutionized our understanding of marine microbial ecology and the importance of microbes in global geochemical cycles. However, the process of DNA sequencing has always been an abstract extension of the research expedition, completed once the samples were returned to the laboratory. During the 2013 Southern Line Islands Research Expedition, we started the first effort to bring next generation sequencing to some of the most remote locations on our planet. We successfully sequenced twenty six marine microbial genomes, and two marine microbial metagenomes using the Ion Torrent PGM platform on the Merchant Yacht Hanse Explorer. Onboard sequence assembly, annotation, and analysis enabled us to investigate the role of the microbes in the coral reef ecology of these islands and atolls. This analysis identified phosphonate as an important phosphorous source for microbes growing in the Line Islands and reinforced the importance of L-serine in marine microbial ecosystems. Sequencing in the field allowed us to propose hypotheses and conduct experiments and further sampling based on the sequences generated. By eliminating the delay between sampling and sequencing, we enhanced the productivity of the research expedition. By overcoming the hurdles associated with sequencing on a boat in the middle of the Pacific Ocean we proved the flexibility of the sequencing, annotation, and analysis pipelines.

Background Vibrio cholerae is a globally dispersed pathogen that has evolved with humans for centuries, but also includes non-pathogenic environmental strains. Here, we identify the genomic variability underlying this remarkable persistence across the three major niche dimensions space, time, and habitat. Results Taking an innovative approach of genome-wide association applicable to microbial genomes (GWAS-M), we classify 274 complete V. cholerae genomes by niche, including 39 newly sequenced for this study with the Ion Torrent DNA-sequencing platform. Niche metadata were collected for each strain and analyzed together with comprehensive annotations of genetic and genomic attributes, including point mutations (single-nucleotide polymorphisms, SNPs), protein families, functions and prophages. Conclusions Our analysis revealed that genomic variations, in particular mobile functions including phages, prophages, transposable elements, and plasmids underlie the metadata structuring in each of the three niche dimensions. This underscores the role of phages and mobile elements as the most rapidly evolving elements in bacterial genomes, creating local endemicity (space), leading to temporal divergence (time), and allowing the invasion of new habitats. Together, we take a data-driven approach for comparative functional genomics that exploits high-volume genome sequencing and annotation, in conjunction with novel statistical and machine learning analyses to identify connections between genotype and phenotype on a genome-wide scale. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-654) contains supplementary material, which is available to authorized users.

Sequencing DNA or RNA directly from the environment often results in many sequencing reads that have no homologs in the database. These are referred to as "unknowns," and reflect the vast unexplored microbial sequence space of our biosphere, also known as "biological dark matter." However, unknowns also exist because metagenomic datasets are not optimally mined. There is a pressure on researchers to publish and move on, and the unknown sequences are often left for what they are, and conclusions drawn based on reads with annotated homologs. This can cause abundant and widespread genomes to be overlooked, such as the recently discovered human gut bacteriophage crAssphage. The unknowns may be enriched for bacteriophage sequences, the most abundant and genetically diverse component of the biosphere and of sequence space. However, it remains an open question, what is the actual size of biological sequence space? The de novo assembly of shotgun metagenomes is the most powerful tool to address this question.

Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.

Additional file 2. List of smMIPs and sequences used for CiRNAseq profiling of the cervicovaginal microbiome (.pdf). List of designed 30 smMIPs with ligation and extension arms that target 434 relevant microbes in the cervicovaginal microbiome at the DNA and RNA levels using CiRNAseq.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Additional file 5 Supplementary Table 2. E. coli growth experiment. T: time; TP: time point of analyses; OD: optical density; RNAc: RNA concentration in ng/uL; URC: unique read counts; R1: replicate 1; R2: replicate 2; URCm: mean of URC from R1 and R2.

The Second Annual Meeting of the European Virus Bioinformatics Center (EVBC), held in Utrecht, Netherlands, focused on computational approaches in virology, with topics including (but not limited to) virus discovery, diagnostics, (meta-)genomics, modeling, epidemiology, molecular structure, evolution, and viral ecology. The goals of the Second Annual Meeting were threefold: (i) to bring together virologists and bioinformaticians from across the academic, industrial, professional, and training sectors to share best practice; (ii) to provide a meaningful and interactive scientific environment to promote discussion and collaboration between students, postdoctoral fellows, and both new and established investigators; (iii) to inspire and suggest new research directions and questions. Approximately 120 researchers from around the world attended the Second Annual Meeting of the EVBC this year, including 15 renowned international speakers. This report presents an overview of new developments and novel research findings that emerged during the meeting, Viruses, 10 (5), ISSN:1999-4915

Additional file 5 Supplementary Table 2. E. coli growth experiment. T: time; TP: time point of analyses; OD: optical density; RNAc: RNA concentration in ng/uL; URC: unique read counts; R1: replicate 1; R2: replicate 2; URCm: mean of URC from R1 and R2.

Additional file 4. Supplementary Table 1. Bacterial species used for in vitro experiments. MMB: Department of Medical Microbiology, Radboudumc.

Additional file 16 Supplementary Figure 4. Pearson���s correlation for Lactobacillus, Gardnerella, Atopobium and Megasphaera. Pearson���s positive correlation obtained from comparing the detection of Lactobacillus (A) (r = 0.9605, p = 0.0006), Gardnerella (B) (r = 0.9384, p = 0.0018), Atopobium (C) (r = 0.9255, p = 0.0028), and Megasphaera (r = 0.8344, p = 0.0196) using both CiRNAseq and 16S rRNA-seq corroborates the specificity and sensitivity of CiRNAseq.

Additional file 4. Supplementary Table 1. Bacterial species used for in vitro experiments. MMB: Department of Medical Microbiology, Radboudumc.

Additional file 11 Supplementary Figure 2. E. coli growth experiment. A Two replicates of the samples subjected to CiRNAseq shows the reproducibility of the technique by the number of unique read counts (URC) obtained in each replicate. B Mean of the replicates��� URC. C OD and RNA concentrations analyzed in time points five (growth), eight (autoclaved), and nine (antibiotic) were comparable with each other. However, as expected, the E. coli growth sample from time point eight had no URC after autoclavation, while the sample treated with cefoxitin had low URC, suggesting inhibition of bacterial metabolic activities. OD and RNA concentrations were measured before autoclavation and antibiotic treatment. T: time in hours; OD: optical density; URC: unique read counts; ng/��L: RNA concentration. *, p

Additional file 11 Supplementary Figure 2. E. coli growth experiment. A Two replicates of the samples subjected to CiRNAseq shows the reproducibility of the technique by the number of unique read counts (URC) obtained in each replicate. B Mean of the replicates��� URC. C OD and RNA concentrations analyzed in time points five (growth), eight (autoclaved), and nine (antibiotic) were comparable with each other. However, as expected, the E. coli growth sample from time point eight had no URC after autoclavation, while the sample treated with cefoxitin had low URC, suggesting inhibition of bacterial metabolic activities. OD and RNA concentrations were measured before autoclavation and antibiotic treatment. T: time in hours; OD: optical density; URC: unique read counts; ng/��L: RNA concentration. *, p

Additional file 2. List of smMIPs and sequences used for CiRNAseq profiling of the cervicovaginal microbiome (.pdf). List of designed 30 smMIPs with ligation and extension arms that target 434 relevant microbes in the cervicovaginal microbiome at the DNA and RNA levels using CiRNAseq.