Metagenomics and marker gene approaches, coupled with high-throughput sequencing technologies, have revolutionized the field of microbial ecology. Metagenomics is a culture-independent method that allows the identification and characterization of organisms from all kinds of samples. Whole-genome shotgun sequencing analyses the total DNA of a chosen sample to determine the presence of micro-organisms from all domains of life and their genomic content. Importantly, the whole-genome shotgun sequencing approach reveals the genomic diversity present, but can also give insights into the functional potential of the micro-organisms identified. The marker gene approach is based on the sequencing of a specific gene region. It allows one to describe the microbial composition based on the taxonomic groups present in the sample. It is frequently used to analyse the biodiversity of microbial ecosystems. Despite its importance, the analysis of metagenomic sequencing and marker gene data is quite a challenge. Here we review the primary workflows and software used for both approaches and discuss the current challenges in the field.

Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses.

Rhodophyta (red algae) is one of three lineages of Archaeplastida1, a supergroup that is united by the primary endosymbiotic origin of plastids in eukaryotes2,3. Red algae are a diverse and species-rich group, members of which are typically photoautotrophic, but are united by a number of highly derived characteristics: they have relatively small intron-poor genomes, reduced metabolism and lack cytoskeletal structures that are associated with motility, flagella and centrioles. This suggests that marked gene loss occurred around their origin4; however, this is difficult to reconstruct because they differ so much from the other archaeplastid lineages, and the relationships between these lineages are unclear. Here we describe the novel eukaryotic phylum Rhodelphidia and, using phylogenomics, demonstrate that it is a closely related sister to red algae. However, the characteristics of the two Rhodelphis species described here are nearly opposite to those that define red algae: they are non-photosynthetic, flagellate predators with gene-rich genomes, along with a relic genome-lacking primary plastid that probably participates in haem synthesis. Overall, these findings alter our views of the origins of Rhodophyta, and Archaeplastida evolution as a whole, as they indicate that mixotrophic feeding-that is, a combination of predation and phototrophy-persisted well into the evolution of the group.

Non-photosynthetic predators are sister to red algae

Artificial intelligence for natural product drug discovery.

Shotgun metagenomics based on untargeted sequencing can explore the taxonomic profile and the function of unknown microorganisms in samples, and complement the shortage of amplicon sequencing. Binning assembled sequences into individual groups, which represent microbial genomes, is the key step and a major challenge in metagenomic research. Both supervised and unsupervised machine learning methods have been employed in binning. Genome binning belonging to unsupervised method clusters contigs into individual genome bins by machine learning methods without the assistance of any reference databases. So far a lot of genome binning tools have emerged. Evaluating these genome tools is of great significance to microbiological research. In this study, we evaluate 15 genome binning tools containing 12 original binning tools and 3 refining binning tools by comparing the performance of these tools on chicken gut metagenomic datasets and the first CAMI challenge datasets. For chicken gut metagenomic datasets, original genome binner MetaBat, Groopm2 and Autometa performed better than other original binner, and MetaWrap combined the binning results of them generated the most high-quality genome bins. For CAMI datasets, Groopm2 achieved the highest purity (> 0.9) with good completeness (> 0.8), and reconstructed the most high-quality genome bins among original genome binners. Compared with Groopm2, MetaBat2 had similar performance with higher completeness and lower purity. Genome refining binners DASTool predicated the most high-quality genome bins among all genomes binners. Most genome binner performed well for unique strains. Nonetheless, reconstructing common strains still is a substantial challenge for all genome binner. In conclusion, we tested a set of currently available, state-of-the-art metagenomics hybrid binning tools and provided a guide for selecting tools for metagenomic binning by comparing range of purity, completeness, adjusted rand index, and the number of high-quality reconstructed bins. Furthermore, available information for future binning strategy were concluded.

/pdf/evaluating-metagenomics-tools-for-genome-binning-with-real-1bmv918y81.pdf

Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets.

Symbiotic mutualisms of bacteria and animals are ubiquitous in nature, running a continuum from facultative to obligate from the perspectives of both partners. The loss of functions required for living independently but not within a host gives rise to reduced genomes in many symbionts. Although the phenomenon of genome reduction can be explained by existing evolutionary models, the initiation of the process is not well understood. Here, we describe the microbiome associated with the eggs of the beetle Lagria villosa, consisting of multiple bacterial symbionts related to Burkholderia gladioli, including a reduced-genome symbiont thought to be the exclusive producer of the defensive compound lagriamide. We show that the putative lagriamide-producing symbiont is the only member of the microbiome undergoing genome reduction and that it has already lost the majority of its primary metabolism and DNA repair pathways. The key step preceding genome reduction in the symbiont was likely the horizontal acquisition of the putative lagriamide lga biosynthetic gene cluster. Unexpectedly, we uncovered evidence of additional horizontal transfers to the symbiont's genome while genome reduction was occurring and despite a current lack of genes needed for homologous recombination. These gene gains may have given the genome-reduced symbiont a selective advantage in the microbiome, especially given the maintenance of the large lga gene cluster despite ongoing genome reduction.IMPORTANCE Associations between microorganisms and an animal, plant, or fungal host can result in increased dependence over time. This process is due partly to the bacterium not needing to produce nutrients that the host provides, leading to loss of genes that it would need to live independently and to a consequent reduction in genome size. It is often thought that genome reduction is aided by genetic isolation-bacteria that live in monocultures in special host organs, or inside host cells, have less access to other bacterial species from which they can obtain genes. Here, we describe exposure of a genome-reduced beetle symbiont to a community of related bacteria with nonreduced genomes. We show that the symbiont has acquired genes from other bacteria despite going through genome reduction, suggesting that isolation has not yet played a major role in this case of genome reduction, with horizontal gene gains still offering a potential route for adaptation.

Horizontal Gene Transfer to a Defensive Symbiont with a Reduced Genome in a Multipartite Beetle Microbiome.

Shotgun metagenomics is a powerful, high-resolution technique enabling the study of microbial communities in situ. However, species-level resolution is only achieved after a process of 'binning' where contigs predicted to originate from the same genome are clustered. Such culture-independent sequencing frequently unearths novel microbes, and so various methods have been devised for reference-free binning. As novel microbiomes of increasing complexity are explored, sometimes associated with non-model hosts, robust automated binning methods are required. Existing methods struggle with eukaryotic contamination and cannot handle highly complex single metagenomes. We therefore developed an automated binning pipeline, termed 'Autometa', to address these issues. This command-line application integrates sequence homology, nucleotide composition, coverage and the presence of single-copy marker genes to separate microbial genomes from non-model host genomes and other eukaryotic contaminants, before deconvoluting individual genomes from single metagenomes. The method is able to effectively separate over 1000 genomes from a metagenome, allowing the study of previously intractably complex environments at the level of single species. Autometa is freely available at https://bitbucket.org/jason_c_kwan/autometa and as a docker image at https://hub.docker.com/r/jasonkwan/autometa under the GNU Affero General Public License 3 (AGPL 3).

Autometa: automated extraction of microbial genomes from individual shotgun metagenomes.

Motivation: Shotgun metagenomics is a powerful, high-resolution technique enabling the study of microbial communities in situ. However, species-level resolution is only achieved after a process of "binning" where contigs predicted to originate from the same genome are clustered. Such culture-independent sequencing frequently unearths novel microbes, and so various methods have been devised for reference-free binning. Existing methods, however, suffer from: (1) reliance on human pattern recognition, which is inherently unscalable; (2) requirement for multiple co-assembled metagenomes, which degrades assembly quality due to strain variance; and (3) assumption of prior host genome removal not feasible for non-model hosts. We therefore devised a fully-automated pipeline, termed "Autometa," to address these issues. 
Results: Autometa implements a method for taxonomic partitioning of contigs based on predicted protein homology, and this was shown to vastly improve binning in host-associated and complex metagenomes. Autometa9s method of automated clustering, based on Barnes-Hut Stochastic Neighbor Embedding (BH-tSNE) and DBSCAN, was shown to be highly scalable, outperforming other binning pipelines in complex simulated datasets.
Availability and implementation: Autometa is freely available at https://bitbucket.org/jason_c_kwan/autometa and as a docker image at https://hub.docker.com/r/jasonkwan/autometa under the GNU Affero General Public License 3 (AGPL 3).

https://www.biorxiv.org/content/biorxiv/early/2018/01/22/251462.full.pdf

Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes

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Autometa: automated extraction of microbial genomes from individual shotgun metagenomes.

Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes