Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today's indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.

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).

Bacteriophages (phages), or bacterial viruses, are the most abundant and diverse biological entities that impact the global ecosystem. Recent advances in metagenomics have revealed their rampant abundance in the biosphere. A fundamental aspect of bacteriophages that remains unexplored in metagenomic data is the process of recombination as a driving force in evolution that occurs among different viruses within the same bacterial host. Here, we systematically examined signatures of recombination in every gene from 211 species-level viral groups in a recently obtained dataset of the Earth's virome that contain corresponding information on the host bacterial species. Our study revealed that signatures of recombination are widespread (84%) among the diverse viral groups. We identified 25 recombination-intense viral groups, widely distributed across the viral taxonomy, and present in bacterial species living in the human oral cavity. We also revealed a significant inverse association between the recombination-intense viral groups and Type II restriction endonucleases, that could be effective in reducing recombination among phages in a cell. Furthermore, we identified recombination-intense genes that are significantly enriched for encoding phage morphogenesis proteins. Changes in the viral genomic sequence by recombination may be important to escape cleavage by the host bacterial immune systems.

CRISPR-Cas9 is a powerful technology that has enabled genome editing in a wide range of species. However, the currently developed Cas9 homologs all originate from mesophilic bacteria, making them susceptible to degradation and unsuitable for applications requiring cleavage at elevated temperatures. Here, we show that the Cas9 protein from the thermophilic bacterium Geobacillus stearothermophilus (GeoCas9) catalyzes RNA-guided DNA cleavage at elevated temperatures. GeoCas9 is active at temperatures up to 70 °C, compared to 45 °C for Streptococcus pyogenes Cas9 (SpyCas9), which expands the temperature range for CRISPR-Cas9 applications. We also found that GeoCas9 is an effective tool for editing mammalian genomes when delivered as a ribonucleoprotein (RNP) complex. Together with an increased lifetime in human plasma, the thermostable GeoCas9 provides the foundation for improved RNP delivery in vivo and expands the temperature range of CRISPR-Cas9.

Motivation

Two key steps in the analysis of uncultured viruses recovered from metagenomes are the taxonomic classification of the viral sequences and the identification of putative host(s). Both steps rely mainly on the assignment of viral proteins to orthologs in cultivated viruses. Viral Protein Families (VPFs) can be used for the robust identification of new viral sequences in large metagenomics datasets. Despite the importance of VPF information for viral discovery, VPFs have not yet been explored for determining viral taxonomy and host targets.

Results

In this work, we classified the set of VPFs from the IMG/VR database and developed VPF-Class. VPF-Class is a tool that automates the taxonomic classification and host prediction of viral contigs based on the assignment of their proteins to a set of classified VPFs. Applying VPF-Class on 731K uncultivated virus contigs from the IMG/VR database, we were able to classify 363K contigs at the genus level and predict the host of over 461K contigs. In the RefSeq database, VPF-class reported an accuracy of nearly 100% to classify dsDNA, ssDNA and retroviruses, at the genus level, considering a membership ratio and a confidence score of 0.2. The accuracy in host prediction was 86.4%, also at the genus level, considering a membership ratio of 0.3 and a confidence score of 0.5. And, in the prophages dataset, the accuracy in host prediction was 86% considering a membership ratio of 0.6 and a confidence score of 0.8. Moreover, from the Global Ocean Virome dataset, over 817K viral contigs out of 1 million were classified.

Availability and implementation

The implementation of VPF-Class can be downloaded from https://github.com/biocom-uib/vpf-tools.

Supplementary information

Supplementary data are available at Bioinformatics online.

Unlabelled

Many bacteria rely on CRISPR-Cas systems to provide adaptive immunity against phages, predation by which can shape the ecology and functioning of microbial communities. To characterize the impact of CRISPR immunization on phage genome evolution, we performed long-term bacterium-phage (Streptococcus thermophilus-phage 2972) coevolution experiments. We found that in this species, CRISPR immunity drives fixation of single nucleotide polymorphisms that accumulate exclusively in phage genome regions targeted by CRISPR. Mutation rates in phage genomes highly exceed those of the host. The presence of multiple phages increased phage persistence by enabling recombination-based formation of chimeric phage genomes in which sequences heavily targeted by CRISPR were replaced. Collectively, our results establish CRISPR-Cas adaptive immunity as a key driver of phage genome evolution under the conditions studied and highlight the importance of multiple coexisting phages for persistence in natural systems.

Importance

Phages remain an enigmatic part of the biosphere. As predators, they challenge the survival of host bacteria and archaea and set off an "arms race" involving host immunization countered by phage mutation. The CRISPR-Cas system is adaptive: by capturing fragments of a phage genome upon exposure, the host is positioned to counteract future infections. To investigate this process, we initiated massive deep-sequencing experiments with a host and infective phage and tracked the coevolution of both populations over hundreds of days. In the present study, we found that CRISPR immunity drives the accumulation of phage genome rearrangements (which enable longer phage survival) and escape mutations, establishing CRISPR as one of the fundamental drivers of phage evolution.

The dysregulation of intestinal microbial communities is associated with inflammatory bowel diseases (IBD). Studies aimed at understanding the contribution of the microbiota to inflammatory diseases have primarily focused on bacteria, yet the intestine harbours a viral component dominated by prokaryotic viruses known as bacteriophages (phages). Phage numbers are elevated at the intestinal mucosal surface and phages increase in abundance during IBD, suggesting that phages play an unidentified role in IBD. We used a sequence-independent approach for the selection of viral contigs and then applied quantitative metagenomics to study intestinal phages in a mouse model of colitis. We discovered that during colitis the intestinal phage population is altered and transitions from an ordered state to a stochastic dysbiosis. We identified phages specific to pathobiotic hosts associated with intestinal disease, whose abundances are altered during colitis. Additionally, phage populations in healthy and diseased mice overlapped with phages from healthy humans and humans with IBD. Our findings indicate that intestinal phage communities are altered during inflammatory disease, establishing a platform for investigating phage involvement in IBD.

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Historically neglected by microbial ecologists, soil viruses are now thought to be critical to global biogeochemical cycles. However, our understanding of their global distribution, activities and interactions with the soil microbiome remains limited. Here we present the Global Soil Virus Atlas, a comprehensive dataset compiled from 2,953 previously sequenced soil metagenomes and composed of 616,935 uncultivated viral genomes and 38,508 unique viral operational taxonomic units. Rarefaction curves from the Global Soil Virus Atlas indicate that most soil viral diversity remains unexplored, further underscored by high spatial turnover and low rates of shared viral operational taxonomic units across samples. By examining genes associated with biogeochemical functions, we also demonstrate the viral potential to impact soil carbon and nutrient cycling. This study represents an extensive characterization of soil viral diversity and provides a foundation for developing testable hypotheses regarding the role of the virosphere in the soil microbiome and global biogeochemistry.