VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data

Prokaryotic Population Dynamics and Viral Predation in a Marine Succession Experiment Using Metagenomics

We performed an incubation experiment of seawater confined in plastic bottles with samples collected at three depths (15, 60, and 90 m) after retrieval from a single offshore location in the Mediterranean Sea, from a late summer stratified water column. Two samples representative of each depth were collected and stored in opaque bottles after two periods of 7 h. We took advantage of the "bottle effect" to investigate changes in the natural microbial communities (abundant and rare). We recovered 94 metagenome-assembled genomes (MAGs) and 1089 metagenomic viral contigs and examined their abundance using metagenomic recruitment. We detected a significant fast growth of copiotrophic bacteria such as Alteromonas or Erythrobacter throughout the entire water column with different dynamics that we assign to "clonal," "polyclonal," or "multispecies" depending on the recruitment pattern. Results also showed a marked ecotype succession in the phototropic picocyanobacteria that were able to grow at all the depths in the absence of light, highlighting the importance of their mixotrophic potential. In addition, "wall-chain-reaction" hypothesis based on the study of phage-host dynamics showed the higher impact of viral predation on archaea in deeper waters, evidencing their prominent role during incubations. Our results provide a step forward in understanding the mechanisms underlying dynamic patterns and ecology of the marine microbiome and the importance of processing the samples immediately after collection to avoid changes in the community structure.

The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes

Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.

The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes

Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.