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1. ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data

   https://doi.org/10.1038/s41467-023-35945-y  

   Du, Yuxuan; Fuhrman, Jed A.; Sun, Fengzhu (December 2023, Nature Communications)

   Abstract The introduction of high-throughput chromosome conformation capture (Hi-C) into metagenomics enables reconstructing high-quality metagenome-assembled genomes (MAGs) from microbial communities. Despite recent advances in recovering eukaryotic, bacterial, and archaeal genomes using Hi-C contact maps, few of Hi-C-based methods are designed to retrieve viral genomes. Here we introduce ViralCC, a publicly available tool to recover complete viral genomes and detect virus-host pairs using Hi-C data. Compared to other Hi-C-based methods, ViralCC leverages the virus-host proximity structure as a complementary information source for the Hi-C interactions. Using mock and real metagenomic Hi-C datasets from several different microbial ecosystems, including the human gut, cow fecal, and wastewater, we demonstrate that ViralCC outperforms existing Hi-C-based binning methods as well as state-of-the-art tools specifically dedicated to metagenomic viral binning. ViralCC can also reveal the taxonomic structure of viruses and virus-host pairs in microbial communities. When applied to a real wastewater metagenomic Hi-C dataset, ViralCC constructs a phage-host network, which is further validated using CRISPR spacer analyses. ViralCC is an open-source pipeline available athttps://github.com/dyxstat/ViralCC. 

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2. CoverM: read alignment statistics for metagenomics

   https://doi.org/10.1093/bioinformatics/btaf147  

   Aroney, Samuel_T N; Newell, Rhys_J P; Nissen, Jakob N; Camargo, Antonio Pedro; Tyson, Gene W; Woodcroft, Ben J (March 2025, Bioinformatics)

   Alkan, Can (Ed.)

   Abstract SummaryGenome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses “Mosdepth arrays” for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results. Availability and implementationCoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces. 

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3. Kairos infers in situ horizontal gene transfer in longitudinally sampled microbiomes through microdiversity-aware sequence analysis

   https://doi.org/10.1101/2023.10.24.563791  

   Brown, Connor L; Cheung, Yat Fei; Song, Haoqiu; Snead, Delaney; Vikesland, Peter; Pruden, Amy; Zhang, Liqing (October 2023, bioRxiv)

   Abstract Horizontal gene transfer (HGT) occurring within microbiomes is linked to complex environmental and ecological dynamics that are challenging to replicate in controlled settings. Consequently, most extant studies of microbiome HGT are either simplistic experimental settings with tenuous relevance to real microbiomes or correlative studies that assume that HGT potential is a function of the relative abundance of mobile genetic elements (MGEs), the vehicles of HGT. Here we introduce Kairos as a bioinformatic tool deployed in nextflow for detecting HGT events “in situ,” i.e., within a microbiome, through analysis of time-series metagenomic sequencing data. Thein-situframework proposed here leverages available metagenomic data from a longitudinally sampled microbiome to assess whether the chronological occurrence of potential donors, recipients, and putatively transferred regions could plausibly have arisen due to HGT over a range of defined time periods. The centerpiece of the Kairos workflow is a novel competitive read alignment method that enables discernment of even very similar genomic sequences, such as those produced by MGE-associated recombination. A key advantage of Kairos is its reliance on assemblies rather than metagenome assembled genomes (MAGs), which avoids systematic exclusion of accessory genes associated with the binning process. In an example test-case of real world data, use of assemblies directly produced a 264-fold increase in the number of antibiotic resistance genes included in the analysis of HGT compared to analysis of MAGs with MetaCHIP. Further,in silicoevaluation of contig taxonomy was performed to assess the accuracy of classification for both chromosomally- and MGE-derived sequences, indicating a high degree of accuracy even for conjugative plasmids up to the level of class or order. Thus, Kairos enables the analysis of very recent HGT events, making it suitable for studying rapid prokaryotic adaptation in environmental systems without disturbing the ornate ecological dynamics associated with microbiomes. Current versions of the Kairos workflow are available here:https://github.com/clb21565/kairos. 

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4. Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton

   https://doi.org/10.1128/mbio.01676-23  

   Alexander, Harriet; Hu, Sarah K.; Krinos, Arianna I.; Pachiadaki, Maria; Tully, Benjamin J.; Neely, Christopher J.; Reiter, Taylor (December 2023, mBio)

   Fraser, Claire M. (Ed.)

   ABSTRACT Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are neither designed to consider eukaryotes nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome-assembled genomes (MAGs) from large-scale metagenomic sequence data sets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8–2,000 µm) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand upon the phylogenetic diversity of likely particle- and host-associated taxa. We use these MAGs to demonstrate an approach to infer the putative trophic mode of the recovered eukaryotic MAGs. We also identify ecological cohorts of co-occurring MAGs, which are driven by specific environmental factors and putative host-microbe associations. These data together add to a number of growing resources of environmentally relevant eukaryotic genomic information. Complementary and expanded databases of MAGs, such as those provided through scalable pipelines like EukHeist, stand to advance our understanding of eukaryotic diversity through increased coverage of genomic representatives across the tree of life. IMPORTANCESingle-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers’ efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity. 

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5. ImputeCC enhances integrative Hi-C-based metagenomic binning through constrained random-walk-based imputation

   Du, Yuxuan; Zuo, Wenxuan; Sun, Fengzhu (May 2024, Springer Nature)

   Ma, Jian (Ed.)

   Metagenomic Hi-C (metaHi-C) enables the recognition of relationships between contigs in terms of their physical proximity within the same cell, facilitating the reconstruction of high-quality metagenomeassembled genomes (MAGs) from complex microbial communities. However, current Hi-C-based contig binning methods solely depend on Hi-C interactions between contigs to group them, ignoring invaluable biological information, including the presence of single-copy marker genes. Here, we introduce ImputeCC, an integrative contig binning tool tailored for metaHi-C datasets. ImputeCC integrates Hi-C interactions with the inherent discriminative power of single-copy marker genes, initially clustering them as preliminary bins, and develops a new constrained random walk with restart (CRWR) algorithm to improve Hi-C connectivity among these contigs. Extensive evaluations on mock and real metaHi-C datasets from diverse environments, including the human gut, wastewater, cow rumen, and sheep gut, demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. ImputeCC’s genuslevel analysis of the sheep gut microbiota further reveals its ability and potential to recover essential species from dominant genera such as Bacteroides, detect previously unrecognized genera, and shed light on the characteristics and functional roles of genera such as Alistipes within the sheep gut ecosystem. Availability: ImputeCC is implemented in Python and available at https://github.com/dyxstat/ImputeCC. The Supplementary Information is available at https://doi.org/10.5281/zenodo.10776604. 

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