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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 (January 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. HiFine: integrating Hi-C-based and shotgun-based methods to refine binning of metagenomic contigs

   https://doi.org/10.1093/bioinformatics/btac295  

   Du, Yuxuan; Sun, Fengzhu; Birol, ed., Inanc (April 2022, Bioinformatics)

   Abstract MotivationMetagenomic binning aims to retrieve microbial genomes directly from ecosystems by clustering metagenomic contigs assembled from short reads into draft genomic bins. Traditional shotgun-based binning methods depend on the contigs’ composition and abundance profiles and are impaired by the paucity of enough samples to construct reliable co-abundance profiles. When applied to a single sample, shotgun-based binning methods struggle to distinguish closely related species only using composition information. As an alternative binning approach, Hi-C-based binning employs metagenomic Hi-C technique to measure the proximity contacts between metagenomic fragments. However, spurious inter-species Hi-C contacts inevitably generated by incorrect ligations of DNA fragments between species link the contigs from varying genomes, weakening the purity of final draft genomic bins. Therefore, it is imperative to develop a binning pipeline to overcome the shortcomings of both types of binning methods on a single sample. ResultsWe develop HiFine, a novel binning pipeline to refine the binning results of metagenomic contigs by integrating both Hi-C-based and shotgun-based binning tools. HiFine designs a strategy of fragmentation for the original bin sets derived from the Hi-C-based and shotgun-based binning methods, which considerably increases the purity of initial bins, followed by merging fragmented bins and recruiting unbinned contigs. We demonstrate that HiFine significantly improves the existing binning results of both types of binning methods and achieves better performance in constructing species genomes on publicly available datasets. To the best of our knowledge, HiFine is the first pipeline to integrate different types of tools for the binning of metagenomic contigs. Availability and implementationHiFine is available at https://github.com/dyxstat/HiFine. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. 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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4. 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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5. 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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