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1. BinaRena: a dedicated interactive platform for human-guided exploration and binning of metagenomes

   https://doi.org/10.1186/s40168-023-01625-8  

   Pavia, Michael J. ; Chede, Abhinav ; Wu, Zijun ; Cadillo-Quiroz, Hinsby ; Zhu, Qiyun ( August 2023 , Microbiome)

   Exploring metagenomic contigs and “binning” them into metagenome-assembled genomes (MAGs) are essential for the delineation of functional and evolutionary guilds within microbial communities. Despite the advances in automated binning algorithms, their capabilities in recovering MAGs with accuracy and biological relevance are so far limited. Researchers often find that human involvement is necessary to achieve representative binning results. This manual process however is expertise demanding and labor intensive, and it deserves to be supported by software infrastructure.

   We present BinaRena, a comprehensive and versatile graphic interface dedicated to aiding human operators to explore metagenome assemblies via customizable visualization and to associate contigs with bins. Contigs are rendered as an interactive scatter plot based on various data types, including sequence metrics, coverage profiles, taxonomic assignments, and functional annotations. Various contig-level operations are permitted, such as selection, masking, highlighting, focusing, and searching. Binning plans can be conveniently edited, inspected, and compared visually or using metrics including silhouette coefficient and adjusted Rand index. Completeness and contamination of user-selected contigs can be calculated in real time.

   In demonstration of BinaRena’s usability, we show that it facilitated biological pattern discovery, hypothesis generation, and bin refinement in a complex tropical peatland metagenome. It enabled isolation of pathogenic genomes within closely related populations from the gut microbiota of diarrheal human subjects. It significantly improved overall binning quality after curating results of automated binners using a simulated marine dataset.

   BinaRena is an installation-free, dependency-free, client-end web application that operates directly in any modern web browser, facilitating ease of deployment and accessibility for researchers of all skill levels. The program is hosted athttps://github.com/qiyunlab/binarena, together with documentation, tutorials, example data, and a live demo. It effectively supports human researchers in intuitive interpretation and fine tuning of metagenomic data.

    

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2. 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.)

   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.

   Single-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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3. 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)

   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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4. MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data

   https://doi.org/10.1038/s41467-023-41209-6  

   Du, Yuxuan ; Sun, Fengzhu ( October 2023 , Nature Communications)

   Metagenomic Hi-C (metaHi-C) can identify contig-to-contig relationships with respect to their proximity within the same physical cell. Shotgun libraries in metaHi-C experiments can be constructed by next-generation sequencing (short-read metaHi-C) or more recent third-generation sequencing (long-read metaHi-C). However, all existing metaHi-C analysis methods are developed and benchmarked on short-read metaHi-C datasets and there exists much room for improvement in terms of more scalable and stable analyses, especially for long-read metaHi-C data. Here we report MetaCC, an efficient and integrative framework for analyzing both short-read and long-read metaHi-C datasets. MetaCC outperforms existing methods on normalization and binning. In particular, the MetaCC normalization module, named NormCC, is more than 3000 times faster than the current state-of-the-art method HiCzin on a complex wastewater dataset. When applied to one sheep gut long-read metaHi-C dataset, MetaCC binning module can retrieve 709 high-quality genomes with the largest species diversity using one single sample, including an expansion of five uncultured members from the orderErysipelotrichales, and is the only binner that can recover the genome of one important speciesBacteroides vulgatus. Further plasmid analyses reveal that MetaCC binning is able to capture multi-copy plasmids.

    

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5. MetaBMF: a scalable binning algorithm for large-scale reference-free metagenomic studies

   https://doi.org/10.1093/bioinformatics/btz577  

   Ma, Terry ; Xiao, Di ; Xing, Xin ; Birol, ed., Inanc ( July 2019 , Bioinformatics)

   Metagenomics studies microbial genomes in an ecosystem such as the gastrointestinal tract of a human. Identification of novel microbial species and quantification of their distributional variations among different samples that are sequenced using next-generation-sequencing technology hold the key to the success of most metagenomic studies. To achieve these goals, we propose a simple yet powerful metagenomic binning method, MetaBMF. The method does not require prior knowledge of reference genomes and produces highly accurate results, even at a strain level. Thus, it can be broadly used to identify disease-related microbial organisms that are not well-studied.

   Mathematically, we count the number of mapped reads on each assembled genomic fragment cross different samples as our input matrix and propose a scalable stratified angle regression algorithm to factorize this count matrix into a product of a binary matrix and a nonnegative matrix. The binary matrix can be used to separate microbial species and the nonnegative matrix quantifies the species distributions in different samples. In simulation and empirical studies, we demonstrate that MetaBMF has a high binning accuracy. It can not only bin DNA fragments accurately at a species level but also at a strain level. As shown in our example, we can accurately identify the Shiga-toxigenic Escherichia coli O104: H4 strain which led to the 2011 German E.coli outbreak. Our efforts in these areas should lead to (i) fundamental advances in metagenomic binning, (ii) development and refinement of technology for the rapid identification and quantification of microbial distributions and (iii) finding of potential probiotics or reliable pathogenic bacterial strains.

   The software is available at https://github.com/didi10384/MetaBMF.

    

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