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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.

 

The Pastaza‐Marañón Foreland Basin (PMFB) holds the most extensive tropical peatland area in South America. PMFB peatlands store ~7.07 Gt of organic carbon interacting with multiple microbial heterotrophic, methanogenic, and other aerobic/anaerobic respirations. Little is understood about the contribution of distinct microbial community members inhabiting tropical peatlands. Here, we studied the metagenomes of three geochemically distinct peatlands spanning minerotrophic, mixed, and ombrotrophic conditions. Using gene‐ and genome‐centric approaches, we evaluate the functional potential of the underlying microbial communities. Abundance analyses show significant differences in C, N, P, and S acquisition genes. Furthermore, community interactions mediated by toxin–antitoxin and CRISPR‐Cas systems were enriched in oligotrophic soils, suggesting that non‐metabolic interactions may exert additional controls in low‐nutrient environments. Additionally, we reconstructed 519 metagenome‐assembled genomes spanning 28 phyla. Our analyses detail key differences across the geochemical gradient in the predicted microbial populations involved in degradation of organic matter, and the cycling of N and S. Notably, we observed differences in the nitric oxide (NO) reduction strategies between sites with high and low N₂O fluxes and found phyla putatively capable of both NO and sulfate reduction. Our findings detail how gene abundances and microbial populations are influenced by geochemical differences in tropical peatlands.

 

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyze 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical, and gene neighborhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter. 

Amazonian peatlands store a large amount of soil organic carbon (SOC), and its fate under a future changing climate is unknown. Here, we use a process-based peatland biogeochemistry model to quantify the carbon accumulation for peatland and nonpeatland ecosystems in the Pastaza-Marañon foreland basin (PMFB) in the Peruvian Amazon from 12,000 y before present to AD 2100. Model simulations indicate that warming accelerates peat SOC loss, while increasing precipitation accelerates peat SOC accumulation at millennial time scales. The uncertain parameters and spatial variation of climate are significant sources of uncertainty to modeled peat carbon accumulation. Under warmer and presumably wetter conditions over the 21st century, SOC accumulation rate in the PMFB slows down to 7.9 (4.3–12.2) g⋅C⋅m⁻²⋅y⁻¹from the current rate of 16.1 (9.1–23.7) g⋅C⋅m⁻²⋅y⁻¹, and the region may turn into a carbon source to the atmosphere at −53.3 (−66.8 to −41.2) g⋅C⋅m⁻²⋅y⁻¹(negative indicates source), depending on the level of warming. Peatland ecosystems show a higher vulnerability than nonpeatland ecosystems, as indicated by the ratio of their soil carbon density changes (ranging from 3.9 to 5.8). This is primarily due to larger peatlands carbon stocks and more dramatic responses of their aerobic and anaerobic decompositions in comparison with nonpeatland ecosystems under future climate conditions. Peatland and nonpeatland soils in the PMFB may lose up to 0.4 (0.32–0.52) Pg⋅C by AD 2100 with the largest loss from palm swamp. The carbon-dense Amazonian peatland may switch from a current carbon sink into a source in the 21st century.