Biology subjects

Villada, J. C.

Publications and source records attributed to Villada, J. C..

10 recordsLinked to original sources

bioRxiv2026

GradeBins: a comprehensive framework to augment metagenomic bin quality control

1.Metagenomic binning and single-cell assembly produce draft genomes whose completeness and contamination vary with experimental and computational choices. Comparing whole bin sets remains difficult because most quality assessment tools report per-bin metrics and operate either with ground truth labels or with inference estimates. GradeBins evaluates complete bin sets under two execution modes while producing matched per-bin and bin-set summaries. For real metagenomes, inference mode integrates bin statistics, mapping depth, taxonomy, and external quality estimates from tools such as CheckM2 and EukCC to standardize per-bin and bin-set quality reporting across Bacteria, Archaea, and Eukaryotes. For synthetic or otherwise labeled datasets, ground truth mode computes base-resolved completeness, contamination, and misbinning from labeled contigs or CAMI mappings, enabling objective benchmarking of binners, parameter choices, and experimental conditions, and calibration of inference-based estimates. Across synthetic metagenomes of 10, 50, 100, 500 and 1,000 Bacteria and Archaea, and a mixed metagenome containing also Eukaryotes, GradeBins separated binner and parameter effects using Total Score and a quality-weighted bin count, together with quality tier distributions, recovery fractions, and label-aware diagnostics. Inference-mode completeness generally tracked ground truth, whereas contamination and clean-bin rates showed mode-dependent shifts that were most pronounced in the mixed community. GradeBins added low overhead in these benchmarks, with peak memory below 8 GB and runtimes typically below 30 seconds. GradeBins enables reproducible protocol comparison, regression testing, and consistent quality reporting for genome-resolved metagenomics in both benchmarking and real-data settings. The full software package is open-source and available for download at https://bbmap.org/tools/gradebins.

microbiology↗

bioRxiv2026

Deployable high-fidelity metagenome binning at scale with QuickBin

1.Reconstructing genomes from metagenomic assemblies is foundational to microbiome research, yet metagenome binning remains constrained by a persistent trade-off between genome fidelity and deployable throughput. Many high-accuracy approaches rely on GPU-intensive workflows, marker-gene-informed postprocessing, or resource demands that limit reproducible use at catalog scale. Here we present QuickBin, a CPU-native, marker-free binning algorithm designed to recover near-complete, ultra-low-contamination metagenome-assembled genomes (MAGs) under practical compute constraints. QuickBin couples a GC-coverage spatial index (BinMap) with a hierarchical, early-exit Oracle cascade of similarity tests (scalar composition/coverage filters followed by SIMD-accelerated k-mer comparisons), invoking a compact neural network only for the small fraction of ambiguous candidate merges. Across synthetic communities evaluated by both marker-based estimates and contig-origin ground truth, QuickBin yields high recovery while maximizing the fraction of sequence assigned to uncontaminated bins. In a benchmark of 297 diverse real metagenomes, QuickBin completed all runs and recovered more MAGs meeting [≥]95% completeness and [≤]1% contamination than widely used CPU binners and compute-intensive alternatives that frequently failed to finish under standardized limits. QuickBin provides a practical path to reproducible, high-fidelity genome-resolved metagenomics at scale for downstream comparative analyses and data products that depend on low-artifact genome reconstructions. The full software package is open-source and available for download at https://bbmap.org, GitHub repo (https://github.com/bbushnell/BBTools), or Docker container (https://hub.docker.com/r/bryce911/bbtools).

microbiology↗

bioRxiv2025

Genome-resolved expansion of Nucleocytoviricota and Mirusviricota reveals new diversity, functional potential, and biotechnological applications

Nucleocytoplasmic large DNA viruses (NCLDV) of the phyla Nucleocytoviricota and viruses of the newly proposed Duplodnaviria phylum, Mirusviricota, exhibit taxonomic richness which continues to expand due to metagenomic sequencing of Earths biomes. Giant viruses contain complex genomes encoding genes of both viral and cellular origin, representing a reservoir of unexplored biological functions with potential implications for ecology, evolution, and biotechnology. Here, we present the largest curated database of giant virus metagenome-assembled genomes (GVMAGs V2), comprising 8,508 species-level clusters inferred from 18,727 genomes, originating from marine, freshwater, anthropogenic and terrestrial environments, a six-fold increase from the previous giant virus phylogenetic frameworks. Phylogenomics and relative evolutionary distance analysis revealed 712 novel genera, 13 previously unknown viral families and a new proposed order, tentatively named Mycodnavirales. We improved gene calling of 12% of giant virus genomes by accounting for alternative and custom genetic codes, enabling more accurate identification of protein-coding genes. Database mining uncovered endogenous viral elements in a broad spectrum of eukaryotes, spanning algae, fungi, and parasitic protists highlighting that giant virus integration is both widespread and evolutionarily persistent. Orthologous clustering of 2.5 million proteins identified 135,998 orthogroups representing comprehensive metabolic capabilities, such as enrichment of genes involved in aromatic compound degradation (commonly associated with bioremediation) in Algavirales genomes. Furthermore, we detected widespread biosynthetic gene clusters underpinning antimicrobial activity and antibiotic resistance, suggesting roles of giant viruses in host defense and in the dissemination of antibiotic resistance genes. Conversely, 67% of orthogroups have unknown functions, underscoring a substantial unexplored potential. This comprehensive publicly available database provides a critical resource for the giant virus research community and a foundation for uncovering virus-host interactions, exploring viral evolution, and identifying reservoirs for novel enzymes with the potential to advance biotechnological applications.

microbiology↗

bioRxiv2025

Rapid terabase-scale simulation of realistic metagenomes for experimental design and pathogen detection with RandomReadsMG

1.Accurate simulation of metagenomic sequencing data is determinant for benchmarking new algorithms, guiding experimental design and generation of synthetic data to develop and train models for AI/ML. However, existing simulators often struggle to reproduce the uneven coverage depth patterns seen in real microbial communities, can be difficult to install, or incur long runtimes. Here we present RandomReadsMG, a fast and scalable read simulator designed to generate synthetic metagenomes with realistic, user-defined depth distributions from hundreds of genomes in a single command. The tool supports multiple abundance models and intra-genome depth variability to better mimic real data. Benchmarks show RandomReadsMG runs orders of magnitude faster than comparable software while maintaining constant memory usage for input datasets of unbounded size. We demonstrate its utility by determining pathogen detection thresholds in a complex microbiome, showcasing its value for optimizing experimental design and creating robust training data sets for bioinformatics and AI/ML. RandomReadsMG is open-source software, distributed as part of the BBTools suite. The full software package is available for download at https://sourceforge.net/projects/bbmap/. For containerized deployment, a Docker image is also available from Docker Hub at https://hub.docker.com/r/bryce911/bbtools.

microbiology↗

bioRxiv2025

A genomic catalog of Earth's bacterial and archaeal symbionts

1Microbial symbiosis drives the functional and phylogenomic diversification of life on Earth, yet remains underexplored due to culturing challenges. This study employed machine learning (ML) to predict symbiotic lifestyles in hundreds of thousands of microbial genomes from diverse environmental metagenome samples and reference genomes. Predictions were performed using symclatron, a novel ML framework developed here to identify genomic signatures of symbionts. Predictions were deposited in a novel Symbiont Genomes catalog (SymGs). The results indicated that 15-23% of uncultivated microbes likely engage in symbiotic relationships with other organisms and are present in half of all known bacterial and archaeal phyla. We also identified genomic signatures of symbiotic lifestyles, including the loss of certain metabolic functions and the differential presence of others that may enable host-dependent living. The symclatron software and the SymGs catalog represent valuable resources for studying symbioses, facilitating future mechanistic investigations of host-microbe associations.

genomics↗

bioRxiv2025

Global Metagenomics Reveals Plastid Diversity and Unexplored Algal Lineages

Photosynthetic organelles in eukaryotes originated through primary endosymbiosis with a cyanobacterium, an event that profoundly shaped the evolutionary landscape of the eukaryotic tree of life. Primary plastids in Archaeplastida, especially in cultivable plants and algae, contribute most to known plastid diversity. Secondary and higher-order endosymbiosis, involving eukaryotic hosts and algal endosymbionts, further spread photosynthesis among protists within the CASH lineages (Cryptophyta, Alveolata, Stramenopila, and Haptophyta). Despite various hypotheses explaining secondary plastid evolution and distribution, empirical support remains limited. Here, we employ cultivation-independent global metagenomics to expand plastid diversity and investigate plastid origins. We captured 1,027 plastid sequences, including 300 novel sequences belonging to previously unsequenced plastids and representing yet-to-be described microeukaryotes. This includes a new lineage that offers insights into plastid evolution in haptophytes and cryptophytes. Our results confirm that Archaeplastida plastids originated from an early-branching cyanobacterial lineage closely related to Gloeomargaritales and identify the closest extant relative of Paulinella plastids. Additionally, our findings suggest two independent origins of secondary red algal plastids, contributing to plastid diversity in CASH lineages and challenging the prevailing model of single secondary plastid origin. Our study highlights the importance of metagenomic data in uncovering biological diversity and advancing understanding of plastid relationships across photosynthetic eukaryotes.

evolutionary biology↗

bioRxiv2024

Proposal of Patescibacterium danicum gen. nov., sp. nov. in the ubiquitous ultrasmall bacterial phylum Patescibacteriota phyl. nov.

Candidatus Patescibacteria is a diverse bacterial phylum that is notable for members with ultrasmall cell size, reduced genomes, limited metabolic capabilities and dependence on other prokaryotic hosts. Despite the prevalence of the name Ca. Patescibacteria in the scientific literature, it is not officially recognized under the International Code of Nomenclature of Prokaryotes (ICNP) and lacks a nomenclatural type. Here, we rectify this situation by describing two closely related circular metagenome-assembled genomes (MAGs) and by proposing one of them (ABY1TS) to serve as the nomenclatural type for the species Patescibacterium danicumTS gen. nov., sp. nov. according to the rules of the SeqCode. Rank-normalized phylogenomic inference confirmed the stable placement of P. danicumTS in the class ABY1 within Ca. Patescibacteria. Based on these results, we propose Patescibacterium gen. nov. to serve as the type genus for associated higher taxa, including the phylum Patescibacteriota phyl. nov. We complement our proposal with a genomic characterization, metabolic reconstruction, and biogeographical analysis of Patescibacterium. Our results confirm small genome sizes (< 1Mbp), low GC content (>36%), and the occurrence of long gene coding insertions in the 23S rRNA sequences, along with reduced metabolic potential, inferred symbiotic lifestyle, and a global distribution. In summary, this effort, focused on the fourth-largest phylum in the bacterial domain, aims to provide nomenclatural stability in the scientific literature.

microbiology↗

bioRxiv2021

Integrative genome-scale metabolic modeling reveals versatile metabolic strategies for methane utilization in Methylomicrobium album BG8

Methylomicrobium album BG8 is an aerobic methanotrophic bacterium that can mitigate environmental methane emission, and is a promising microbial cell factory for the conversion of methane to value-added chemicals. However, the lack of a genome-scale metabolic model (GEM) of M. album BG8 has hindered the development of systems biology and metabolic engineering of this methanotroph. To fill this gap, a high-quality GEM was constructed to facilitate a system-level understanding on the biochemistry of M. album BG8. Next, experimental time-series growth and exometabolomics data were integrated into the model to generate context-specific GEMs. Flux balance analysis (FBA) constrained with experimental data derived from varying levels of methane, oxygen, and biomass were used to model the metabolism of M. album BG8 and investigate the metabolic states that promote the production of biomass and the excretion of carbon dioxide, formate, and acetate. The experimental and modeling results indicated that the system-level metabolic functions of M. album BG8 require a ratio > 1:1 between the oxygen and methane specific uptake rates for optimal growth. Integrative modeling revealed that at a high ratio of oxygen-to-methane uptake flux, carbon dioxide and formate were the preferred excreted compounds; at lower ratios, however, acetate accounted for a larger fraction of the total excreted flux. The results of this study reveal a trade-off between biomass production and organic compound excretion and provide evidence that this trade-off is linked to the ratio between the oxygen and methane specific uptake rates.

microbiology↗

bioRxiv2019

Codon usage bias creates a ramp of hydrogen bonding at the 5′-end in prokaryotic ORFeomes

Codon usage bias exerts control over a wide variety of molecular processes. The positioning of synonymous codons within coding sequences (CDSs) dictates protein expression by mechanisms such as local translation efficiency, mRNA Gibbs free energy, and protein co-translational folding. In this work, we explore how codon variants affect the position-dependent content of hydrogen bonding, which in turn influences energy requirements for unwinding double-stranded DNA. By analyzing over 14,000 bacterial, archaeal, and fungal ORFeomes, we found that Bacteria and Archaea exhibit an exponential ramp of hydrogen bonding at the 5'-end of CDSs, while a similar ramp was not found in Fungi. The ramp develops within the first 20 codon positions in prokaryotes, eventually reaching a steady carrying capacity of hydrogen bonding that does not differ from Fungi. Selection against uniformity tests proved that selection acts against synonymous codons with high content of hydrogen bonding at the 5'-end of prokaryotic ORFeomes. Overall, this study provides novel insights into the molecular feature of hydrogen bonding that is governed by the genetic code at the 5'-end of CDSs. A web-based application to analyze the position-dependent hydrogen bonding of ORFeomes has been developed and is publicly available (https://juanvillada.shinyapps.io/hbonds/).

microbiology↗

bioRxiv2019

The unique coding sequence of pmoCAB operon from type Ia methanotrophs simultaneously optimizes transcription and translation

Understanding the interplay between genotype and phenotype is a fundamental goal of functional genomics. Methane oxidation is a microbial phenotype with global-scale significance as part of the carbon biogeochemical cycle, and is a sink for greenhouse gas. Microorganisms that oxidize methane (methanotrophs) are taxonomically diverse and widespread around the globe. Recent reports have suggested that type Ia methanotrophs are the most prevalent methane-oxidizing bacteria in different environments. In methanotrophic bacteria, complete methane oxidation is encoded in four operons (pmoCAB, mmoXYZBCD, mxaFI, and xoxF), but how evolution has shaped these genes to execute methane oxidation remains poorly understood. Here, we used a genomic meta-analysis to investigate the coding sequences that encode methane oxidation. By analyzing isolate and metagenome-assembled genomes from phylogenetically and geographically diverse sources, we detected an anomalous nucleotide composition bias in the coding sequences of particulate methane monooxygenase genes (pmoCAB) from type Ia methanotrophs around the globe. We found that this was a highly conserved sequence that optimizes codon usage in order to maximize translation efficiency and accuracy, while minimizing the synthesis cost of transcripts and proteins. We show that among the seven types of methanotrophs, only type Ia methanotrophs possess a unique coding sequence of the pmoCAB operon that is under positive selection for optimal resource allocation and efficient synthesis of transcripts and proteins in environmental counter gradients with high oxygen and low methane concentrations. This adaptive trait possibly enables type Ia methanotrophs to respond robustly to fluctuating methane availability and explains their global prevalence.

microbiology↗