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At least 19 recordsLinked to original sources

PubMed2025

A complete and near-perfect rhesus macaque reference genome: lessons from subtelomeric repeats and sequencing bias.

A truly complete, telomere-to-telomere (T2T), and error-free reference genome remains a foundational resource-and long-standing goal-for unbiased comparative and functional genomics. While recent T2T assemblies of humans and other primates have made substantial progress, most still contain thousands of base-level errors, particularly within highly repetitive regions. Here, we present T2T-MMU8v2.0, a near-perfect T2T assembly of the rhesus macaque (Macaca mulatta), representing the highest base-level accuracy reported in a primate genome to date. By employing an optimized ONT-only assembly strategy, we identify subtelomeric satellite-rich regions as the principal bottleneck to improving assembly quality, owing to technological biases in long-read platforms and limitations in current hybrid assembly frameworks. We discover 268 previously unannotated repeat families and resolve ~8 Mbp of SATR satellite arrays, with over 99-fold enrichment in historically misassembled subtelomeric regions. These satellites form four distinct genomic architectures, each with unique SATR satellite composition, segmental duplication organization, and epigenetic signatures, distinct from the subtelomeric architectures observed in hominid genomes. Notably, in contrast to the largely gene-poor subtelomeric regions in African hominids, the SATR architectures in macaques harbor 58 actively transcribed genes, supported by open chromatin and expression data, suggesting gene innovation within these repetitive regions. Functionally, T2T-MMU8v2.0 improves read mappability and accuracy across sequencing platforms, and results in a 19% improvement of transcription start site enrichment scores and 5,821 additional chromatin accessibility peaks on average, thereby enhancing variant detection, regulatory annotation, and transcriptomic resolution in population genetics or single-nucleus studies. Together, this work establishes a new benchmark for genomics, offers a roadmap for resolving complex repetitive regions, and reveals previously unrecognized features of subtelomeric genome structure and evolution.

Journal Article↗

PubMed2025

Properties Governing Native State Entanglements and Relationships to Protein Function.

Non-covalent lasso entanglements are structural motifs found in a majority of globular proteins, and their misfolding has been linked to a range of biological consequences. Here, we characterize these motifs' structural and physicochemical properties, sequence biases, functional site correlations, and universal features across E. coli, S. cerevisiae, and H. sapiens. We find that the crossing residues, which pierce the plane of the entanglement loop, are 11-times more likely to be a β-strand than an α-helix or random coil, and that around this position the protein sequence is 2.5-times more likely to be composed of a stretch of all hydrophobic residues (most often Val, Ile, or Phe) compared to other sequence motifs. Functionally, crossing residues are enriched at enzyme active sites in S. cerevisiae and small molecule binding residues across all species to degrees greater than expected by random chance. Metal binding residues are enriched in these entanglements in H. sapiens. Increasing statistical power by pooling together these species data, we find RNA-binding residues are enriched in these entanglement components. On the other hand, there is a spatial depletion of crossing residues at sites involved in protein binding. Using machine learning, we identified eight robust features predictive of these entanglements, achieving AUROC scores of 0.8 across species. These results are significant because they suggest a direct role for components of native entanglements in particular protein functions, as well as identifying strong secondary structure and sequence preferences in native entanglements.

Humans↗

PubMed2026

Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.

As researchers and clinicians seek to identify human genomic alterations relevant to traits and disorders, identifying and aggregating evidence providing mechanistic support for associations between alterations and phenotypes remains challenging. In particular, the study of noncoding genomic variation remains a major challenge because of the lack of accurate functional annotation for activity in a given context and across alleles. Experimental evidence is critical for prioritizing and interpreting functional effects of genetic alterations. Massively parallel reporter assays (MPRAs) have emerged as a powerful high-throughput approach, enabling quantification of regulatory element activity and allelic effects, as well as systematic dissection of gene regulatory logic and variant effects across different contexts. However, the diversity of MPRA designs, lack of standardized formats, and many potential processing parameters hamper data integration, reproducibility, and meta-analyses across studies. To address these challenges, the Impact of Genomic Variation on Function (IGVF) Consortium established an MPRA focus group to develop community standards, including harmonized file formats, and robust analysis pipelines for a wide range of library types and experimental designs. Here, we present these formats and comprehensive computational tools, MPRAlib and MPRAsnakeflow, for uniform processing from raw sequencing reads to counts, processing, and visualization. Using diverse MPRA data sets, we investigated technical variability sources including barcode sequence bias, outlier barcodes, and delivery method (episomal vs. lentiviral). Our results establish best practices for MPRA data generation and analysis, facilitating robust, reproducible research and large-scale integration. The presented tools and standards are publicly available, providing a foundation for future collaborative efforts in regulatory genomics.

Humans↗

PubMed2026

The planktonic microbiome of the Great Barrier Reef.

Large genome databases have markedly improved our understanding of marine microorganisms1-5. Although these resources have focused on prokaryotes, genomes from many dominant marine lineages, such as Pelagibacter and Prochlorococcus, are conspicuously underrepresented. Here we present the Great Barrier Reef Microbial Genomes Database (GBR-MGD), comprising 5,283 prokaryotic genomes obtained from Great Barrier Reef seawater samples using Nanopore and Illumina sequencing, including a collection of high-quality genomes of underrepresented groups. We show that standard short-read assemblies miss these populations owing to a combination of strain heterogeneity and low-GC-percentage sequencing bias. The GBR-MGD also comprises 20 chromosome-level picoeukaryote and 808,585 viral genomes, including a newly described clade of marine Crassvirales. We demonstrate the utility of the GBR-MGD to identify indicator taxa that can reliably predict the effects of reef management practices, such as the establishment of marine protected zones.

Bacteria↗

PubMed2025

Phylogenomic Analyses Reveal that Panguiarchaeum Is a Clade of Genome-Reduced Asgard Archaea Within the Njordarchaeia.

The Asgard archaea are a diverse archaeal phylum important for our understanding of cellular evolution because they include the lineage that gave rise to eukaryotes. Recent phylogenomic work has focused on characterizing the diversity of Asgard archaea in an effort to identify the closest extant relatives of eukaryotes. However, resolving archaeal phylogeny is challenging, and the positions of 2 recently described lineages-Njordarchaeales and Panguiarchaeales-are uncertain, in ways that directly bear on hypotheses of early evolution. In initial phylogenetic analyses, these lineages branched either with Asgards or with the distantly related Korarchaeota, and it has been suggested that their genomes may be affected by metagenomic contamination. Resolving this debate is important because these clades include genome-reduced lineages that may help inform our understanding of the evolution of symbiosis within Asgard archaea. Here, we performed phylogenetic analyses revealing that the Njordarchaeales and Panguiarchaeales constitute the new class Njordarchaeia within Asgard archaea. We found no evidence of metagenomic contamination affecting phylogenetic analyses. Njordarchaeia exhibit hallmarks of adaptations to (hyper-)thermophilic lifestyles, including biased sequence compositions that can induce phylogenetic artifacts unless adequately modeled. Panguiarchaeum is metabolically distinct from its relatives, with reduced metabolic potential and various auxotrophies. Phylogenetic reconciliation recovers a complex common ancestor of Asgard archaea that encoded the Wood-Ljungdahl pathway. The subsequent loss of this pathway during the reductive evolution of Panguiarchaeum may have been associated with the switch to a symbiotic lifestyle, potentially based on H2-syntrophy. Thus, Panguiarchaeum may contain the first obligate symbionts within Asgard archaea besides the lineage leading to eukaryotes.

Phylogeny↗

PubMed2026

Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models.

Recent advances in large language models have extended to genomic applications, yet model robustness relative to context is unclear. Here, we demonstrate two intrinsic biases (input sequence length and nucleotide position) affecting SegmentNT results, a model included with the Nucleotide Transformer that provides nucleotide-level predictions of biological features. We demonstrate that nucleotide position within the input sequence (beginning, middle, or end) alters the nature of SegmentNT's raw prediction probabilities, which can be standardized to improve prediction consistency. While longer input sequence length improves model performance, diminishing returns suggest a surprisingly small input length of ∼3072 nucleotides might be sufficient for many applications. We further identify a 24-nucleotide periodic oscillation in SegmentNT's prediction probabilities, revealing an intrinsic bias potentially linked to the model's training tokenization (6-mers) and architecture. We identify potential approaches to account for these biases and provide generalizable insights for utilizing nucleotide-resolution functional prediction models.

Nucleotides↗

PubMed2026

Hard to Halt: Automation Bias in Agent-Driven Sequencing Prior Authorization Workflows.

PURPOSE: Prior authorization (PA) for exome or genome sequencing is a time-consuming process that impedes timely rare disease diagnosis. Large language model-based browser agents offer potential for automating these workflows, but their clinical reliability remain uncharacterized. METHODS: We developed a sandbox compromising a simulated ES/GS PA submission payer portal and a synthetic EHR containing 836 patient records spanning compliant profiles and deficient profiles with different types of issues. Gemini 3 Pro, Gemini 3 Flash, and Claude Opus 4.5 were evaluated on task completion rate, form completion accuracy, and appropriate withholding for deficient profiles. RESULTS: Larger models achieved much higher task completion rates (Gemini 3 Pro 95.45%, Claude Opus 4.5 93.67%) compared to Gemini 3 Flash (56.05%), but nearly universally failed to withhold submission for deficient profiles whereas Gemini 3 Flash ironically demonstrated superior withholding performance (17.33%). In a non-agentic setting, Gemini 3 Pro correctly identified 91% of the issues in deficient profiles, indicating that withholding failure is attributable to the browser interaction rather than the model's reasoning limitations. CONCLUSION: Current LLM-based browser agents exhibit a systematic bias towards form submission that poses risks in PA workflows. A modular, multi-agent architecture with human supervision is necessary for a safe clinical deployment.

Journal Article↗

PubMed2026

Mitogenome assembly and phylogenetic relationships of Phalaris arundinacea.

INTRODUCTION: As a perennial herb of Poaceae, Phalaris arundinacea plays key roles in grazing, production, and soil and water conservation because of its well-developed rhizomes and seed dispersal. We assembled and annotated the first mitogenome of P. arundinacea to support evolutionary and taxonomic research. METHODS: We assembled and annotated the first complete mitochondrial genome of P. arundinacea by integrating Illumina short reads with Nanopore long reads via a hybrid assembly strategy. The genome architecture was comprehensively characterized, encompassing codon usage bias, repetitive sequence organization, and inter-organellar genetic exchange with the chloroplast genome. RESULTS AND DISCUSSION: Assembly of the P. arundinacea mitogenome revealed two circular structures with a combined length of 526,717 bp. The genome comprised a set of 37 protein-coding genes (PCGs), 27 tRNAs, and 8 rRNAs, with the rRNA genes exhibiting full assembly (100% coverage). The mitochondrial genome contained 154 forward and 164 palindromic repeats, along with 25 tandem repeats and 124 simple sequence repeats (SSRs). Notably, 102 SSRs were distributed on contig1, predominantly in tetrameric form. Furthermore, 376 RNA editing sites were predicted. A total of 104 fragments were integrated into the mitochondrial genome from the chloroplast, amounting to 55,866 bp of transferred sequence. Finally, phylogenetic analysis of 28 plant mitogenomes placed P. arundinacea closest to species within the genus Poa (P. chaixii and P. pratensis). Comparative analysis of non-synonymous-to-synonymous substitution rate (Ka/Ks) ratios across divergent species revealed that the mitochondrial genome of P. arundinacea underwent stabilizing evolutionary dynamics, characterized by predominant purifying selection with several lineage-specific variations in selective pressure. Our findings support the close phylogenetic relationship between P. arundinacea and species of the genus Poa and provide a reference mitochondrial genome resource for future comparative studies within Phalaris that incorporate broader taxon sampling. These results support deeper phylogenetic investigations of P. arundinacea and facilitate future work on its germplasm characterization and applied use.

Phalaris arundinacea↗

PubMed2026

An enhanced multisegment RT-PCR method for influenza A virus sequencing: Improved performance and reduced preparation time over traditional methods.

Influenza A viruses (IAVs) remain a major global health threat, affecting both human and animal populations. Whole-genome sequencing is essential for monitoring viral evolution, zoonotic transmission, and emerging variants. However, conventional RT-PCR methods often result in incomplete gene coverage, amplification biases, and reduced sequencing accuracy, particularly in clinical samples. We developed a robust In-house method for IAV full-genome sequencing using the Oxford Nanopore Technologies (ONT) long-read sequencing platform. This method integrates an in-house multisegment Reverse Transcription PCR (RT-PCR) method with a streamlined 2-pool primer design targeting all eight IAV gene segments. RNA extracted from clinical and stock virus samples was reverse-transcribed and amplified using Superscript IV-based chemistry, followed by magnetic bead purification to ensure high-quality amplicons. Sequencing libraries were prepared with the Native Barcoding Kit 24 (SQK-NBD114.24) and sequenced on R10.4.1 flow cells on the MinION MK1C device. Data analysis using the Iterative Refinement Meta-Assembler (IRMA) confirmed improved read depth, uniform coverage, and complete genome recovery. Compared to conventional methods, our In-House Multisegment 2-Pool (IH-MS2P) RT-PCR method generated higher numbers of matched read counts, minimized chimeric artifacts, and delivered superior genome coverage across human, swine, and avian isolates. This optimized RT-PCR method provides a high-performance, time-efficient, and portable solution for influenza genomics, demonstrating robust applicability even with clinical samples of low RNA yield.

Influenza A virus↗

PubMed2026

Transgene sequence codon optimization and composition determines replication competence of self-amplifying RNA.

Self-amplifying RNA (saRNA) is an emerging RNA therapeutic modality that can facilitate higher magnitude and more durable protein expression at substantially lower doses than nonreplicating mRNA. Unlike conventional messenger RNA (mRNA), alphavirus-derived saRNA must support a replicase-driven RNA amplification step in addition to translation, raising the possibility that transgene coding sequences impose sequence-level constraints on replication. Here, saRNA replication was found to be dependent on the codon composition of the transgene; multiple therapeutic transgenes were replication defective despite an intact Venezuelan Equine Encephalitis Virus (VEEV)-derived saRNA backbone. Replication defects were rescued by synonymous codon re-optimization of the same transgenes, indicating that nucleotide-level features of the coding sequence, rather than the encoded protein, govern replication competence. Comparative compositional analyses identified a distinct signature associated with productive replication, characterized by elevated GC (>53%) and GC3 (>63%) content, higher codon adaptation to human (>0.75), and reduced UpA (<43/kb) and UpU (<41/kb) dinucleotide density. Moreover, deliberate compositional perturbation of an otherwise replication-competent transgene shifted these features and abolished replication, supporting a causal and combinatorial role for sequence composition in defining saRNA replication outcome. These findings define an underappreciated constraint in saRNA therapeutics and motivate saRNA-specific payload design frameworks that incorporate alphavirus-associated compositional biases during transgene sequence optimization.

Codon↗

PubMed2023

Quantitative analysis of tRNA abundance and modifications by nanopore RNA sequencing.

Transfer RNAs (tRNAs) play a central role in protein translation. Studying them has been difficult in part because a simple method to simultaneously quantify their abundance and chemical modifications is lacking. Here we introduce Nano-tRNAseq, a nanopore-based approach to sequence native tRNA populations that provides quantitative estimates of both tRNA abundances and modification dynamics in a single experiment. We show that default nanopore sequencing settings discard the vast majority of tRNA reads, leading to poor sequencing yields and biased representations of tRNA abundances based on their transcript length. Re-processing of raw nanopore current intensity signals leads to a 12-fold increase in the number of recovered tRNA reads and enables recapitulation of accurate tRNA abundances. We then apply Nano-tRNAseq to Saccharomyces cerevisiae tRNA populations, revealing crosstalks and interdependencies between different tRNA modification types within the same molecule and changes in tRNA populations in response to oxidative stress.

RNA↗

PubMed2026

Pan-genomics and multi-omics for deciphering genetic variation and accelerating genetic improvement in ruminant livestock.

Livestock reference genomes have transformed the discovery of variants associated with production, reproduction, health, and environmental adaptation. Nevertheless, a single linear reference represents only one mosaic haplotype and incompletely captures sequence diversity within a species, particularly structural variants, copy-number changes, repeat-rich regions, and breed-specific sequences. Pangenomes address this limitation by integrating multiple high-quality assemblies or population-scale variants into a unified sequence or graph representation. Concurrently, multi-omics approaches connect genomic variation with transcriptomic, epigenomic, manuscriptproteomic, metabolomic, and microbiome responses, thereby improving biological interpretation of genotype-phenotype relationships. This review synthesizes recent progress in livestock pangenomics and multi-omics, with emphasis on cattle, goats, sheep, water buffalo, and chickens. It describes advances in long-read and haplotype-resolved sequencing, graph construction, structural-variant discovery and genotyping, functional annotation, and integrative analysis. Recent pangenome studies have uncovered substantial non-reference sequence, reduced reference bias, identified breed- and population-specific structural variants, and resolved candidate variants underlying pigmentation, body size, tail morphology, cashmere production, altitude adaptation, and other economically relevant traits. However, translation into routine breeding remains constrained by uneven population representation, inconsistent structural-variant definitions, limited functional annotation, computational demands, and insufficient validation across environments. Future progress will depend on diverse near-complete assemblies, graph-aware imputation and genomic prediction, long-read transcriptomics, single-cell and spatial omics, rigorous causal validation, and open, interoperable resources. Together, these developments can support more accurate, resilient, and biologically informed livestock improvement. Importantly, current dairy-cattle evidence indicates that pangenome-derived structural variants can substantially improve variant discovery and functional interpretation while yielding only marginal average gains in routine genomic prediction, favoring targeted augmentation rather than wholesale replacement of established SNP-based evaluations.

Animals↗

PubMed2026

Features affecting Cas9-induced editing efficiency and patterns in tomato: evidence from a large CRISPR dataset.

CRISPR/Cas9 is a cornerstone of plant genome editing, yet the determinants of editing efficiency for a given single-guide RNAs (sgRNAs) and DNA double-strand break (DSB) repair outcomes remain poorly understood, particularly in plants. Here, we generated a large experimental dataset comprising 420 sgRNAs targeting promoters, exons, and introns of 137 genes in tomato protoplasts, and quantified editing efficiency and repair footprints together with chromatin accessibility and transcriptional state in the same cellular context. Editing efficiency was consistently higher at targets in accessible chromatin and modestly higher in promoters and introns than in exons, whereas transcriptional activity had no detectable effect. Editing efficiencies were more similar among sgRNAs targeting the same gene than among different genes, revealing a local genomic influence on Cas9 activity. A distinct subset of sgRNAs achieved near-complete editing and produced characteristic repair footprints dominated by long deletions with extended microhomology tracts, indicative of microhomology-mediated end joining (MMEJ), resembling patterns associated with high-efficiency guides in human cells, and suggesting conserved sequence-driven repair biases across species. In contrast, widely used human-trained prediction models failed to accurately rank sgRNA performance in plants, highlighting the limits of cross-species predictability. Together, this dataset provides a resource for improving guide design and mechanistic understanding of plant DNA repair.

Solanum lycopersicum↗

PubMed2026

Precision ID mtDNA Whole Genome Panel and sequencing of telogen hairs - perspectives for validation and implementation in casework.

Shed hair is a commonly encountered type of forensic evidence. Shed telogen hairs generally contain insufficient or highly degraded nuclear DNA for STR profiling; however, mtDNA analysis of telogen hair and hair shafts remains possible. We validated whole mitochondrial genome (mtGenome) sequencing using the Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific) and subsequently implemented the panel for the analysis of telogen hair, buccal, and casework samples. We analysed 90 diluted DNA samples containing 3-3,600 mtDNA copies, shed telogen hairs and their corresponding mtDNA from buccal swabs from 91 individuals, and 11 archived DNA extracts from hair samples in criminal cases. Complete mtGenome sequences were consistently recovered in 99% of samples across DNA dilution series at DNA input levels as low as 47 mtDNA copies, demonstrating the assay's robustness under low-template conditions. We obtained complete and reproducible mtGenome sequences with ≥ 327 mtDNA copies/µL from telogen hair samples. After applying ISFG recommendations and excluding low-confidence discrepancies associated with high-strand bias, heteroplasmic variants and sequencing artifacts, mtGenome sequence concordance increased from 93.4% to 100%. None of the 16 negative controls produced complete mtDNA sequences. Six negative controls showed low-level mtDNA signal (2-8 variants), consisting predominantly of common polymorphisms. These samples did not yield complete mtGenome sequences and showed no correspondence to any of the analysed samples. Finally, archived telogen hair samples from criminal cases presented complete mtGenome sequences with an average read depth of 1,037x.Our findings highlight the reliability of mtDNA analysis of telogen hairs using the Precision ID mtDNA Whole Genome Panel for implementation in forensic casework.

Forensic casework↗

PubMed2025

Quantifying prevalence and risk factors of HIV multiple infection in Uganda from population-based deep-sequence data.

People living with HIV can acquire secondary infections through a process called superinfection, giving rise to simultaneous infection with genetically distinct variants (multiple infection). Multiple infection provides the necessary conditions for the generation of novel recombinant forms of HIV and may worsen clinical outcomes and increase the rate of transmission to HIV seronegative sexual partners. To date, studies of HIV multiple infection have relied on insensitive bulk-sequencing, labor intensive single genome amplification protocols, or deep-sequencing of short genome regions. Here, we identified multiple infections in whole-genome or near whole-genome HIV RNA deep-sequence data generated from plasma samples of 2,029 people living with viremic HIV who participated in the population-based Rakai Community Cohort Study (RCCS). We estimated individual- and population-level probabilities of being multiply infected and assessed epidemiological risk factors using the novel Bayesian deep-phylogenetic multiple infection model (deep - phyloMI) which accounts for bias due to partial sequencing success and false-negative and false-positive detection rates. We estimated that between 2010 and 2020, 4.09% (95% highest posterior density interval (HPD) 2.95%-5.45%) of RCCS participants with viremic HIV multiple infection at time of sampling. Participants living in high-HIV prevalence communities along Lake Victoria were 2.33-fold (95% HPD 1.3-3.7) more likely to harbor a multiple infection compared to individuals in lower prevalence neighboring communities. This work introduces a high-throughput surveillance framework for identifying people with multiple HIV infections and quantifying population-level prevalence and risk factors of multiple infection for clinical and epidemiological investigations.

Humans↗

PubMed2026

RAmpSim: a thermodynamic simulator for hybridization capture in metagenomic sequencing.

MOTIVATION: Simulators that generate synthetic datasets help address the lack of ground truth for developing and benchmarking computational tools. Many read simulators assume uniform sampling across reference genomes; however, for newer capture-based sequencing technologies (e.g. TELSeq), this assumption is intentionally broken to oversample regions of interest. Along with systematic biases arising from probe multiplicity, sequence composition, and species abundances inherent to capture-based sequencing, this mismatch between modeling assumptions and the characteristics of real data necessitates the design of a new capture-based sequencing-specific simulator. RESULTS: We present RAmpSim, a fast simulator that models bait-target hybridization and fragment capture using a thermodynamic nearest-neighbor energy model and Boltzmann-weighted sampling of binding sites. Fragments are generated through multinomial sampling parameterized by bait concentration, binding energy, and genomic abundance before being passed to existing models of platform-specific errors. Implemented in Rust, RAmpSim reproduces empirical within-genome coverage and cross-species enrichment patterns observed in capture-based metagenomic datasets. RAmpSim generally outperforms a uniform baseline with respect to position-based earth mover's distance when compared against the empirical coverage distribution. Classification analysis also shows high recall in recovering empirical high-coverage regions while outperforming a uniform baseline. AVAILABILITY: Code, example scripts, and data sources are available at https://github.com/az002/RAmpSim.git.

Metagenomics↗

PubMed2025

SURROGATE SELECTION OVERSAMPLES EXPANDED T CELL CLONOTYPES.

Surrogate selection is an experimental design that without sequencing any DNA can restrict a sample of cells to those carrying certain genomic mutations. In immunological disease studies, this design may provide a relatively easy approach to enrich a lymphocyte sample with cells relevant to the disease response because the emergence of neutral mutations associates with the proliferation history of clonal subpopulations. A statistical analysis of clonotype sizes provides a structured, quantitative perspective on this useful property of surrogate selection. Our model specification couples within-clonotype birth-death processes with an exchangeable model across clonotypes. Beyond enrichment questions about the surrogate selection design, our framework enables a study of sampling properties of elementary sample diversity statistics; it also points to new statistics that may usefully measure the burden of somatic genomic alterations associated with clonal expansion. We examine statistical properties of immunological samples governed by the coupled model specification, and we illustrate calculations in surrogate selection studies of melanoma and in single-cell genomic studies of T cell repertoires.

Bayes’s rule↗

PubMed2025

Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models.

Most phylogenetic trees are inferred using time-reversible evolutionary models that assume that the relative rates of substitution for any given pair of nucleotides are the same regardless of the direction of the substitutions. However, there is no reason to assume that the underlying biochemical mutational processes that cause substitutions are similarly symmetrical. We consider two non-reversible nucleotide substitution models: (1) a 6-rate non-reversible model (NREV6) that is applicable to analyzing mutational processes in double-stranded genomes in that complementary substitutions occur at identical rates; and (2) a 12-rate non-reversible model (NREV12) that is applicable to analyzing mutational processes in single-stranded (ss) genomes in that all substitution types are free to occur at different rates. Using likelihood ratio and Akaike Information Criterion-based model tests, we show that, surprisingly, NREV12 provided a significantly better fit than the General Time Reversible (GTR) and NREV6 models to 21/31 dsRNA and 20/30 dsDNA datasets. As expected, however, NREV12 provided a significantly better fit to 24/33 ssDNA and 40/47 ssRNA datasets. We tested how non-reversibility impacts the accuracy with which phylogenetic trees are inferred. As simulated degrees of non-reversibility (DNR) increased, the tree topology inferences using both NREV12 and GTR became more accurate, whereas inferred tree branch lengths became less accurate. We conclude that while non-reversible models should be helpful in the analysis of mutational processes in most virus species, there is no pressing need to use these models for routine phylogenetic inference.

Models of evolution↗