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PubMed2026

Zone equalisation normalisation for improved alignment of epigenetic signal.

MOTIVATION: High-throughput genomic technologies have transformed our understanding of biological systems, yet direct comparison and visualisation of these complex datasets remains challenging. Existing normalisation methods often fail to align genomic signal across samples due to sensitivity to sequencing depth differences and localised high-signal artefacts, leading to inconsistent replicate behaviour and increased downstream variability. RESULTS: We introduce Zone Equalisation Normalisation (ZEN), a novel approach designed to improve cross-sample signal alignment of genomic data. ZEN rescales genomic signal based on variance estimated within biologically enriched regions, reducing the influence of extreme outliers while preserving underlying biological structure. Using a diverse collection of data and our new genome-wide benchmarking approach, we reveal that ZEN improves biological and technical replicate alignment across the majority of tested conditions and experimental platforms. We further show that this improved signal comparability is associated with fewer differential accessibility calls between technical replicates and a more conservative set of biological differences. Together, these results demonstrate that ZEN provides a complementary framework to improve the accuracy and reliability of genomic data analysis and that normalisation choice can affect downstream analyses and biological interpretation. AVAILABILITY AND IMPLEMENTATION: ZEN is available as an open-source Python package via conda and PyPI. Source code, documentation, tutorials, and code to reproduce the analyses are available at https://github.com/Genome-Function-Initiative-Oxford/Zone-Equalisation-Normalisation and Zenodo (https://doi.org/10.5281/zenodo.21067751).

Epigenesis, Genetic↗
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↗
PubMed2026

Chromosome-Scale Genome of Zoonotic Eyeworm Thelazia callipaeda from China.

Thelazia callipaeda is a vector-borne zoonotic eyeworm infecting companion animals, wildlife, and humans, but chromosome-scale genomic resources from Chinese clinical material remain limited. We generated a genome supported by Pacific Biosciences (PacBio) high-fidelity (HiFi) sequencing and high-throughput chromosome conformation capture (Hi-C) from 100 adult worms recovered from naturally infected dogs in Beijing and compared its chromosome-scale organization with Portuguese assembly GCA_965194785.1. The final assembly spans 119.53 megabases (Mb) and comprises 115 top-level sequences, including four pseudomolecules totaling 91.26 Mb (76.34%) and 111 unanchored sequences. Genome-mode Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis recovered 98.5% complete chromadorean orthologues, and the representative 11,788-protein gene set recovered 92.6%. Sequence-level alignment resolved Chinese chromosomes 1-4 (chr1-chr4) to Portuguese chr1, chrX, chr3, and chr2, respectively, with retained alignments covering 95.9-99.2% of each Chinese pseudomolecule and estimated sequence identities of 99.75-99.91%. Strong chromosome-scale collinearity was accompanied by localized reverse-collinear regions, including 0.243 Mb and 0.115 Mb intervals on chr2-chrX and chr3-chr3. The anchored sequences contained 96.7% of predicted genes and were substantially more gene-dense than the unanchored sequences. These results establish a clinically sourced Chinese chromosome-scale reference and provide a validated framework for future individual-worm, population-genomic, structural-variation, and comparative genomic studies of this parasite.

Hi-C↗
PubMed2026

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BACKGROUND: Teat number is a crucial economic trait in pigs. It directly affects the ability of sows to lactate, which in turn influences the survival and health of piglets. The teat number of French Large White pigs is close to 16, while the teat number of American Large White and Landrace pigs is about 14. In order to improve the teat number of American Landrace and Large White pigs through molecular approaches and precise breeding techniques, we genotyped 2,131 American Landrace and 4,564 American Large White with teat number phenotype using a 50 K SNP chip. Then, the SNP-chip data was imputed to the level of whole-genome sequencing (iWGS). Based on iWGS data, we conducted GWAS to identify novel, significant SNPs associated with teat number and to incorporate them into genomic selection. RESULTS: In Landrace pigs, significant SNPs for TTN mapped to SSC2, SSC7, SSC8, and SSC14; the SSC8 and SSC14 effects are novel. LTN mapped to SSC7, RTN to SSC7 and SSC8. The lead SSC7 SNP explained 2.60% of TTN phenotypic variance. In Large White pigs, significant SNPs were detected on SSC7 and SSC10 for TTN; SSC7, SSC10, and SSC12 for LTN; and SSC7 and SSC10 for RTN. The most significant locus on SSC7 accounted for 2.99% of the phenotypic variance in TTN. Additionally, a multi-population meta-analysis detected significant novel SNPs for LTN on SSC1 and SSC8. By utilizing Bayesian fine mapping, the most precise QTL confidence interval on SSC7 for both TTN and RTN in Large White pigs was reduced to 40 kb. By integrating functional gene annotation with RNA-seq and ATAC-seq data from Erhualian and Bamaxiang pigs mammary placodes at embryonic day 26, we prioritized PTPN13, TRPV3, ZDHHC13, and BRD2 as novel candidate genes for teat number. We then incorporated the significant SNPs to GBLUP and benchmarked genomic-selection accuracy. In both breeds, fitting the top SNP as fixed maximized prediction for TTN and RTN, whereas treating all significant loci as an additional random effect optimized LTN. CONCLUSIONS: Our findings provide a theoretical basis for dissecting new key genes affecting teat number and for advancing molecular breeding of teat number in pigs.

Animals↗
PubMed2026

Agentomics: an agentic system that autonomously develops novel state-of-the-art solutions for biomedical machine learning tasks.

MOTIVATION: Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack flexibility, while large language models (LLMs) struggle to consistently deliver reproducible machine learning codebases, and existing LLM Agent-powered solutions lag behind human-engineered ML models. RESULTS: Here, we introduce Agentomics, an autonomous LLM-powered agentic system for end-to-end ML experimentation. Given a biomedical dataset, Agentomics implements various ML modeling strategies, and produces a ready-to-use ML model. Agentomics introduces strict validation checkpoints for standard ML development steps, allowing gradual development on top of working code with defined interfaces and validated artifacts. Further, it offers native support for biomedical foundation models that can be leveraged during experimentation. The generic nature of Agentomics allows the user to create ML solutions for a large variety of datasets and use various LLMs. We evaluate Agentomics across 20 datasets from the domains of Protein Engineering, Drug Discovery, and Regulatory Genomics. When benchmarked against other agentic systems, Agentomics outperformed them in all tested domains. When benchmarked against human expert solutions, Agentomics generated novel state-of-the-art models for 11/20 established benchmark datasets. AVAILABILITY AND IMPLEMENTATION: Agentomics is implemented in Python. Source code and documentation are freely available at: https://github.com/BioGeMT/Agentomics-ML.

Machine Learning↗
PubMed2026

pLAST-a tool for rapid comparison and classification of bacterial plasmid sequences.

MOTIVATION: The increasing number of fully sequenced bacterial plasmids being annotated and catalogued has prompted the development of computational tools for comparing and classifying them. Existing approaches typically compare full-length DNA sequences (e.g. Mash, BLASTn, and ANI-based methods) or translated open reading frames (ORFs) (e.g. DIAMOND), with plasmid-level scores obtained by aggregating ORF-to-ORF similarities; however, they are either restricted to closely related plasmids or become computationally demanding in large-scale analyses. RESULTS: We describe pLAST (plasmid Language Analysis and Search Tool), a plasmid-search tool built using word2vec representations of protein-family content informed by local genomic context. Benchmarks indicate that pLAST outperforms nucleotide-based methods and performs comparably to DIAMOND in identifying functionally similar plasmids and compared with the widely used Mash, it achieves 26% and 24% improvements in detecting shared mating-pair formation system type and relaxase type, respectively. This performance scales to database searches across hundreds of thousands of sequences, as demonstrated using the precomputed PlasmidScope collection of ∼750 000 plasmids. Beyond global similarity, pLAST also returns per-ORF plasmid-plasmid alignments, enabling detection of shared functional modules. AVAILABILITY AND IMPLEMENTATION: pLAST is freely accessible as a web server at https://plast.lbs.cent.uw.edu.pl/ or https://plast.lbs.biol.uw.edu.pl/ and available as a Python module along with a precomputed database at https://github.com/labstructbioinf/pLAST for customized analysis.

Plasmids↗
PubMed2003

Zerg: a very fast BLAST parser library.

SUMMARY: Zerg is a library of sub-routines that parses the output from all NCBI BLAST programs (Blastn, Blastp, Blastx, Tblastn and Tblastx) and returns the attributes of a BLAST report to the user. It is optimized for speed, being especially useful for large-scale genomic analysis. Benchmark tests show that Zerg is over two orders of magnitude faster than some widely used BLAST parsers. AVAILABILITY: http://bioinfo.iq.usp.br/zerg

Database Management Systems↗
PubMed2026

Leveraging ONT move table values for signal aware variant calling.

Oxford Nanopore Technologies (ONT) sequencing enables long-range haplotype phasing and contiguous genome assembly but still exhibits elevated error rates that challenge small variant calling, particularly for insertions and deletions (Indels). While raw electrical signals contain rich information, existing signal-aware methods require computationally intensive processing of large signal files. Here, we present Clair3 v2, a method that leverages the ONT move table-a lightweight byproduct of basecalling that maps signal events to nucleotide positions-to improve variant calling accuracy. Clair3 v2 builds upon Clair3 and integrates signal-level dwelling time to significantly enhance variant calling performance. We also propose a genome position based circular buffer to incorporate dwelling time with minimal computational overhead. Benchmarking across six Genome in a Bottle samples demonstrates substantial improvements in variant calling accuracy. With HAC basecalling, Clair3 v2 achieves a mean SNP F1-score of 97.69% at 10 × depth (compared to 96.45% for baseline Clair3), and Indel F1 scores improved from 64.27% to 76.70%, while gains persisted at higher depths. The benefits were most pronounced for longer Indels and in complex genomic regions, where Indel F1 scores in long homopolymer regions improved from 14.3% to 45.2%. Benchmark results across various basecalling modes, samples, and coverage settings outperformed Clair3 baselines and other methods, including DeepVariant and Dorado Variant, and demonstrate the significant benefits of Clair3 v2. Furthermore, Clair3 v2 incurs negligible runtime compared to standard Clair3, making it practical for routine use.

Sequence Analysis, DNA↗
PubMed2006

Benchmarking ortholog identification methods using functional genomics data.

BACKGROUND: The transfer of functional annotations from model organism proteins to human proteins is one of the main applications of comparative genomics. Various methods are used to analyze cross-species orthologous relationships according to an operational definition of orthology. Often the definition of orthology is incorrectly interpreted as a prediction of proteins that are functionally equivalent across species, while in fact it only defines the existence of a common ancestor for a gene in different species. However, it has been demonstrated that orthologs often reveal significant functional similarity. Therefore, the quality of the orthology prediction is an important factor in the transfer of functional annotations (and other related information). To identify protein pairs with the highest possible functional similarity, it is important to qualify ortholog identification methods. RESULTS: To measure the similarity in function of proteins from different species we used functional genomics data, such as expression data and protein interaction data. We tested several of the most popular ortholog identification methods. In general, we observed a sensitivity/selectivity trade-off: the functional similarity scores per orthologous pair of sequences become higher when the number of proteins included in the ortholog groups decreases. CONCLUSION: By combining the sensitivity and the selectivity into an overall score, we show that the InParanoid program is the best ortholog identification method in terms of identifying functionally equivalent proteins.

Algorithms↗
PubMed2026

Large-scale benchmarking of prokaryotic annotation tools across thousands of species.

BACKGROUND: Genome annotation is an important step in deriving functional meaning from prokaryotic sequencing data, yet systematic evaluations guiding tool selection are lacking. We present the first large-scale investigation of four prominent open-source annotation tools (Prokka, Bakta, EggNOG-mapper, and PGAP) across 156,033 diverse genomes. This includes Escherichia coli strains for baseline performance, thousands of archaea and bacteria genomes, as well as frameshifted and metagenome-assembled genomes. RESULTS: Bakta excels in annotating high-quality bacterial genomes, while PGAP was better for archaeal genomes and challenging bacterial assemblies, including metagenome-assembled, fragmented, or contaminated samples. For Gene Ontology annotation, PGAP consistently provides broader term coverage, whereas EggNOG-mapper offers more terms per feature. CONCLUSIONS: Our findings highlight tool-specific strengths crucial for selecting optimal solutions based on genome quality, taxonomy, and origin (e.g. MAGs). This study provides an evidence-based guide for users and informs future tool development.

Molecular Sequence Annotation↗
PubMed2006

AceView: a comprehensive cDNA-supported gene and transcripts annotation.

BACKGROUND: Regions covering one percent of the genome, selected by ENCODE for extensive analysis, were annotated by the HAVANA/Gencode group with high quality transcripts, thus defining a benchmark. The ENCODE Genome Annotation Assessment Project (EGASP) competition aimed at reproducing Gencode and finding new genes. The organizers evaluated the protein predictions in depth. We present a complementary analysis of the mRNAs, including alternative transcript variants. RESULTS: We evaluate 25 gene tracks from the University of California Santa Cruz (UCSC) genome browser. We either distinguish or collapse the alternative splice variants, and compare the genomic coordinates of exons, introns and nucleotides. Whole mRNA models, seen as chains of introns, are sorted to find the best matching pairs, and compared so that each mRNA is used only once. At the mRNA level, AceView is by far the closest to Gencode: the vast majority of transcripts of the two methods, including alternative variants, are identical. At the protein level, however, due to a lack of experimental data, our predictions differ: Gencode annotates proteins in only 41% of the mRNAs whereas AceView does so in virtually all. We describe the driving principles of AceView, and how, by performing hand-supervised automatic annotation, we solve the combinatorial splicing problem and summarize all of GenBank, dbEST and RefSeq into a genome-wide non-redundant but comprehensive cDNA-supported transcriptome. AceView accuracy is now validated by Gencode. CONCLUSION: Relative to a consensus mRNA catalog constructed from all evidence-based annotations, Gencode and AceView have 81% and 84% sensitivity, and 74% and 73% specificity, respectively. This close agreement validates a richer view of the human transcriptome, with three to five times more transcripts than in UCSC Known Genes (sensitivity 28%), RefSeq (sensitivity 21%) or Ensembl (sensitivity 19%).

Computational Biology↗
PubMed2026

A unified benchmark of supervised and retrieval-based methods for viral genomic sequence classification.

The rapid growth of genomic sequencing demands fast, accurate, and scalable analysis methods. In viral genomic classification, expanding labeled reference collections can make supervised models costly to update and dependent on fixed label sets, motivating retrieval-based genomic classification as a simpler, more flexible alternative. We present a unified benchmark of supervised and retrieval-based methods for viral genomic sequence classification across three viral classification tasks: hepatitis C virus (HCV) genotyping, COVID-19 discrimination, and human papillomavirus (HPV) genotyping. We compare standard sequence encodings (one-hot, k-mers, FCGR) with dense embeddings (dna2vec, DNABERT). For each representation, we evaluate supervised classifiers (Random Forest, Decision Tree, XGBoost) and retrieval-based classification, where sequence vectors are indexed with FAISS and labels are assigned via similarity-weighted k-NN. Furthermore, we benchmark multiple FAISS index types (Flat, IVF, HNSW, IVFPQ, OPQ) to characterize accuracy-speed-memory trade-offs at scale. The results show that XGBoost and retrieval using Flat or IVF indexes achieve strong classification performance under different computational profiles. Compressed indexes such as IVFPQ and OPQ substantially reduce memory usage, although their accuracy loss depends on the dataset and representation. Overall, supervised XGBoost provides a favorable accuracy-size trade-off, while retrieval-based classification remains competitive and allows labeled reference sequences to be incorporated without retraining a global classifier. This benchmark provides practical guidance for selecting sequence representations, classifiers, and vector-search indexes under different accuracy, memory, and update requirements.

Genome, Viral↗
PubMed2004

A Bayesian approach to DNA sequence segmentation.

Many deoxyribonucleic acid (DNA) sequences display compositional heterogeneity in the form of segments of similar structure. This article describes a Bayesian method that identifies such segments by using a Markov chain governed by a hidden Markov model. Markov chain Monte Carlo (MCMC) techniques are employed to compute all posterior quantities of interest and, in particular, allow inferences to be made regarding the number of segment types and the order of Markov dependence in the DNA sequence. The method is applied to the segmentation of the bacteriophage lambda genome, a common benchmark sequence used for the comparison of statistical segmentation algorithms.

Algorithms↗
PubMed2026

Benchmarking Assembly-Free K-mer Methods for Species Identification in Complex Plant Groups: A Case Study in Populus.

Species identification in taxonomically complex plant groups is frequently limited by the inadequacy of organellar markers, whose phylogenetic signal is disrupted by cytonuclear discordance and chloroplast capture. Using the taxonomically complex genus Populus as a model, we evaluated an assembly-free k-mer workflow against a curated SNP reference benchmark. Whole-genome resequencing data from 235 Populus individuals were curated to a 202-individual, 34-species reference dataset in which all retained species are strictly monophyletic in a genome-wide SNP analysis. Independent maximum likelihood analyses further confirmed that the 31 non-hybrid backbone species each maintained high-support monophyly, while taxa of documented reticulate origin showed placement patterns consistent with their reticulate histories. ABBA-BABA D-statistics detected widespread residual allele sharing within the backbone, though the strongest signals did not correspond to the species pairs responsible for the few k-mer identification failures. Against this benchmark, complete plastomes showed limited resolution, recovering only 3.0% species monophyly and 71.1% nearest-neighbor assignment. The optimized k-mer workflow, operating directly on raw reads without assembly or alignment, recovered 91.2% species monophyly, 99.0% nearest-neighbor assignment, and 98.0% group-average assignment. K-mer length was the primary accuracy-controlling parameter, with k = 31 falling within a stable accuracy plateau. Distance-based metrics reached near-saturation at 0.2× sequencing depth, indicating that low-coverage genome skimming can support scalable nuclear genome-based identification with standard computational resources. K-mer distance heatmaps also flagged unusual genomic affinities in hybrid-origin and outlier samples, providing a rapid screen for subsequent population genomic analyses. These results support assembly-free k-mer distances as an efficient tool for reference-based species identification and sample screening in complex plant groups, with residual limitations concentrated near recently diverged species boundaries. Model-based phylogenomic, coalescent, and network analyses remain necessary for resolving deeper species relationships and detailed introgression histories.

Populus↗
PubMed2026

Comparing ARG Inference Methods Under Transmission of Reproductive Success: Tree Imbalance Matters.

Inferring coalescent trees from genomic data has become a major subject in population genetics, particularly with the recent advances in tree sequence reconstruction methods. However, it remains unclear how well these methods perform for imbalanced genealogies. Such imbalances can arise from processes such as cultural transmission of reproductive success (CTRS) or positive selection. Using simulated genomic data, we benchmarked three major software packages, SINGER, Relate, and tsinfer, by comparing the imbalance of reconstructed trees by these methods with that of the true simulated trees, for three indices that quantify this imbalance. The three methods performed well under scenarios yielding balanced trees. However, their accuracy declined as imbalance increased. Performances also varied with mutation rate, recombination rate, and sample size. This study opens possibilities for applying these methods to infer CTRS or positive selection in large-scale genomic datasets, using simulation-based inference such as approximate Bayesian computation.

Models, Genetic↗
PubMed2025

Transcriptional benchmark dose modeling of ultraviolet radiation-induced genomic activation in mouse skin.

The in vivo transcriptional response of mouse skin to ultraviolet radiation (UV-R) exposure reveals key genomic alterations associated with UV-R-induced damage but it does not provide precise dose thresholds for these effects. These initial findings provided the impetus to advance dose-response characterization by integrating benchmark dose (BMD) modeling with transcriptomic data, aiming to identify biologically relevant points of departure for gene and pathway activation. To accomplish this, mice were exposed to five erythemally weighted UV-R doses (0-40 mJ/cm2) emitted from a UV-emitting tanning device, across six post-exposure timepoints (0-96 h). Four analytical methods were used to estimate BMDs, with the lowest consistent response dose (LCRD) approach yielding the most sensitive estimates (1.21-3.44 mJ/cm2). Transcriptomic responses revealed activation of shared pathways related to DNA damage and cancer, oxidative stress and metabolism, inflammation and immunity, and hormonal disruption. Notably, the majority of LCRD BMD estimates (1.21-3.44 mJ/cm2) were lower than the International Electrotechnical Commission standard actinic exposure limit (3 mJ/cm2 (erythemally weighted)) for broadband UV-R (200-400 nm) for unprotected skin and the eye for an 8 h period. These findings suggest that transcriptomic BMD modeling can detect early biological responses to UV-R at doses lower than current exposure limits.

Animals↗
PubMed2026

Genomic Characterization of Classic Adamantinoma, Osteofibrous Dysplasia, and Osteofibrous Dysplasia-like Adamantinoma.

Classic adamantinoma, osteofibrous dysplasia (OFD), and OFD-like adamantinoma are rare bone tumors arising primarily in the tibiae. Their distinction can be challenging; data on their molecular pathogenesis remain limited. We searched our pathology files in 2004-2024 for available cases and performed targeted next-generation sequencing along with whole-genome single-nucleotide polymorphism arrays and 3-dimensional genomics/Hi-C sequencing in selected cases. Our cohort included 3 classic adamantinomas (2 females and 1 male; age, 14-56 years), 5 OFDs (3 females and 2 males; age, 9-25 years), and 2 OFD-like adamantinomas (1 female and 1 male; age, 30-41 years). Of the 10 tumors, 9 arose from the tibiae; 1 classic adamantinoma originated from the radius. The 3 classic adamantinomas harbored multiple copy number gains involving chromosome 7, 8, 10, 12, and/or 19. Focal deletion of chromosome 17, intergenic rearrangement involving FGFR1, and NRAS p.G12D were each present in 1 classic adamantinoma. Of the 5 OFDs, KMT2A p.C2441F, KMT2D p.S1040P, PHOX2B p.G213D, and RIF1 deletion were each present in 1 case; no additional copy number/single-nucleotide variants were identified. Of the 2 OFD-like adamantinomas, one case with tumor clusters visible only on cytokeratin immunostain harbored no variants, whereas another case with tumor clusters visible on light microscopy and cytokeratin/p40 immunostains showed gains of chromosome 7, 8, 19, and 20. By Hi-C, 1 classic adamantinoma harbored an approximately 9 Mb tandem duplication on chromosome 12q, 1 OFD harbored a rearrangement with breakpoints near MECOM and HOOK3, and the OFD-like adamantinoma with tumor clusters visible only on cytokeratin immunostain harbored no structural variant. In conclusion, classic adamantinomas and OFD might be genetically distinct. Classic adamantinomas harbored multiple alterations, including chromosome/arm-level copy number gains, the detection of which could aid their distinction from OFDs. Using genomics as the benchmark, OFD-like adamantinomas might be better delineated by light microscopy or p40 than by cytokeratin immunohistochemistry. These data expanded our molecular understanding of these rare bone tumors.

Humans↗