NAR Genomics and Bioinformatics

Expanded repeat disorders remain without a disease modifying treatment. Some of the most important modifiers of disease severity include inherited repeat size, their somatic mosaicism, and whether they contain interruptions. Targeted sequencing approaches are becoming the gold standard on how these parameters are measured despite repeats being notoriously challenging to sequence. Here we developed CHARLIE (Comprehensive High-throughput Analysis of Repeat Length, Interruptions and Expansions) to identify and position interruptions within expanded repeats. We used samples from Huntingtons disease and myotonic dystrophy type 1 sequenced with Illumina MiSeq and PacBios Single-Molecule Real-Time Sequencing. We validated the pipeline against previous methods for identifying germline-inherited interruptions. One challenge that can potentially mask the identification of true interruptions and sequence variation is the accuracy of sequencing platforms, which, when applied to expanded repeats, is largely unknown. Our results suggest that each sequencing platform produces a distinct error profile and we show that PCR-free library preparation for SMRT sequencing improves sequencing accuracy. CHARLIE, therefore, provides a method that can help differentiate between trivial sequencing errors and those interruptions that modify disease presentation, which can be inherited or somatic in origin.

Techniques for evaluating gene regulatory network (GRN) inference methods typically focus on recovering small ground-truth networks or on benchmarking against simulated data. However, both approaches have important limitations and fail to capture the biological variability present in real datasets. FERRET is a framework for benchmarking single-cell GRN inference methods based on a simple biological assumption: independent estimates of the regulatory network from the same cellular state should resemble one another more closely than estimates from distinct cellular states. Rather than relying on incomplete or simulated ground truth, FERRET quantifies network robustness using two complementary metrics: Robustness Area Under the Curve (RAUC), an AUC-like measure of within-cell-type network similarity relative to between-cell-type similarity, and Monotonicity, which assesses the consistency of network similarity across edge-weight cutoffs. FERRET also supports biological validation through pathway enrichment analysis. We validate FERRET using experimentally derived ChIP-seq networks from B lymphocytes and fibroblasts as positive controls and randomly generated networks as negative controls, showing that biologically related networks receive high robustness scores whereas randomly generated networks receive scores consistent with chance. Finally, we apply FERRET to multiple GRN inference methods on real single-cell RNA-sequencing datasets to identify methods that produce the most robust, biologically informative regulatory networks. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/739795v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@da7e4eorg.highwire.dtl.DTLVardef@9a5425org.highwire.dtl.DTLVardef@a6e60org.highwire.dtl.DTLVardef@d466cd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Single-cell RNA-sequencing measurements are uniquely well-poised to identify coregulated gene transcription. Coregulation should be apparent as correlation of expression levels, but at which unit to quantify expression for calculation of correlations is not clear. To enable evaluation of normalization methods for identification of coregulation we have curated and preprocessed 35 scRNA-seq samples with known regulature relationships into a catalog. These datasets contain promoter-reporter data representing known positive coregulation and B cells where isotypic mutual exclusion of {kappa} and {lambda} light chains represent known negative coregulation. As an example, we use the catalog to evaluate seven normalization methods. Different normalization methods perform better for positive or negative coregulation, and results indicate implementation choices for advanced normalization methods The catalog may serve as a resource for evaluating novel proposed normalization methods and is available at https://huggingface.co/datasets/valsv/scrna-coregulation-benchmark

Most in silico perturbation methods for single-cell transcriptomics have been validated only on individual datasets, leaving their reliability and generalizability unknown. Through systematic cross-method, cross-dataset benchmarking of eight methods spanning six mathematical frameworks across four datasets, we find that six of eight methods--including widely used VAE-based and tensor decomposition approaches--fail to produce detectable transcription factor (TF)-to-pathway signals. Only CellOracle and DDIM consistently detected TF-to-glycolysis directional regulation. Cross-pathway analysis in PBMC monocytes revealed biologically coherent TF-pathway associations beyond glycolysis (SPI1[->]glycolysis 4.4x enrichment, FOS[->]AP-1 targets 4.4x), with SOX9 serving as a biological specificity control (no pathway enrichment). Method choice alone could reverse biological conclusions: DDIM and scTenifoldKnk rankings were significantly anti-correlated ({rho}=-0.811, p=0.027). CRISPRi Perturb-seq validation in K562 cells confirmed TF knockdown suppresses glycolysis gene expression (JUN {delta}=-1.72, CEBPB {delta}=-1.59, SPI1 {delta}=-1.57, FOS {delta}=-0.70), but CellOracle-predicted perturbation directions did not match experimental directions (40.9% agreement, not different from chance), revealing a fundamental gap between steady-state correlation and causal perturbation. Diagnostic analyses using VAE latent space profiling, correlation distribution comparison, and gene-gene graph analysis identified distinct failure modes in unsuccessful methods: VAE latent space competition (STAT3 signal-to-noise 0.44 vs. SPI1 4.25), correlation noise (TF-glycolysis |r|=0.038 indistinguishable from background |r|=0.047), and graph non-specificity (0.84x enrichment). A controlled ablation experiment showed that adding a GRN prior to DDIM did not improve target recall (delta=0 for all TFs), confirming that performance differences are multi-factorial. These findings establish preliminary guidance for method selection, including cross-pathway validation, direction-aware benchmarking, and minimum data requirements ([≥]500 cells, [≥]1,000 HVGs).

Abstract Ribosome profiling is widely used to map translation across the genome, yet current sORF discovery pipelines are constrained by their reliance on annotated protein coding regions and by treating biological replicates primarily as tools for quantification rather than as strict filters for reproducibility. This results in two major issues, the systematic exclusion of pseudogene derived loci from analysis and the generation of extremely large candidate sets in which true translation events are difficult to distinguish from noise. To overcome these limitations, a five-stage framework was developed that expands the search space to include pseudogene loci while enforcing dual biological replicate concordance as a hard binary filter. When applied to four human cell lines with matched replicates and benchmarked against existing tools, the framework produces dramatically smaller and more reliable candidate sets, reducing thousands to millions of predicted sORFs down to a small number of high confidence concordant loci. Across HeLa cell cycle conditions, a limited set of unique concordant sORFs was identified spanning multiple transcript classes including upstream ORFs, retained introns, and nonsense mediated decay transcripts, along with a pseudogene derived candidate. Notably, the pseudogene encoded sORF RPL26P19 was detected consistently in HeLa cells but missed or inconsistently identified by existing tools, while a separate pseudogene sORF from UBE3AP2 was observed specifically in normal primary cell types and absent in HeLa. Both candidates were later found to be supported by entries in OpenProt, even though this was not known during the analysis, which gives added confidence that the results are genuine and not biased by prior expectations. Overall, these findings suggest that pseudogenes are not just inactive remnants in the genome but can act as real, functional sources of translated sORFs in a cell type specific manner. At the same time, it becomes clear that most current tools are not designed to detect these signals effectively. This highlights the need for approaches that both broaden the regions being searched and apply stricter reproducibility checks, so that truly meaningful translation events can be identified more reliably.

RNA-Seq, analyses of RNA abundance by next-generation sequencing, has become a near-universal tool in modern biology. Availability of streamlined protocols and kits, straightforward ability to multiplex hundreds of samples, low cost of short-read sequencing, and well-established analytical pipelines make RNA-Seq a method of choice when even a few genes need to be analyzed in parallel. While many tools have been developed for quality control, mapping, and visualization of RNA-Seq data, managing all these individually still requires substantial familiarity with shell scripting and R, and remains a bottleneck for laboratories with limited computational background. We assembled FetchR, an intuitive pipeline with built-in, clear explanations of features and outputs, for local analyses of RNA-Seq data from either own .fastq files or data imported from Sequence Read Archive via the ENA Portal API. The pipeline operates in Windows Subsystem for Linux (WSL) and is installed via a single script that handles all individual tools, as well as their dependencies and updates, including the reference genome annotation(s), and system requirements. The outputs include standard quality control checks, data visualization, read summation, differential gene expression analyses, visualization, and exploratory analyses using Gene Ontology and Gene Set Enrichment Analyses, as well as detailed logs of every step for subsequent reproducible reporting.

MinHash-based genome comparison is governed by two statistical constraints: a noise floor on k-mer size, controlling chance collisions between unrelated sequences, and a precision floor on sketch size, controlling uncertainty in the estimated Jaccard similarity. These constraints are best characterized for fixed-size bottom-sketch MinHash, the classical construction underlying tools such as Mash and still the standard basis for comparing genomes of similar size. Nevertheless, while, the noise floor has an established closed-form solution, sketch size is commonly selected using fixed defaults or heuristic choices independent of genome length and target precision. Moreover, recent work on scaled (FracMinHash) sketching has highlighted the difficulty of obtaining an analogous closed-form confidence interval for the Jaccard similarity. Here, we show that under the fixed-size bottom-sketch MinHash model, the shared-hash count follows a binomial model, which lets classical proportion-estimation theory be applied directly. Combining this with Fofanov's k-selection criterion and the exact identity-Jaccard relationship yields a single closed-form design rule, s* (L, {rho}* , ), that unifies the noise and precision floors into one operating envelope. The resulting design rule was validated against exact Clopper-Pearson interval inversion, idealized binomial sampling, realistic overlapping k-mer simulation and 54 bacterial genome pairs spanning species-to-order taxonomic levels. In realistic simulations, empirical coverage remained within 1-3 percentage points of the idealized reference in 14 of 15 tested cells. In real genomes, the classical identity-Jaccard relationship showed increasing positive bias with taxonomic divergence, from a median of -1.4% within species to +27% at genus and +77% at family level. We further show that s* (L) is not smooth in genome length but a discontinuous, previously unreported staircase caused by the ceiling function used for k-mer selection, with practical consequences concentrated at specific genome-size boundaries.

BackgroundHi-C experiments produce genome-wide chromatin contact maps from which structural features can be quantified across multiple genomic scales, ranging from megabase-scale compartments to kilobase-scale boundaries and chromatin loops. Although high-resolution analyses commonly rely on hundreds of millions of sequenced read pairs, the minimum sequencing depth required for different classes of Hi-C-derived features has not been systematically established. We therefore sought to determine these depth requirements systematically. ResultsWe performed progressive random subsampling of twelve Hi-C libraries representing multiple cell types and experimental protocols. Loop-density and loop-size inference from the P(s) log-derivative was evaluated across all libraries, whereas compartment, insulation, and boundary analyses were performed on a subset of nine libraries with comparable full-depth coverage. Rather than defining sufficient depth as an arbitrary fraction of the full-depth value, we introduced a biologically motivated criterion based on the variability between independent biological replicates. The required sequencing depth for each metric was defined as the point at which subsampling scatter first reached the variability observed between independent biological replicates, representing the accuracy that additional sequencing cannot improve upon. Using this criterion, loop-density estimation reached its biological floor at approximately 10 million read pairs, loop-size estimation at approximately 20 million, insulation scores and boundary detection at approximately 30 million, and compartment eigenvectors at 100-kilobase resolution at approximately 60 million read pairs. ConclusionsDifferent Hi-C-derived metrics require substantially different sequencing depths to achieve biologically meaningful accuracy. For all metrics, depth-induced variability fell below biological replicate variability well before full sequencing depth was reached. These thresholds provide practical guidance for experimental design and sequencing budget allocation, suggesting that, in many studies, increasing the number of biological replicates is likely to improve reproducibility more effectively than sequencing individual libraries to greater depth.

SummaryHigh-throughput transcriptomics has made gene signatures central to interpreting gene expression data, with applications in diagnosis, prognosis, and prediction. Quantifying signature activity and assessing its robustness remain challenging because scoring methods primarily rely on various assumptions, and no single approach is universally optimal. Here, we present pysigscore, a Python framework for gene set scoring in bulk and single-cell RNA-seq data. pysigscore integrates 18 built-in scoring methods with a fully customisable scorer, allowing users to define and benchmark new scoring functions. It also provides reliability analyses, including p-value estimation and leave-one-out experiments, to assess the significance of scores and gene-level contributions. We validated pysigscore on the CCLE, TCGA, and PBMC datasets, recovering the expected enrichment in liver, hypoxia, inflammatory, and cell-cycle signatures. Availability and ImplementationSource code is available at https://github.com/bioinformatics-hub/pysigscore. Contact: tommaso.giacomello@phd.unibocconi.it, francesca.buffa@unibocconi.it Supplementary informationSupplementary data are available at Bioinformatics online.

A crucial aspect of the bioinformatics workflow in small RNA-sequencing is the alignment of reads to a database of reference ncRNAs. Alignment algorithms such as Bowtie, Burrows-Wheeler Aligner (BWA), and Spliced Transcripts Alignment to a Reference (STAR) - which are designed for aligning reads to a reference genome - are typically used. Aligning short RNA-sequenced reads to a database of non-coding RNAs (ncRNAs) is fundamentally a different task than aligning longer reads to a genome due to ncRNAs (i) having roughly the same number of nucleotides as the reads being aligned and (ii) being subsequences of other ncRNAs. To account for these differences, we developed the novel alignment algorithm LevenMap. Of all reads which exactly matched a reference ncRNA in a publicly available dataset, LevenMap aligned 100.0% of them to their respective ncRNA while all other aligners mapped less than 40% of these reads to their corresponding ncRNA. Furthermore, the mean ratio (length of read) / (length of corresponding reference ncRNA) of all aligned reads was 1.0 and 0.998 for LevenMap with at most zero and one mismatch(es) allowed, respectively; this ratio was no more than 0.51 for all other aligners. Overall, LevenMap is designed to account for the nuances of aligning small RNA-sequencing data to a database of reference ncRNAs and yields more biologically relevant counts compared to traditional aligners in this context. LevenMap is free and publicly available on GitHub: https://github.com/hdlugas/LevenMap.

Bulk and single-cell RNA sequencing (scRNA-seq) have become essential for investigating disease mechanisms and identifying diagnostic biomarkers. However, the growing volume of transcriptomic data remains difficult to reuse efficiently for many researchers. Downstream analysis often requires multiple statistical, visualization, and reporting tools, creating fragmented workflows that reduce transparency and reproducibility, particularly when analyzing scRNA-seq data. To address this gap, we developed CoTRA (Comprehensive Toolbox for RNA-seq Analysis), an open-source R/Shiny package for bulk and scRNA analysis. CoTRA integrates established methods into modular workflows, exposes parameters, and offers alternatives at selected stages. It supports bulk RNA-seq quality assessment, differential expression, annotation, enrichment, and reporting, as well as scRNA quality control, dimensionality reduction, clustering, marker identification, cell-type annotation, differential abundance, trajectory inference, pathway activity, and cell-cell communication. CoTRA runs on workstations or HPC environments without mandatory external data submission and was tested on Linux, Windows, and macOS. Compared with 14 other platforms for bulk RNA-seq/scRNA-seq, CoTRA supported 46 of 49 predefined functionality criteria. Tool validation using published rd10 retinal bulk RNA-seq identified 1,947 shared differentially expressed genes with concordant direction and strong log2 fold-change agreement. A retinal scRNA-seq case study demonstrated appropriate clustering, cell-type resolved analysis, and pathway activity scoring. CoTRA provides a graphical environment for bulk and single-cell RNA-seq analysis while retaining parameter transparency, methodological flexibility, and reproducible outputs. Strong concordance with the published bulk RNA-seq analysis supports the workflow consistency, while the single-cell case study demonstrates its applicability to advanced scRNA-seq analysis. The source code is freely available at https://github.com/UmairSeemab/CoTRA.

Long-read RNA sequencing has revealed extensive transcript diversity, but newly observed isoforms remain difficult to interpret beyond their classification as known or novel. Here, we present IsoAtlas, an interactive multispecies database for visual exploration and population-scale interpretation of transcript isoforms using 1, 035 human and 414 mouse uniformly processed long-read RNA-sequencing samples. Users can search annotated genes and transcripts or submit novel transcript models in GTF format, visualize their structures, and examine sample-level support, prevalence, expression, tissue and disease context, and sequencing-platform evidence. IsoAtlas integrates structurally equivalent transcripts across GENCODE, RefSeq and CHESS, consolidating evidence that would otherwise be distributed across annotation-specific identifiers. It further links corresponding human and mouse transcript models, enabling users to assess cross-species conservation and enabling users to assess cross-species conservation and inform the suitability of mouse models for isoform-specific studies. IsoAtlas can also evaluate arbitrary user-supplied transcript structures directly against accumulated long-read evidence. IsoAtlas therefore complements established reference annotations with an extensible evidence layer that connects transcript structure to population prevalence, biological context and cross-species support. IsoAtlas is freely available at https://www.isoatlas.org/. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/744438v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@89bd72org.highwire.dtl.DTLVardef@f48bbforg.highwire.dtl.DTLVardef@102ae37org.highwire.dtl.DTLVardef@fbbc2b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background Multi-omics integration has emerged as a powerful strategy to decode the molecular complexity of biological systems. However, the diversity of available methods, each designed with distinct assumptions, objectives, and computational requirements, makes method selection, usage and interpretation challenging for nonexpert users. Here we present BiomiX 3.0, an updated version of the BiomiX platform that extends its integration capabilities with four additional methods: Similarity Network Fusion (SNF), NEighborhood-based Multi-Omics clustering (NEMO), Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), and PRAMIGO (Phenotyping netwoRk Application for Multi-omics InteGratiOn), a novel supervised heterogeneous graph transformer (HGT) introduced in this work. Results We benchmarked all five methods, MOFA, DIABLO, SNF, NEMO, and PRAMIGO, on two independent multiomics datasets derived from a Chronic Lymphocytic Leukemia (CLL) cohort comparing IGHV-mutated and unmutated patients, and a pulmonary tuberculosis (PTB) cohort versus healthy controls. Supervised methods (DIABLO, PRAMIGO) consistently achieved higher condition-specific discrimination as measured by the Adjusted Rand Index (ARI) and the Adjusted Mutual Information (AMI). In contrast, unsupervised methods (SNF, NEMO) revealed alternative patient stratifications driven by independent sources of biological variance while MOFA performed in a semi-supervised way occupies an intermediate position, capturing latent factors that explain both disease-associated and orthogonal sources of variance. Systematic gene-centric analysis of the top-ranked features prioritized by each method was supported by manual biological annotation of shared and method-specific signals. Across cohorts, we annotated 154 shared features (116 genes, 38 metabolites) and 60 method-unique features per cohort, demonstrating that no single integration strategy captures the full landscape of biologically relevant signals. In the CLL cohort, shared features spanned B-cell receptor biology, innate immune signaling, RAS/MAPK activation, and epigenetic regulation, while methodunique features revealed supervised-method-specific insights into vesicle trafficking (DIABLO), immune checkpoints (MOFA), and ncRNA regulation (PRAMIGO). In the PTB cohort, a convergent interferon/innate immune signature dominated shared features across all methods. Still, method-unique analysis uncovered DIABLO-specific acylcarnitine metabolic reprogramming, MOFA-specific restoration of lysosomal trafficking, and PRAMIGO-specific {gamma}{delta} T-cell and immunoglobulin repertoire diversity. Across both cohorts, SNF and NEMO proved useful for detecting biological and technical sources of variation that were orthogonal to the primary condition of interest. Ultimately, PRAMIGO uniquely enables the construction of heterogeneous graphs modeling cross-modal molecular interactions, uncovering epigenetic co-regulation programs in CLL and multi-omics inflammatory modules in PTB that are difficult to identify using conventional integration approaches. Conclusions BiomiX 3.0 provides a graphical user interface (GUI) multi-method integration environment that democratizes access to state-of-the-art multi-omics analysis. By combining both unsupervised and supervised integration strategies within a unified platform and introducing graph-based learning through PRAMIGO, BiomiX 3.0 enables researchers across disciplines with complementary tools to interrogate the biological sources of variation in their data, without requiring bioinformatics expertise.

Ribosome profiling experiments capture a snapshot of translation by sequencing ribosome-protected fragments (RPFs). Alignment to a well-curated transcriptome captures most RPF signal, but can miss ribosome activity when expressed exons are absent from the reference. Genome alignment of RPFs expands the detectable sequence space, but can expose ambiguity between homologous loci. Existing workflows often couple reference choice with the handling of ambiguously mapped reads, making it difficult to isolate the effects of either decision on downstream analyses. Here, we developed RiboFlow v2 by adding genome alignment and configurable handling of multimapping reads to the existing transcriptome workflow. Using 24 human ribosome profiling libraries and their matched RNA-seq data, we found that nucleotide-level coverage at matched coding-sequence positions and translation efficiency (TE) were broadly concordant between alignment strategies under stringent alignment filters. Even under these conditions, individual genes showed substantial differences in coverage and TE estimates. These differences included coverage in alternative exons absent from selected transcripts and ambiguous alignments between protein-coding genes and processed pseudogenes. Clustering genes by read-assignment patterns identified groups associated with pseudogene prevalence and highlighted shared exonic sequence among reference transcripts as a source of alignment discrepancies. RiboFlow v2 extends the analysis beyond selected reference transcripts and enables identification of genes whose ribosome coverage and abundance estimates depend on reference composition and how ambiguously mapped reads are handled.

Translation elongation relies on accurate codon-anticodon pairing. Here, we present tTEscanR, an R package designed to investigate this translational interface. By quantifying mRNA codon demand alongside tRNA anticodon availability, tTEscanR provides scalable estimates of translation rates directly from standard transcriptomic and chromatin accessibility count matrices, where genomic features are represented as rows and experimental conditions as columns. tTEscanR seamlessly integrates into existing bulk and single-cell pipelines and provides modular functions for quality control, filtering, normalization, statistical analysis, and visualization. A structured data object centralizes workflow outputs and associated metadata. A built-in multilevel visualization module generates customizable, publication-ready graphical outputs to facilitate data interpretation and reproducibility of the complex translational landscape. We demonstrate the utility of tTEscanR across cancer biology, aging, and neurodegeneration datasets, uncovering critical translational regulatory programs overlooked by conventional analyses. tTEscanR is available in an open-source repository as a standalone tool or workflow plug-in.

Prophages are major drivers of bacterial evolution, mediating horizontal gene transfer and lysogenic conversion to alter host phenotypes. Nevertheless, identifying prophages within bacterial genomes remains challenging due to their heterogeneity and similarity to other mobile genetic elements. Here we present PHORAGER (Prophage Hunting, vOtu Retrieval, Annotation and Genomic ExploRation), a scalable Nextflow pipeline for the standardised identification and quality assessment of prophages from bacterial genomes. PHORAGER incorporates bacterial genome pre-processing, consolidation of predictions from multiple mining tools, annotation-based filtering to reduce false positives, and generation of ready-to-analyse summary tables. We validated PHORAGER using 30,824 publicly available ESKAPE pathogen genomes. PHORAGER recovered more high-quality prophages than individual mining tools alone, and through extensive quality assessments removed a substantial number of false-positive predictions. In total 23,132 putative prophages were identified, the majority belonging to the class Caudoviricetes, and exhibiting a high degree of host-specificity. Putative antimicrobial resistance genes were detected in 0.48% of prophages, whereas virulence factors were most abundant in S. aureus prophages. ESKAPE prophages also frequently encoded anti-phage defence systems. PHORAGER is freely available as open-source software and the ESKAPE prophage collection generated in this study provides a reusable resource for further investigations. GRAPHICAL ABTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/742953v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1ce582org.highwire.dtl.DTLVardef@11fc004org.highwire.dtl.DTLVardef@17765f5org.highwire.dtl.DTLVardef@1c6f0ff_HPS_FORMAT_FIGEXP M_FIG C_FIG

An important application of single-cell genomics experiments is to identify genes specific to biological categories or experimental conditions. Although numerous approaches have been proposed to identify such genes, we consider an axiomatic approach based on defining properties that a specificity measure should have. This leads us to develop ember (Entropy Metrics for Biological ExploRation), which we show is the only method satisfying four key desired properties for a specificity measure. Applying ember to eight tissues from eight founder mouse strains, we find that gene specificity is often unintuitive: canonical markers can be supplanted, housekeeping genes are context-dependent, and mouse strain can drive unexpected cell type switching. Unsupervised learning on entropy metrics uncovers shared genes specialized to male gonads and kidney, as well as genes specific to non-consecutive developmental stages in the kidney. To facilitate further exploration of gene specificity in mice, we have also developed a comprehensive specificity database, along with a web interface, API and MCP server. Extending ember to a human PBMC dataset collected from 255 diverse individuals, we find that variation in PBMCs is largely localized to classical monocytes. We also find genes with unique specificity by sex, age and ancestral background. Together, these applications establish ember as a powerful tool and provide a roadmap for elucidating the impact of human genetic variation using the murine model.

Transcriptomics datasets generated using next-generation sequencing techniques such as single cell RNA-sequencing (scRNA-seq) and spatially-resolved transcriptomics (SRT) allow researchers to study patterns in gene expression across celltypes, temporal processes, and spatial organization at ever-higher resolutions and depths. scRNA-seq analyses produce gene expression profiles and celltype-specific gene sets that require annotation to provide biological meaning, a process that has traditionally relied on the manual interpretations of clinical scientists. Similarly, SRT experiments typically require subjective, time-consuming annotation of spatial domains. Recent advances in large language model (LLM) methods offer opportunities to assist in the interpretation of such datasets. Many current LLM-based approaches aim to annotate transcriptomics-derived gene sets by integrating information from publicly available and online biological resources. While these approaches can be effective, they often struggle when presented with weakly-related or fully uncorrelated genes, sometimes inferring and justifying biological relationships that are not supported by existing literature. Additionally, the quality of LLM-generated interpretations is dependent on the provision of appropriate biological context and careful prompt design, both of which can present significant barriers to effective use. To address these limitations we propose genesetGPT, an efficient, LLM-based framework that emphasizes both curated biological context and iterative prompt construction, thus enabling realistic summarization of heterogeneous gene sets at scale. genesetGPT is implemented as an open-source Python package available for download at https://github.com/jr-leary7/genesetGPT.

We developed BWR-finder (Burrows-Wheeler transform-based Repeat finder), a new software tool for database-free detection of interspersed repeats in large genomes of tens of gigabases. BWR-finder employs a BWT-based seed-and-extend repeat detection algorithm and parallelized extension computation, improving both runtime and memory usage. In benchmarks using the rice and human genomes, BWR-finder reduced runtime compared with RepeatModeler2, HiTE, and REPrise, and reduced memory usage compared with HiTE and REPrise, while maintaining high nucleotide-level repeat detection sensitivity. BWR-finder also enabled whole-genome repeat detection in genomes larger than 10 Gb and identified candidate repeat regions and repeat consensus sequences in the 20.3-Gb Pleurodeles waltl genome that were not associated with existing repeat annotations or libraries.

Rapid and accurate identification of viral sequences underpins clinical diagnostics, epidemiological surveillance, and the safety testing of biological products, yet the established alignment-based methods such as BLAST are inherently closed-set, with accuracy tied to how well a query is already represented in the reference database. Recent advances in genomic foundation models (GFMs) have enabled alignment-free approaches to sequence classification. However, their performance relative to traditional methods remains incompletely characterised. We evaluated GFMs, specifically the Nucleotide Transformer (NT) and DNABERT families, for viral sequence classification using a dataset derived from the Reference Viral Database (RVDB). Sequence embeddings were generated with mean, max, and CLS pooling, classified by nearest-neighbour search in an embedding index and compared with BLAST as a reference. Matching BLAST provided a particularly stringent benchmark here, because as a closed-set method it compares each query directly against the exact reference sequences it stores, whereas a foundation model must rely on representations shaped by broad, general-purpose pre-training. On sequences of standard length, the best model, NTv3-650M, reached an accuracy of 0.93, approaching the 0.97 achieved by BLAST. On long genomic sequences, evaluated on a much smaller test set, the difference narrowed further, with NTv3-650M reaching 0.95 against 0.98 for BLAST. Robustness analyses showed that all GFMs were markedly more sensitive to base masking than BLAST. Both methods indexed the same labelled reference sequences, and BLAST compared queries with them directly at the nucleotide level, so approaching it from frozen, general-purpose representations without any parameter update is a meaningful result. NTv3-650M emerged as the most promising candidate for further optimisation and deployment in bioinformatics applications.