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bioRxiv2026

Long-read sequencing of Mycobacterial tuberculosis is comparable to short-read sequencing for antimicrobial resistance prediction and epidemiological studies.

BackgroundShort-read genetic sequencing technologies (mainly Illumina) have been extensively used for around a decade for Mycobacterium tuberculosis complex (MTBC) outbreak analysis and genomic drug susceptibility testing (gDST) with the result that Illumina has become the de facto gold standard. Long-read sequencing, as exemplified by Oxford Nanopore Technologies (ONT), offer the prospect of faster, simpler, and portable sequencing. In this work, we carry out the largest to date comparison of how well Illumina and ONT technologies sequence MTBC samples, making use of R10.4 ONT flowcells, updated basecalling models and deep-learning variant calling. MethodsA total of 508 samples were sequenced using both short and long-read platforms. All samples originated from South Africa or Vietnam and were over-selected for drug resistance and also included several local outbreaks and a range of lineages. The South African and Vietnamese samples had already been Illumina sequenced. Samples with [≥]50 read depth by Illumina were selected for sequencing by ONT using one of the GridION or PromethION platforms. Bioinformatics processing was done using a modified online cloud platform which included reference-based variant calling, catalogue-based gDST and identified related samples via SNP counting to inform outbreak detection. The lineages and gDST predictions obtained by short-and long-sequencing were compared for all samples as were all putative clusters identified via SNP counting. For convenience Illumina was used as the reference method. FindingsOf the 508 samples, 425 (83.7%) had sufficient read depths to permit comparison between the two sequencing technologies. The assigned lineages were identical for 407/425 (95.8%) samples and all discordances were due to mixed lineages being identified by one technology. Evidence of non-tuberculous mycobacterium (NTM) subpopulations were found in nine samples. Using Illumina as the reference method, the very major error (VME) rate of ONT for predicting resistance to all 15 drugs is 1.0% (0.6-1.5%) whilst the major error (ME) rate is 1.7% (1.3-2.2%) with an unclassified rate of 6.9% (6.3-7.5%). This is below the thresholds specified by the CLSI. Considering each of the 15 drugs individually they had VME and ME point estimates below [≤]3% in 29/30 cases; and most 25/30 below [≤]1.5%. Filtering out all samples containing mixtures left 382 isolates. By appropriate masking of the reference genome we were able to obtain a mean SNP distance between the two platforms of 0.13 (median of zero) for the same sample and for 376/382 samples (98.4%, CI:96.6-99.4%) the difference was [≤]1 SNPs. The high concordance in SNP identification ensured that few differences in the 43 putative clusters among 172 isolates were observed. InterpretationThe differences between the two sequencing platforms for the key clinical outputs is so small that it is now within the tolerances set by regulatory agencies. Provided the sequencing is of sufficient quality, we have therefore reached a threshold whereby sequencing data from long-and short-read platforms can be aggregated. This will enable large scale analyses by national and international public health agencies whilst allowing the MTBC community to take advantage of the portability and speed of long-read sequencing. FundingThe NIHR Health Protection Research Unit: Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford (NIHR200915), a partnership between the UK Health Security Agency (UKHSA) and the University of Oxford, the National Institute for Health and Care Research Biomedical Research Centre: Oxford (BRC) and the Ellison Institute of Technology, Oxford Ltd. The CRyPTIC project was funded by Wellcome [214560/Z/18/Z], a Wellcome Trust/Newton Fund-MRC Collaborative Award (200205/Z/15/Z); and the Bill & Melinda Gates Foundation Trust (OPP1133541). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe conducted a PubMed Central full text search for "tuberculosis" AND ("drug resistance prediction" OR "drug susceptibility prediction") AND ("genome" OR "genomic" OR "geno-typic") AND ("ont" OR "oxford nanopore") between 2022 and 2026 (conducted 1 April 2026). This returned 62 papers; of which, six used both Illumina and ONT sequencing. One of these, published in 2023, directly compared the performance of the two platforms on 151 M. tuberculosis isolates oversampled for resistance. The investigation yielded comparative results for the earlier generation ONT flow cell (R9{middle dot}4{middle dot}1) and base-caller (guppy version 5{middle dot}0{middle dot}16). Another, published in 2026, investigated a targeted next-generation sequencing panel of 20 amplicons using ONT sequencing on R10.4.1 flow cells with guppy 6{middle dot}4{middle dot}6. They compared the results on 71 isolates against phenotypic data and Illumina whole genome sequencing (for 53 isolates) but had low rates of resistance, with all drugs but isoniazid being limited to under five resistant isolates. Two other small studies (10 and 13 samples, respectively) conducted feasibility studies comparing ONT with Illumina, also using earlier generation flow cells and base-calling technology from ONT. Two further studies compared Illumina with ONT for direct sputum sequencing and did not investigate the comparative performance of the two platforms for variant call accuracy, resistance prediction, and outbreak detection. Illumina sequencing technology is widely used for genomic sequence analysis in research, and clinical and public health contexts. Consequently, it has become the de facto reference standard for generating whole genome sequence data. Whilst previous studies established the promise and limitations of long-read (ONT) sequencing as an alternative to short-read sequencing (mainly Illumina), the enhanced performance arising from newer flowcells (e.g. R10.4.1), V14 chemistry, and the latest basecallers (dorado v4.3.0/5.0.0) has not been analysed. Neither has any ONT analysis incorporating the new deep-learning variant callers been evaluated in a large-scale comparative study. Thus, it is currently unclear whether data generated by either platform can be used safely in aggregated analyses for research and clinical or public health service. Added value of this studyWe compared how well short-(Illumina) and long-read (ONT) sequencing platforms identify the genetic variants in M. tuberculosis, predict antituberculous drug resistance and recog-nise outbreaks. The long-reads were generated using the latest generation ONT R10.4.1 flows cells, V14 chemistry, super high accuracy basecalling (dorado v4.3.0/5.0.0) and a bioinformatics analysis pipeline built using the Clair3 deep-learning based variant caller. A total of 508 clinical samples were sequenced using both technologies, substantially more than previous studies. The sampling frame was much larger than previously investigations and included a large proportion of isolates with resistance to first-line and second-line antibiotics as well as bedaquiline. Thus, providing greater statistical power for resistance prediction than before. In particular, the inclusion of bedaquiline resistance provided evidence useful for predicting resistance to this newly deployed drug for treating multi-drug resistant (MDR) TB. We find that the differences between technologies are small meaning that either technology can be used alone safely, and services using both technologies can confidently aggregate the data for analysis. Implications of all the available evidenceThis will be a benefit to local, regional and international organisations, particularly public health agencies, which often have a mix of the two main sequencing technologies for characterising TB whole genome sequences. It also opens up the sequence based diagnostic market to greater competition, particularly if the observed performance can be replicated for other pathogen species.

microbiology↗
bioRxiv2026

Local sequence context determines the effect of glycine substitutions in collagen triple helices

Glycine substitutions in the collagen triple helix cause diverse heritable disorders, but their effects vary with local sequence environment. We tested whether sequence context helps determine the consequences of Gly replacement by combining case-weighted bioinformatic analysis, thermodynamic measurements on collagen model peptides (CMPs), and all-atom molecular dynamics simulations. Case-weighted analysis of pathogenic COL3A1 glycine substitutions identified Pro immediately following the substituted Gly, corresponding to a GP context, as the strongest enriched local feature. To examine this experimentally, we designed CMPs with stabilizing terminal segments flanking native collagen sequence windows containing clinically observed Gly[->]Ser and Gly[->]Arg substitutions. Gly[->]Arg substitutions were generally more destabilizing than Gly[->]Ser at the same site. However, the strongest effect was sequence-dependent: within the Gly[->]Ser class, GP-site substitutions caused larger losses of thermal stability and unfolding enthalpy than nonGP substitutions, and some GP-site Gly[->]Ser mutations were as destabilizing as Gly[->]Arg substitutions. Molecular dynamics simulations showed that all peptides remained globally triple-helical, but GP-site mutants exhibited greater loss of canonical interchain hydrogen bonds and reduced local backbone accommodation. Thus, the effect of glycine substitution in collagen depends not only on the replacing residue but also on the permissiveness of the surrounding sequence, with Pro-adjacent sites representing especially restrictive local environments. Statement of SignificanceCollagen diseases often result from replacement of a required glycine in the triple helix, but the same substitution can have very different consequences at different sites. Using clinical bioinformatics, collagen model peptides, and molecular dynamics simulations, we show that the surrounding sequence is a major determinant of mutational outcome. The most restrictive context identified here is a GP site, in which proline immediately follows the substituted glycine: Gly[->]Ser mutations at GP sites are more destabilizing than Gly[->]Ser mutations at sites without a following proline and can be as damaging as Gly[->]Arg substitutions. These results define a sequence-based rule for collagen mutational tolerance relevant to both triple-helix stability and interpretation of pathogenic variants.

biophysics↗
bioRxiv2026

ArchaeaHQ: A Curated Reference Database of Archaeal Genomes

Archaea have proven to be major players in biogeochemical cycles across diverse ecosystems, yet we still see an underrepresentation of archaeal genomes in the datasets used by popular computational biology tools. Here we present ArchaeaHQ, a quality-controlled, systematically curated reference database of 21,644 archaeal genomes compiled initially from 35,993 assemblies from all four archaeal kingdoms retrieved from NCBI: Methanobacteriati (Euryarchaeota), Thermoproteati (TACK), Nanobdellati (DPANN), and Promethearchaeati (Asgard). All genomes in the database passed standardized quality control, requiring [≥]70% completeness and [≤]10% contamination. A total of 44.2% of genomes in ArchaeaHQ achieved [≥]90% completeness, while 93.1% exhibited [≤]5% contamination. ArchaeaHQ comprises 16,199 metagenome-assembled genomes (MAGs; 74.8%) and 5,445 isolate genomes (25.2%). Approximately 75% of MAGs are assigned to 17 ecologically meaningful categories based on sampling origin, and around 65% of genomes include geographic metadata. ArchaeaHQ is available at https://doi.org/10.6084/m9.figshare.32266599 and provides an analysis-ready reference set for metagenomic classification, biogeochemical and ecological studies, comparative genomics, and development of archaeal-specific bioinformatic tools. Impact StatementArchaea are key drivers of the global carbon, nitrogen and methane cycles, yet their genomes remain underrepresented and inconsistently curated in the public databases that power modern computational biology tools. We present ArchaeaHQ, a quality-controlled, systematically curated reference set of 21,644 archaeal genomes spanning all four archaeal kingdoms, each passing standardized completeness and contamination thresholds and enriched with environmental and geographic metadata. By providing an analysis-ready, downloadable resource compatible with standard pipelines, ArchaeaHQ fits the gap between taxonomy-focused frameworks and unfiltered genome archives supporting metagenomic classification, biogeochemical and ecological studies, comparative genomics, and the development of archaeal-specific bioinformatic tools.

microbiology↗
bioRxiv2026

Long-read cross-platform validation reveals novel repeat features in myotonic dystrophy type 2

The broader application of long-read sequencing (LRS) for repeat expansion characterization in myotonic dystrophy type 2 (DM2) and other repeat expansion disorders (REDs) remains limited by the lack of systematic validation and benchmarking of sequencing results and bioinformatic workflows. Here, we performed an orthogonal cross-platform validation of previously generated Oxford Nanopore Technologies (ONT) data by sequencing the same DNA samples with Pacific Biosciences (PacBio) HiFi following amplification-free targeted enrichment in a cohort of 8 DM2 patients. Despite substantial differences in sequencing chemistry and coverage, the two platforms showed high concordance in repeat size estimation, somatic mosaicism, and repeat architecture. This validation confirmed the presence of the (TCTG)n motif and enabled the identification of a previously unreported (CCCG)n motif at the 3' end of expanded alleles, further highlighting the structural complexity of the CNBP expansion. Through this analysis, we also established a bioinformatic workflow that improved ONT-based repeat characterization, addressing limitations in motif resolution and enabling more accurate analysis of CNBP expansions. Overall, this study provides a validated framework for LRS-based CNBP repeat analysis, supporting the integration of these technologies into routine molecular investigation for DM2 and other REDs.

genomics↗
bioRxiv2026

Metagenomic prediction of methane emissions in sheep using single- and multi-matrix BLUP models with taxonomic and functional microbial features

BackgroundEnteric methane emissions from ruminant livestock represent a major greenhouse gas contributor, yet identification of high- and low-emitting ruminants remains expensive and logistically challenging for agricultural methane mitigation strategies. Ruminal microbial profiles derived from long-read sequencing technology provide a potential proxy to predict methane production. The optimal bioinformatic pipelines for processing long-read metagenomic data to perform methane predictions have yet to be determined. Here we evaluated how different metagenomic analysis pipelines affect methane predictive model accuracy in grazing sheep. ResultsWe applied three bioinformatic pipelines to characterize the taxonomic and functional features of rumen microbiomes from 396 sheep. Functional abundance features were annotated from Clusters of Orthologous Genes (COG) or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The single-matrix model using COG features achieved the highest microbiability (m2 = 0.942: proportion of variance component explained by microbial features) and predictive accuracy (5-fold cross validation r = 0.609: Pearsons correlation between predicted and observed values). Both functional features outperformed all taxonomic features across all three pipelines in predictive accuracy. The multi-matrix models combined functional and taxonomic features slightly improved methane predictive accuracy across both 5-fold cross-validation and leave-one-day-out validation compared to the models using functional features alone. ConclusionsThese findings demonstrate the potential advantages of using long-read metagenomic data to predict enteric methane emissions in ruminants. COG-based functional features achieved the highest predictive accuracy among all feature types, suggesting that functional annotation of existing long-read sequences is sufficient for accurate methane prediction without requiring complementary taxonomic data.

genomics↗
bioRxiv2026

Single-cell multi-omics analysis reveals heterogeneity and plasticity of neutrophil states in response to immunotherapies

BackgroundTumor-associated neutrophils (TANs) are emerging as functionally heterogeneous and plastic cells in the tumor microenvironment. In immunologically cold tumors, elevated neutrophil abundance correlates with poor prognosis and resistance to immune checkpoint inhibition (ICI). Whether distinct anti-tumoral neutrophil states can be induced by different immunotherapies and how they relate to treatment efficacy remains unclear. MethodsUsing the syngeneic MOC2-huEGFR (M2h) mouse model of head and neck squamous cell cancer (HNSCC), we treated tumor-bearing mice with agonistic anti-CD40 monoclonal antibody (mAb) (aCD40), TNF, Cetuximab, or a combination of all three, designated Neutrophil Activating Therapy (NAT). In addition to evaluating anti-tumor efficacy, we performed single-cell multiomics RNA and protein sequencing, followed by bioinformatics analyses and flow cytometry validation. NAT-induced anti-tumor efficacy and related neutrophil states were also assessed in another cold tumor model, 9464D-GD2 neuroblastoma. Murine treatment-induced neutrophil gene signatures were then evaluated using clinical, proteomic, and transcriptomic data from HNSCC patients. ResultsFive transcriptionally distinct neutrophil states (N0-N4), including precursor state CD49d+ N4, were identified using the M2h model. N0 neutrophils (immunosuppressive/quiescent) dominated untreated tumors, but not in successful treatments. ISG+ N1 neutrophils and CCR3+ N3 neutrophils expanded by aCD40, TNF, and NAT treatment with anti-tumoral gene signatures and found more interacting with CD8+ T cells from bioinformatics analysis. N2 neutrophils reflected a recently established hypoxia-adapted state found in all treatments. ICAM1 (CD54) emerged as a marker of treatment-induced neutrophil activation, discriminating N1, N2, and N3 neutrophils from N0 neutrophils, validated by flow cytometry. In the 9464D-GD2 neuroblastoma model, NAT treatment also reduced the N0 dominance seen in untreated tumors in the HNSCC model but failed to induce anti-tumoral neutrophil states. In 23 HNSCC patients who received ICI therapy, ICAM1 protein expression in neutrophils trended toward association with responder status (TMA-level p=0.029), and ICAM1 neutrophil gene expression also trended toward association with improved overall survival in TCGA data (HR=0.75, p=0.059). ConclusionsDistinct immunotherapy-induced neutrophil states are defined by transcriptional profiles enriched in different functional pathways, associated with both anti-tumor and pro-tumor signatures. ICAM1 identifies activated neutrophils and potentially serves as a biomarker of ICI response in HNSCC, warranting further clinical validation. WHAT IS ALREADY KNOWN ON THIS TOPICNeutrophil heterogeneity has received increasing attention, with studies identifying antitumoral neutrophil populations, either at baseline or induced by treatment. Several effective treatment regimens involve an anti-CD40 agonist (aCD40) antibody, among them Neutrophil Activating Therapy (NAT), which combines aCD40, TNF, and a tumor antigen binding antibody designed to reprogram neutrophils. NAT could thus be particularly effective in cold, myeloid-rich tumors that are largely unresponsive to conventional immunotherapies such as checkpoint blockade, enacting these anti-tumoral effects through similar and different mechanisms; however, this has not been tested. WHAT THIS STUDY ADDSThis study adds a single-cell multi-omics framework for defining treatment-induced neutrophil heterogeneity in MOC2-huEGFR and 9464D-GD2 tumors, two immunologically cold models. It highlights ICAM1/CD54 and interferon-stimulated genes as markers of a dominant antitumor neutrophil state, while showing that neutrophil state composition variy across tumor models. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICYThese results support the efficacy of a myeloid-modulating therapy built around aCD40 and TNF in a cold murine head and neck cancer model, and to a lesser extent in a cold murine neuroblastoma model. ICAM1/CD54 expression in neutrophils was also identified as a promising marker of antitumor activity and treatment response. More broadly, this work suggests that incorporating aCD40 and/or TNF into existing treatment regimens could improve outcomes, while ICAM1/CD54-high neutrophils may serve as a useful therapeutic readout.

cancer biology↗
bioRxiv2026

An end-to-end framework for single-cell-resolution, whole-transcriptomic spatial profiling in post-mortem human brain

Single-cell resolution spatial transcriptomics enables transcriptome-wide molecular profiling within intact tissue architecture, providing unprecedented opportunities to investigate cellular organization and disease-associated molecular states in the human brain. However, applying these technologies to post-mortem human brain tissue remains challenging due to RNA degradation, heterogeneity in tissue preservation, and a lack of standardized analytical workflows. These challenges are particularly pronounced for whole-transcriptome platforms, where successful implementation requires optimization of both experimental and computational procedures. Here, we present an end-to-end framework for single-cell-resolution, whole-transcriptome spatial transcriptomics of fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) post-mortem human brain tissue. The framework combines an optimized experimental workflow with a preservation-agnostic bioinformatics pipeline for data processing, integration, and annotation. Experimentally, we show that a condensed two-day Visium HD workflow provides improved library quality, lowered qPCR cycle thresholds, and more consistent fragment size distributions. Sequencing saturation analyses further identified cost-effective sequencing depths that maximize transcript recovery while minimizing redundant sequencing. Computationally, we established a scalable workflow incorporating DAPI-based nuclear segmentation, transcript assignment, quality control, reference-guided integration, clustering, and cell-type annotation. We implemented a reference-based highly variable gene selection strategy to enable robust cross-sample harmonization independent of tissue preservation method. Application of this framework to seven Alzheimers disease frontal cortex specimens (five FF and two FFPE) generated a unified single-cell spatial transcriptomic atlas comprising more than 530,000 spatially resolved cells. The integrated dataset resolved major neuronal, glial, and vascular cell populations, recapitulated expected cortical architecture, and enabled direct comparison of FF- and FFPE-derived spatial transcriptomic profiles. Together, this work provides a practical experimental and computational framework for single-cell resolution, whole-transcriptome spatial transcriptomics in post-mortem human brain tissue and delivers a publicly available resource that expands the utility of archived and frozen specimens for studies of neurodegeneration and other neurological disorders. ImportanceSpatial transcriptomics of human post-mortem brain tissue is limited by RNA degradation, preservation variability, and lack of standardized workflows. Here, we present an end-to-end framework combining an optimized two-day Visium HD protocol with a preservation-agnostic bioinformatics pipeline. This approach improves library quality, defines efficient sequencing strategies, and enables robust spatial profiling across both FF and FFPE samples. Applied to Alzheimers disease brain tissue, it generates high-resolution single-cell spatial data and expands the utility of archived and frozen specimens for studying neurodegenerative diseases.

neuroscience↗
bioRxiv2026

Whole-genome sequencing data of a diverse grapevine germplasm collection maintained in Bordeaux, France

Grapevine (Vitis vinifera) is one of the worlds most economically important fruit crops and a model species for perennial fruit tree genetics and genomics. The extensive genetic diversity found in cultivated and wild Vitis species provides a valuable resource for studies of domestication, adaptation, trait evolution, and breeding. This article presents a standardized whole-genome variant dataset comprising 547 grapevine accessions maintained in the INRAE Bordeaux grapevine germplasm collection, including 397 domesticated V. vinifera cultivars and 150 wild Vitis accessions. Whole-genome sequencing data were generated at a target sequencing depth of approximately 20x, and sequence variants were identified using a standardized Genome Analysis Toolkit (GATK) workflow against the reference genome PN40024v4 (40X). Variant discovery was carried out simultaneously across the complete sample set to ensure consistent genotype calling. All accessions were sequenced using the same technology and processed using the same reference genome, sequence alignment, variant-calling, and filtering workflow to produce a standardized variant dataset comprising ca. 9.1M SNPs and 0.77M INDELs. The resulting VCF files provide a harmonized genomic resource that can be readily reused for studies of grapevine genetics, germplasm characterization, population genomics, comparative genomics, genome-wide association studies, and the development and benchmarking of bioinformatic methods. SPECIFICATIONS TABLE O_TBL View this table: org.highwire.dtl.DTLVardef@14f2b12org.highwire.dtl.DTLVardef@94b5acorg.highwire.dtl.DTLVardef@1232f80org.highwire.dtl.DTLVardef@1038e85org.highwire.dtl.DTLVardef@81ec71_HPS_FORMAT_FIGEXP M_TBL C_TBL VALUE OF THE DATAO_LIThis dataset provides whole-genome raw sequencing for 118 wild Vitis accessions originating from North America and Asia and a sequencing-derived variant data for these accessions and 429 Vitis vinifera cultivars and wild accessions from the INRAE Bordeaux germplasm collection, previously published by Dong et al. 2023[1]. The dataset captures genetic variation across a total of 547 grapevine accessions, including domesticated and wild grapevine germplasm using a common variant-calling pipeline, facilitating direct comparisons among accessions. C_LIO_LIThe inclusion of wild Vitis species together with cultivated grapevine accessions provides a resource for studies of grapevine diversity, domestication, phylogenetic relationships, and comparative genomics. The dataset enables the investigation of genetic variation across multiple Vitis species using a standardized set of genomic variants. C_LIO_LIThe variant call format (VCF) file can be reused for population genetics, phylogenetic analyses, genetic diversity assessments, introgression analyses, and the identification of genomic regions of interest. The dataset is compatible with widely used bioinformatics software and can be integrated with other publicly available grapevine genomic resources. C_LIO_LIThis dataset constitutes a genomic resource for grapevine breeding and conservation research. Researchers can use these data to identify genetic diversity in wild relatives, compare allelic variation between cultivated and wild germplasm, investigate candidate loci associated with traits of interest, and support the management and characterization of grapevine germplasm collections. C_LI

genomics↗
bioRxiv2026

Lysine methyltransferase SET7 links cardiometabolic risk to endothelial dysfunction by dysregulating mRNA splicing and eNOS-CaM interaction

BACKGROUNDLysine methyltransferase SET7 activates gene expression through mono- methylation of histone H3 at lysine 4 (H3K4me1) and modulates protein function via mono- methylation of non-histone proteins (Kme1). However, its role and molecular targets in endothelial dysfunction associated with cardiometabolic disorders remain unknown. METHODSEndothelial-specific Setd7 knockout (Setd7EC-KO) mice were generated and endothelial function assessed in WT and Setd7EC-KO mice after high-fat diet (HFD). Human aortic endothelial cells (HAECs) were used to investigate SET7 expression and function under metabolic stress. SET7-dependent histone and non-histone targets were identified by proteomic and ChIP analyses. Insights from these datasets guided the design of bioinformatic, molecular, and functional studies to define their regulatory mechanisms. Clinical relevance was evaluated in human arteries. RESULTSHFD selectively increased endothelial SET7 expression in WT mouse aortas. Despite comparable metabolic abnormalities, Setd7EC-KO mice were protected from HFD- induced endothelial dysfunction, oxidative stress, and inflammation. In HAECs, high glucose emerged as the strongest inducer of SET7 expression, promoting pro-inflammatory and pro- oxidant gene expression, monocyte adhesion, and ROS generation. These effects were reproduced by overexpression of catalytically active SET7 and reversed by its inhibition or silencing. Proteomic and ChIP analyses revealed that SET7-dependent H3K4me1 activates transcription of spliceosome components, linking aberrant mRNA splicing to endothelial inflammation and oxidative stress. Moreover, Kme1-proteomics identified endothelial nitric oxide synthase (eNOS) as a direct SET7 substrate. Bioinformatic analyses and mutagenesis experiments demonstrated that SET7-mediated mono-methylation of eNOS at K494 disrupts calmodulin (CaM) binding and impairs NO synthesis. These molecular signatures were also observed in internal mammary arteries from patients with vascular disease and hyperglycemia. CONCLUSIONSSET7 drives endothelial dysfunction through a dual mechanism: 1) H3K4me1-dependent activation of splicing machinery triggering inflammation and oxidative stress, and 2) eNOS mono-methylation at K494 reducing NO bioavailability. Targeting SET7 may therefore represent a promising avenue to safeguard endothelial homeostasis in cardiometabolic disease. GRAPHIC ABSTRACTA graphic abstract is available for this article.

molecular biology↗
bioRxiv2026

Potato Agent: AI-Driven Data and Knowledge Exploration on an Agent-Ready Potato Multi-Omics Platform

Potato is an important non-cereal food crop worldwide. However, the limited number of functionally validated genes remains a major bottleneck to favorable allele stacking and genome design breeding in potato. Rapid advances in AI agents offer a promising means to support crop breeding by translating natural-language questions into coordinated data analysis and knowledge retrieval. Their reliable use for potato breeding, however, is constrained by fragmented multi-omics resources that lack consistent curation and machine-accessible interfaces. Here, we constructed an agent-ready potato multi-omics database integrating genomic resources from 150 potato accessions, 259 bulk RNA-seq samples, and 14 spatial transcriptomic datasets into a pangenome, a tissue expression atlas, co-expression networks, and spatial expression maps accessible through open APIs. We developed 39 potato-specific Agent Skills for reproducible bioinformatics analysis and comprehensive data and knowledge exploration, enabling natural-language questions to be translated into standardized data-retrieval and analysis tasks. By integrating direct evidence from potato studies, functions of homologous genes in Arabidopsis, rice, and maize, and tissue expression patterns, we generated genome-wide functional predictions for 37,658 genes in the DM reference genome. We further developed Potato Agent as a multi-user, browser-based platform with isolated workspaces and online result preview, reducing the technical burden of agent deployment and providing direct access to integrated data, knowledge, and workflows. Case studies demonstrated its capabilities in reproducible bioinformatics analysis, agent-assisted identification of a tuber development regulator, scientific data visualization, and haplotype-aware promoter analysis and sgRNA design. Together, the agent-ready database and Potato Agent provide an integrated infrastructure for functional gene discovery and hybrid breeding in potato.

plant biology↗
bioRxiv2026

Discovery of a novel UV-absorbing mycosporine-like amino acid in Vertebrata lanosa using an expanded combinatorial structure database incorporating non-proteinogenic amino acids and organic solutes

Mycosporine-like amino acids (MAAs) are among nature's most effective sunscreen compounds, capable of converting harmful ultraviolet radiation into harmless heat, and are widely distributed in marine organisms such as red macroalgae. Although decades of research have led to numerous discoveries, the rate of new MAA identifications has declined. To address this, we considerably expanded our previously developed combinatorial MAA database, increasing the number of covered structures tenfold. Following a comprehensive literature search for plausible but undescribed building blocks, the database now incorporates an extensive set of proteinogenic and non-proteinogenic amino acids, as well as other marine organic osmolytes, in combination with all (currently) known MAA scaffolds. This expanded resource was integrated into our identification platform, which combines UHPLC-VWD-HRMS2 analysis, feature-based molecular networking, and bioinformatics-driven annotation. Application of this updated workflow enabled the isolation and structural elucidation of a novel MAA, mycosporine-cysteinolic acid, from the red marine macroalga Vertebrata lanosa. Altogether, this study provides a valuable extension of the bioinformatics-based MAA screening pipeline, enhancing the annotation and discovery of novel MAAs in natural matrices.

plant biology↗
bioRxiv2026

FetchPA: a guided, end-to-end solution for local ATAC-Seq data processing and analyses

Local genome accessibility strongly correlates with activity of cis-regulatory elements, and Assay for Transposase-Accessible Chromatin coupled with next-generation sequencing (ATAC-Seq) has emerged as method of choice to profile chromatin accessibility in both healthy and pathogenic conditions. The introduction of streamlined protocols and manufacturer kits has made this technique accessible to labs of a variety of disciplines, background, and research interests. Many bioinformatics tools have been created for the quality control, mapping, and visualization of ATAC-seq data, however these tools require familiarity with shell scripting, version control, UNIX directory structure, Python and/or R. Several pipelines for the processing of ATAC-seq data have been developed, yet even with these tools, bioinformatic analyses represents a bottleneck between wet-lab protocol execution and graphical representation of differentially accessible regions. To address this problem, we assembled FetchPA, an intuitive pipeline which allows users with virtually no scripting and version control experience to install and manage all software for end-to-end analyses of ATAC-Seq data. FetchPA handles both local and public repository sources of sequencing data, executes standard QC benchmarks, and handles genome assembly and alignment using industry-standard PEPATAC pipeline. Further, it guides the user through the identification of differentially accessible regions and allows basic exploratory analyses via a dialogue interface. FetchPA 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, reference genome annotations and system resource allocation.

genomics↗
bioRxiv2026

FAST-MaP: Chemical Mapping of RNA Structures Using Primer-less Sequencing

RNA structure governs the function of non-coding RNAs and influences mRNA stability and translation, making testing of models of experimental RNA structure relevant to fields spanning structural biology, virology, and molecular therapeutics. Here we present FAST-MaP (Fast and Accessible Sequencing Technology for Mutational Profiling), a protocol that enables per-nucleotide RNA structure characterization using standard molecular biology equipment and requiring no sequencing infrastructure or bioinformatics expertise. RNA is chemically modified with orthogonal probes (2A3 and DMS), reverse-transcribed, and PCR-amplified to produce dsDNA amplicons that are submitted directly to a commercial primer-less sequencing service. Returned FASTQ files are processed through a freely accessible web server to generate normalized reactivity profiles within minutes. The complete protocol, from DNA template to structural data, can be completed in approximately one week. We illustrate the workflow on a 659-nucleotide RNA, demonstrating how to test structure preservation across buffers, and how to test specific secondary and tertiary structure predictions of the RNA from computational modeling or cryo-electron microscopy. The protocol requires only standard molecular-biology skills and does not require sequencing or bioinformatics expertise.

biochemistry↗
bioRxiv2026

RNA-sequencing of butyrate-treated human lymphoblastoid cell lines shows dysregulated genes of multiple disease-related pathways

Abstract Butyrate, a 4-carbon fatty acid produced from insoluble starch by the gut microbiome, plays key roles in mitochondrial respiration, activation of cell-surface G-protein-coupled receptors, and protein acetylation by inhibiting histone deacetylases (HDACs). Butyrate (as sodium salt) added to drinking water was shown to improve pathological symptoms in numerous model mouse disorders, including models for cancer metastasis, neuroinflammation, autism spectrum disorders, dementia, insulin resistance, and colorectal cancer. Over 5000 PubMed-listed studies feature butyrate in their title, yet its genome-wide transcriptomic effects have not been reported in human cell lines from multiple healthy donors. Here, we report the genome-wide transcriptomic effects of 1 mM sodium butyrate on human lymphoblastoid cell lines (LCLs) generated from blood samples of eight unrelated healthy donors. Paired bioinformatics analysis comparing the butyrate-treated LCLs to the same untreated cell line identified thousands of differentially expressed gene (DEG) transcripts with padj<0.05, of which over half were upregulated by butyrate. Pathway analysis showed that butyrate exposure of LCLs upregulated extracellular matrix, cell adhesion, junction organization, Wnt signaling, axonogenesis, and neurodevelopment pathways. Downregulated pathways included cell cycle, G2/M checkpoint, E2F/MYC targets, DNA replication and repair, mitotic spindle integrity, mTOR signaling, oxidative phosphorylation, unfolded-protein response, and translation/ribosome-related pathways. Our RNA-seq study, utilizing LCLs from healthy donors and paired bioinformatic analysis, opens venues for assessing butyrate effects on cellular pathways and potential therapeutics for cancer, metabolic, immune, and CNS-related disorders.

genomics↗
bioRxiv2013

PyRAD: assembly of de novo RADseq loci for phylogenetic analyses

Restriction-site associated genomic markers are a powerful tool for investigating evolutionary questions at the population level, but are limited in their utility at deeper phylogenetic scales where fewer orthologous loci are typically recovered across disparate taxa. While this limitation stems in part from mutations to restriction recognition sites that disrupt data generation, an alternative source of data loss comes from the failure to identify homology during bioinformatic analyses. Clustering methods that allow for lower similarity thresholds and the inclusion of indel variation will perform better at assembling RADseq loci at the phylogenetic scale.\n\nPyRAD is a pipeline to assemble de novo RADseq loci with the aim of optimizing coverage across phylogenetic data sets. It utilizes a wrapper around an alignment-clustering algorithm which allows for indel variation within and between samples, as well as for incomplete overlap among reads (e.g., paired-end). Here I compare PyRAD with the program Stacks in their performance analyzing a simulated RADseq data set that includes indel variation. Indels disrupt clustering of homologous loci in Stacks but not in PyRAD, such that the latter recovers more shared loci across disparate taxa. I show through re-analysis of an empirical RADseq data set that indels are a common feature of such data, even at shallow phylogenetic scales. PyRAD utilizes parallel processing as well as an optional hierarchical clustering method which allow it to rapidly assemble phylogenetic data sets with hundreds of sampled individuals.\n\nAvailabilitySoftware is written in Python and freely available at http://www.dereneaton.com/software/\n\nSupplementScripts to completely reproduce all simulated and empirical analyses are available in the Supplementary Materials.

Bioinformatics↗
bioRxiv2014

KmerStream: Streaming algorithms for k-mer abundance estimation

Motivation: Several applications in bioinformatics, such as genome assemblers and error corrections methods, rely on counting and keeping track of k-mers (substrings of length k). Histograms of k-mer frequencies can give valuable insight into the underlying distribution and indicate the error rate and genome size sampled in the sequencing experiment.\n\nResults: We present KmerStream, a streaming algorithm for computing statistics for high throughput sequencing data based on the frequency of k-mers. The algorithm runs in time linear in the size of the input and the space requirement are logarithmic in the size of the input. This very low space requirement allows us to deal with much larger datasets than previously presented algorithms. We derive a simple model that allows us to estimate the error rate of the sequencing experiment, as well as the genome size, using only the aggregate statistics reported by KmerStream and validate the accuracy on sequences from a PhiX control.\n\nAs an application we show how KmerStream can be used to compute the error rate of a DNA sequencing experiment. We run KmerStream on a set of 2656 whole genome sequenced individuals and compare the error rate to quality values reported by the sequencing equipment. We discover that while the quality values alone are largely reliable as a predictor of error rate, there is considerable variability in the error rates between sequencing runs, even when accounting for reported quality values.\n\nAvailability: The tool KmerStream is written in C++ and is released under a GPL license. It is freely available at https://github.com/pmelsted/KmerStream\n\nContact: pmelsted@hi.is

Bioinformatics↗
bioRxiv2014

Using 2k + 2 bubble searches to find SNPs in k-mer graphs

This preprint is now available in published form as: Using 2k + 2 bubble searches to find SNPs in k-mer graphs, Reda Younsi; Dan MacLean, Bioinformatics 2014; doi: 10.1093/bioinformatics/btu706\n\nSingle Nucleotide Polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterisation of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage.We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment based methods. We tested accuracy of the SNP calling using sequence data from sixteen ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further.Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs.The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at:https://github.com/redayounsi/2kplus2

Bioinformatics↗
bioRxiv2014

Data mining of Gene expression profiles of Saccharomyces cerevisiae in response to mild heat stress response

We chose yeast as a model organism to explore how eukaryotic cells respond to heat stress. This study provides details on the way yeast responds to temperature changes and is therefore an empirical reference for basic cell research and industrial fermentation of yeast. We use the Qlucore Omics Explorer (QOE) bioinformatics software to analyze the gene expression profiles of the heat stress from Gene Expression Omnibus (GEO). Genes and their expression are listed in heat maps, and the gene function is analyzed against the biological processes and pathways. We can find that the expression of genes changed over time after heat stress. Gene expression changed rapidly from 0 min to 60 min after heat shock, and gene expression stabilized between 60 min to 360 min. The yeast cells begin to adjust themselves to the high temperatures in terms of the level of gene expression at about 60 min. In all of the involved pathways and biological processes, those related to ribosome and nucleic acid metabolism declined in about 15?30 min and those related to starch and sucrose increased in the same time frame. Temperature can be a simple way to control the biological processes and pathways of cell.

Bioinformatics↗