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PubMed2026

Systemic immune activation in hereditary cancer predisposition syndromes: a cross-sectional study.

BACKGROUND: Immune surveillance mechanisms contribute to the elimination of precancerous lesions in hereditary cancer predisposition syndromes (HCPSs). METHODS: By combining single-cell transcriptomics, multiparametric mass cytometry and cytokine profiling of the systemic immune environment in 391 individuals among whom 227 are living with HCPSs we investigated phenotypic alterations in cancer-free individuals with HCPS. RESULTS: A decrease in peripheral B cell abundance and their more differentiated phenotype have been confirmed both in breast cancer patients with germline pathogenic variants in BRCA1 (gpath(BRCA1)) and in patients living with Lynch syndrome (LS). Pre-cancer women with gpath(BRCA1) exhibited an activated phenotype of multiple immune cell lineages, similar to those with manifest disease. In LS, B cell phenotypes exhibited the largest changes in response to cancer eradication, while increased peripheral IL-6 levels was detected even in presymptomatic individuals with LS. CONCLUSIONS: HCPS-specific differences in the phenotype of the systemic immune system might be leveraged in future risk-reducing strategies.

Humans↗
PubMed2026

Profiler: an open web platform for multi-omics analysis.

MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities. RESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research. AVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).

Software↗
PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗
PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗
PubMed2026

Whole-transcriptome-scale isoform-resolved spatial imaging of single cells in tissues.

Cell and tissue functions arise from complex interactions among numerous genes, and a systematic understanding of these functions requires isoform-resolved transcriptomic analysis of single cells with high spatial resolution. Here, we introduce an in situ RNA amplification method and its integration with multiplexed error-robust fluorescence in situ hybridization (MERFISH) to detect short RNA sequences and enable whole-transcriptome-scale, isoform-resolved spatial transcriptomics of individual cells in intact tissues. Using this approach, we imaged ∼33,000 distinct RNAs-including ∼23,000 genes and ∼10,000 isoforms-in the mouse brain. Our data enabled systematic analyses of region- and cell-type-specific gene programs and ligand-receptor-based cell-cell communications. These data further revealed rich spatial diversity and cell-type specificity in isoform usage across numerous genes, as well as brain structures particularly rich in isoform specificity. We anticipate broad application of this method for characterizing the molecular and cellular basis of tissue functions, unlocking previously inaccessible discoveries in cell and organismal biology.

Animals↗
PubMed2026

A High-Resolution Stereo-Seq Spatial Transcriptomic Resource for Adult Holstein Cattle Liver.

The bovine liver is a highly compartmentalized organ that plays essential roles in continuous gluconeogenesis and nitrogen recycling; however, its spatial molecular architecture has remained largely uncharacterized due to the limitations of traditional bulk and single-cell approaches. To address this gap, Spatial Enhanced Resolution Omics-sequencing (Stereo-seq) was utilized to generate a subcellular-resolution (500 nm) transcriptomic map of an adult Holstein cattle liver, and a refined reference-guided workflow was implemented to overcome standard annotation limitations in livestock. Raw sequencing data were processed using the Stereo-seq Analysis Workflow and analyzed with Stereopy, Seurat, SingleR, and reference-guided workflows. Spatial aggregation was evaluated at Bin20, Bin50, Bin100, Bin150, and Bin200. Increasing bin size increased molecular identifier counts and detected-gene complexity while progressively reducing spatial granularity. Bin50, corresponding to 50 × 50 DNA nanoballs and an approximate nominal footprint of 25 × 25 µm, was therefore selected as a practical intermediate aggregation level for the primary analyses. Quality-control assessment, Leiden clustering, UMAP visualization, reference-based cell-type annotation, cluster-marker analysis, and spatial mapping of canonical hepatic genes demonstrated preservation of biologically interpretable liver transcriptional organization. Raw sequencing data processed spatial matrices, annotated objects, and analysis code are publicly available to support reanalysis and computational benchmarking. In summary, we present a Stereo-seq spatial transcriptomic resource generated from liver tissue of an adult Holstein cow. This initial resource provides a valuable foundation for future studies of bovine liver biology, comparative genomics, and the spatial basis of livestock health and production traits.

Animals↗
PubMed2003

Molecular analysis of cancer using DNA and protein microarrays.

In conclusion, array-based technologies have emerged that contribute to profiling tissues at the genomic, transcriptomic and proteomic levels. Analytical tools are needed to mine the vast amount of data generated. Ultimately the molecular analysis of cancer at a genome and proteome scale will allow better classification of disease and tailored individualized therapy for individual patients.

Biotechnology↗
PubMed2026

Mining the sHSP20 (small heat-shock protein) gene family in finger millet (Eleusine coracana (L.) Gaertn.): structural, evolutionary and predicted abiotic-stress-responsive insights.

Small heat-shock proteins (sHSPs, the HSP20 family) are ATP-independent molecular chaperones that hold partially unfolded substrates and protect the proteome during heat and other abiotic stresses; every member is defined by a conserved &#x3b1;-crystallin domain (ACD). Finger millet (Eleusine coracana) is a climate-resilient, calcium-rich allotetraploid cereal of the semi-arid tropics whose HSP20 repertoire had not been catalogued. The present study is an entirely computational (in silico) analysis of the chromosome-scale reference genome of finger millet (NCBI GenBank assembly GCA_032690845.1, cultivar KNE 796-S). Mining the predicted proteome with the ACD profile (Pfam PF00011) and confirming every candidate by NCBI CD-search recovered 76 non-redundant ACD-bearing HSP20 genes (EcHSP20-1-EcHSP20-76). Based on phylogeny and TargetP-predicted localization, the members were classified into ten subfamilies: seven cytosolic/nuclear classes (C-I to C-VII, 60 members) together with chloroplastic (11), mitochondrial (3) and endoplasmic-reticulum (2) groups. The proteins ranged from 110 to 355 amino acids (12.1-39.2&#xa0;kDa) with theoretical pI of 4.85-9.69. The 76 loci were distributed over 14 of the 18 chromosomes and were conspicuously absent from chromosomes 8&#xa0;A, 8B, 9&#xa0;A and 9B, with pronounced clustering on chromosomes 1, 2, 3 and 6. Duplication analysis detected 149 paralogous pairs (49 homoeologous, 80 segmental/dispersed and 18 tandem); 147 of 148 pairs for which substitution rates could be calculated returned Ka/Ks&#x2009;<&#x2009;1 (mean 0.20), indicating strong purifying selection consistent with retention after whole-genome/allopolyploid duplication. Promoter analysis (PlantCARE) revealed enrichment of abscisic-acid-responsive (ABRE), MYB/MYC drought-related, STRE, DRE, low-temperature (LTR) and methyl-jasmonate/salicylic-acid elements, whereas canonical heat-shock elements (HSE) were not recovered. Expression profiling against a public drought transcriptome (SRP081350) showed that about half of the genes (39 of 76) are transcribed in leaf tissue, the expressed fraction being dominated by the cytosolic class C-I. This first finger-millet HSP20 catalogue provides a verified, reproducible framework and nominates computationally predicted candidate genes for future functional work on thermotolerance in cereals.

Allotetraploid↗
PubMed2026

A ribozyme ligase that requires a 3' terminal phosphate on its RNA substrate.

Ribozymes likely played essential roles in catalyzing metabolic processes and facilitating genome replication in primordial RNA-based life. In vitro evolution has allowed us to expand the biochemical capabilities of RNA, especially new ribozyme chemistries. Here, we report the serendipitous discovery of ribozyme ligases that catalyze the attack of the 2'-hydroxyl group of an RNA substrate on its own 5'-triphosphate group, but only when the substrate possesses a 3'-phosphate vicinal to its nucleophilic 2'-hydroxyl group. The ligases' requirement for a 3'-phosphate group on its substrate resembles enzymatic mechanisms found in protein-based RNA repair pathways. We propose that ribozyme-catalyzed ligation of 3'-phosphorylated RNA could have provided pathways for RNA repair in primordial cells. We demonstrate that these ribozymes ligate specifically to 3'-phosphorylated RNA present in a heterogeneous mixture of cellular RNAs. We further show that these ribozymes can capture cleaved RNAs with 3'-phosphate and 2'-3'-cyclic phosphate termini, enabling us to selectively amplify the captured RNAs. These results demonstrate their potential utility as enrichment reagents for profiling RNA cleavage products in transcriptomics studies. Our findings not only report a new catalytic reactivity in RNA but also provide insights into ribozyme evolution, primordial RNA repair, and potential applications in RNA sequencing.

RNA, Catalytic↗
PubMed2026

Mapping convergent regulators of melanoma drug resistance by PerturbFate.

High-throughput genomic studies have uncovered associations between diverse genetic alterations and disease phenotypes. However, elucidating how perturbations in functionally disparate genes give rise to convergent cellular states remains challenging. Here we present PerturbFate, a high-throughput, cost-effective, combinatorial-indexing single-cell platform that enables systematic interrogation of massively parallel CRISPR interference1 perturbations across the full spectrum of gene regulation, from chromatin remodelling and nascent transcription to steady-state transcriptomic phenotypes. Using PerturbFate, we profiled more than 300,000 cultured melanoma cells to characterize multimodal phenotypic and gene regulatory responses to perturbations in more than 140 vemurafenib resistance-associated genes. We uncovered a shared dedifferentiated cell state marked by convergent cooperative transcription factor activities across diverse genetic perturbations. We further dissected phenotypic responses to perturbations in Mediator complex components, linking module-specific biochemical properties to convergent transcriptional activations. We identified common regulatory nodes that drive similar phenotypic outcomes across distinct genetic perturbations. We also delineated how perturbations in functionally unrelated genes reshape cell state. Thus, PerturbFate establishes a versatile platform for identifying key molecular regulators by anchoring multimodal regulatory dynamics to disease-relevant phenotypes.

Humans↗
PubMed2026

A nucleolar stress gene signature enables quantitative scoring across multi-omics contexts.

The nucleolus is essential for ribosome biogenesis and cellular homeostasis, and its dysfunction can induce nucleolar stress, a process implicated in cancer and other diseases. However, nucleolar stress is commonly inferred from morphological changes or a limited set of functional assays, and quantitative approaches based on gene expression profiles remain lacking. Here, we integrate literature curation with multi-dataset screening to define a nucleolar stress gene signature and develop a nucleolar stress score (NuS) applicable to bulk transcriptomics, single-cell transcriptomics, proteomics, and spatial transcriptomics. Using this framework, we show in colorectal cancer models that oxaliplatin induces nucleolar stress, suppresses nascent rRNA synthesis, and activates p53 signaling, whereas these responses are attenuated in oxaliplatin-resistant cells. Combined with a ribosome biogenesis activity score (RiboSis), NuS captures related but distinct dimensions of nucleolar function and stratifies tumors into functional states associated with clinical outcomes. NuS-based analysis of perturbational transcriptomes further prioritizes compounds with putative nucleolar stress-inducing activity. Collectively, this study provides a quantitative framework for evaluating nucleolar stress and illustrates its applications in disease stratification and drug mechanism discovery.

Cell Nucleolus↗
PubMed2025

Dynamic changes in chromosome and nuclear architecture during maturation of normal and ALS C9orf72 motor neurons.

We have investigated changes in chromosome conformation, nuclear organization, and transcription during differentiation and maturation of control and mutant motor neurons harboring hexanucleotide expansions in the C9orf72 gene that cause amyotrophic lateral sclerosis (ALS). Using an in vitro reprogramming, differentiation and neural maturation protocol, we obtained highly purified populations of post-mitotic motor neurons for both normal and diseased cells. As expected, as fibroblasts are reprogrammed into iPSCs, and as iPSCs differentiate into motor neurons, chromatin accessibility, chromosome conformation, and nuclear organization change along with large-scale alterations in transcriptional profiles. We find that the transcriptome changes extensively during the first three weeks of post-mitotic neuronal maturation, with thousands of genes changing expression, but then is relatively stable for the next three weeks. In contrast, chromosome conformation and nuclear organization continue to change over the entire 6-week maturation period: chromosome territoriality increases, long-range interactions along chromosomes decrease, compartmentalization strength increases, and centromeres and telomeres increasingly cluster. In motor neurons derived from ALS patients such changes in chromosome conformation were much reduced. Chromatin accessibility changes also showed delayed maturation. The transcriptome in these cells matured relatively normally but with notable changes in expression of genes involved in lipid, sterol and mitochondrial function. We conclude that neural maturation is associated with large scale post-mitotic changes in gene expression, chromosome conformation and nuclear organization, and that these processes are defective in motor neurons derived from ALS patients carrying C9orf72 hexanucleotide repeat expansions.

Journal Article↗
PubMed2026

GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.

Spatial transcriptomics technologies have revolutionized the analysis of spatial gene expression, yet integrating spatial information and generating data across heterogeneous samples remain challenging. We present GenOT, a generative framework combining multi-scale graph self-supervised contrastive learning with optimal transport barycenter theory for efficient cross-slice and cross-platform spatiotemporal interpolation. The core innovation of GenOT lies in introducing an optimal transport barycenter-based interpolation algorithm, which mathematically models spatial distribution differences across heterogeneous samples to reconstruct spatiotemporal gene expression dynamics. Extensive evaluations demonstrate that GenOT consistently outperforms existing approaches in spatial domain identification, cross-platform interpolation, and developmental trajectory reconstruction.

Spatial Transcriptomics↗
PubMed2026

Representation learning for multi-modal spatially resolved transcriptomics data.

MOTIVATION: Spatial transcriptomics enables in-depth molecular characterization of samples on a morphology and RNA level while preserving spatial location. Integrating the resulting multi-modal data is an unsolved problem, and developing new solutions in precision medicine depends on improved methodologies. RESULTS: We introduce AESTETIK, a convolutional deep learning model that jointly integrates spatial, transcriptomics, and morphology information to learn accurate spot representations. AESTETIK yielded substantially improved cluster assignments on widely adopted technology platforms (e.g. 10x Genomics&#x2122;, NanoString&#x2122;) across multiple datasets. We achieved performance enhancement on structured tissues (e.g. brain) with a 21% increase in median ARI over previous state-of-the-art methods. Notably, AESTETIK also demonstrated superior performance on cancer tissues with heterogeneous cell populations, showing a 2-fold increase in breast cancer, 79% in melanoma, and 21% in liver cancer. We expect that these advances will enable a multi-modal understanding of key biological processes. AVAILABILITY AND IMPLEMENTATION: AESTETIK is implemented in Python 3 and is available as open source software at http://www.github.com/ratschlab/aestetik. The Snakemake pipeline for reproducing the results is available at http://www.github.com/ratschlab/st-rep.

Spatial Transcriptomics↗
PubMed2002

Generation of kidney transcriptomes using serial analysis of gene expression.

Chronic renal disease initiation and progression remain incompletely understood. Genomewide expression monitoring should clarify the mechanisms which cause progressive renal disease by determining how clusters of genes coordinately change their activity. Serial analysis of gene expression (SAGE) is a technique of expression profiling which permits simultaneous and quantitative analysis of 9- to 13-bp sequence tags that correspond to unique mRNAs. Key principles of the technique are use of PCR in a manner to minimize distortion and serial concatenation of tags which facilitates sequencing and permits identification of many expressed genes in a single cDNA molecule. Tags are extracted from many concatenated sequences, counted using software, and identified by comparison with existing gene databases. In aggregate, gene expression profiles generated from a tag library comprise a transcriptome which represents a comprehensive and quantitative profile of genes expressed at the time of analysis. These global snapshots of gene expression patterns can better define basic cell biology and provide insights into disease pathogenesis by simultaneously determining the net consequences of gene-gene and gene-environment interactions on expression of thousands of genes. Rather than applying a priori assumptions (i.e., hypothesis testing), transcriptome analysis is hypothesis generating and requires no prior knowledge of gene expression. SAGE kidney transcriptomes, from normal animals and animals with progressive kidney disease, are being produced and can be analyzed for novel pathogenetic mechanisms. The use of SAGE and other genomic and proteomic tools should result in a better understanding of kidney disease pathogenesis and in identification of new therapeutic targets.

Animals↗
PubMed2026

Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection.

BACKGROUND: The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear. RESULTS: In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (P&#x2009;=&#x2009;0.004), and both groups showed decreased loads one week later (symptomatic, P&#x2009;=&#x2009;0.001; asymptomatic, P&#x2009;=&#x2009;0.035). Compared with benchmarking 16&#xa0;S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho&#x2009;=&#x2009;0.90, Pmax < 0.001). A total of 670 bacteria species were identified to show a relative abundance&#x2009;&#x2265;&#x2009;0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log2(fold change or FC) < -1 and adjusted P&#x2009;<&#x2009;0.05) compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host's gene expression using an expression quantitative trait bacteria (eQTB) model, we found 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted P&#x2009;<&#x2009;0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation. CONCLUSIONS: In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.

Humans↗
PubMed2024

Single cell RNA sequencing provides novel cellular transcriptional profiles and underlying pathogenesis of presbycusis.

Age-related hearing loss (ARHL) or presbycusis is associated with irreversible progressive damage in the inner ear, where the sound is transduced into electrical signal; but the detailed mechanism remains unclear. Here, we sought to determine the potential molecular mechanism involved in the pathogeneses of ARHL with bioinformatics methods. A single-cell transcriptome sequencing study was performed on the cochlear samples from young and aged mice. Detection of identified cell type marker allowed us to screen 18 transcriptional clusters, including myeloid cells, epithelial cells, B cells, endothelial cells, fibroblasts, T cells, inner pillar cells, neurons, inner phalangeal cells, and red blood cells. Cell-cell communications were analyzed between young and aged cochlear tissue samples by using the latest integration algorithms Cellchat. A total of 56 differentially expressed genes were screened between the two groups. Functional enrichment analysis showed these genes were mainly involved in immune, oxidative stress, apoptosis, and metabolic processes. The expression levels of crucial genes in cochlear tissues were further verified by immunohistochemistry. Overall, this study provides new theoretical support for the development of clinical therapeutic drugs.

Animals↗
PubMed2026

PotatoRTD and TomatoRTD: Comprehensive Reference Transcript Datasets for Accurate Transcriptome Analysis and Isoform Discovery.

Transcriptome annotations provide essential information on transcript locations, sequences and structures, including transcription start, end sites and splice junctions. They underpin key biological analyses such as gene and transcript quantification, and the study of transcriptional and post-transcriptional regulation, including alternative transcription initiation, polyadenylation and splicing. Accurate characterisation of transcript isoforms is critical for understanding how gene expression relates to functional protein products. However, for many species-including Solanaceae crops such as potato and tomato-current annotations suffer from limited isoform coverage, with tens or hundreds of thousands of splice junctions and transcript isoforms missing. This undermines the completeness and accuracy of transcript-level analyses. Here, by generating Iso-seq and RNA-seq on a range of tissues and samples, we have produced transcriptome annotations for both potato and tomato with improved coverage, diversity, accurate splice junctions, and transcript start and end sites. We have also made these high-quality resources accessible through genome browsers. These enhanced annotations will enable more accurate transcriptome analyses, supporting higher-resolution and novel biological discoveries.

Solanum tuberosum↗