Genome Research

Nuclear bodies are nucleoprotein complexes with established functions that target chromatin at specific locations and regulate specific RNA processing functions, thereby influencing gene expression. However, the mechanisms that define how nuclear bodies are targeted to specific locations within the genome where they function remain poorly understood. One significant challenge is capturing and understanding the multiple cell-specific interactions occurring in these complexes, arising from RNA components interacting with each other and with DNA and nucleic acid-binding proteins within the context of the nucleuss three-dimensional organization. Mapping these interactions is critical for elucidating mechanisms such as RNA splicing, a key driver of cell-specific transcript diversity. Here, we use RNA-DNA Split Pool Recognition of Interactions by Tag Extension (RD-SPRITE) to characterize, for the first time, sex-specific RNA-RNA and RNA-DNA interactions in Drosophila S2 (male) and Kc (female) cells. We determined the sex-specific RNA-RNA interaction map within the nucleus and, using RNA-DNA interaction data, pinpointed the target loci of various RNA molecules, including small nuclear RNAs (snRNAs), which are core components of the spliceosome-a ribonucleoprotein complex involved in RNA splicing. Based on RNA-RNA interaction data, we also identified novel long non-coding RNAs that may regulate splicing. Furthermore, we investigated the role of transcription factor (TF) CLAMP in sex-specific targeting of the spliceosome. We generated RD-SPRITE datasets in the presence and absence of CLAMP, a key TF involved in dosage compensation, sex-specific RNA splicing, and chromatin organization. We determined that CLAMP regulates global changes in spliceosomal interactions with chromatin, inhibits aberrant snRNA interactions, and regulates sex-specific interactions of RNAs involved in splicing function. Additionally, our dataset provides a valuable resource for investigating additional processes, such as miRNA-mediated silencing, nucleolar functions of snoRNAs, and Cajal body functions of scaRNAs, among others. To facilitate broad community use, we have developed a computational platform, "FlySprite," that enables Drosophila researchers to explore sex-specific RNA-RNA interactions, as well as DNA targets of RNA clusters, through a user-friendly interface.

Cell-to-cell transcriptional heterogeneity, or noise, is an intrinsic property of the transcriptome with implications for development, disease progression, and aging. Bulk RNA-seq masks this variability by averaging gene expression across cells, whereas single-cell RNA sequencing (scRNA-seq) resolves it. Nevertheless, separating biological noise from technical variance remains challenging, particularly across platforms with different chemistries. We benchmarked two widely adopted technologies, Evercode WT (SPLiT-seq, Parse Biosciences) and Chromium (10x Genomics), on human lymphoblastoid nuclei. Evercode WT achieved targeted sequencing depth and nuclei number far more reliably, and its random-hexamer priming yielded more intronic reads and non-coding RNA genes; Chromium recovered more cells and detected polyadenylated transcripts and cell-line markers more sensitively. Despite these opposing biases, the platforms showed comparable gene detection and strongly correlated expression profiles. Using datasets from both platforms, we defined a noise metric detrended from mean expression and showed that per-gene estimates were reproducible across chemistries. Noise was lower in G2M than in G1 and was most strongly associated with gene length rather than exonic length. Expression of genes with CpG-island promoters was less variable than that of those without. This study establishes a platform-independent basis for quantifying transcriptional noise and a framework for selecting an appropriate scRNA-seq platform.

Short tandem repeats (STRs) are a rich and highly polymorphic source of human genetic variation, but representing and genotyping them in pangenome graphs remains challenging. Explicitly encoding each STR allele as a separate graph path results in increasingly complex local structures as cohort diversity increases, leading to larger index sizes and requiring significant resources for graph reconstruction when new alleles are introduced. Here, we propose STR-PG, a topologically decoupled genome-wide framework that separates stable locus representation from scalable STR allele content. STR-PG uses topologically fixed pointer nodes to represent each target locus, while allele sequences, repeat counts, motif annotations, and population frequency metadata are stored in an external registry. Short reads are mapped to STR loci via syncmer-based flanking anchors, and genotyping is performed within a locus-specific candidate space using allele-level alignment likelihood and Bayesian inference. Newly supported alleles can be integrated through registry-level updates without the need to rebuild the graph structure. Evaluations using simulated whole-genome sequencing data, 1000 Genomes Project (1kGP) samples, and r real whole-exome sequencing data from matched whole-blood-cell controls demonstrate that STR-PG maintains accurate genotyping results across various STR classes, reproduces expected population structures, and substantially reduces the computational cost of integrating additional alleles. STR-PG provides a compact and scalable framework for population-scale STR analysis using short-read sequencing.

Existing pedigree approaches to identifying de novo mutations (DNMs) require at least two parents and a single offspring, limiting applicability. Here, we introduce OOPS (Only One Parent Sequencing), a framework for detecting DNMs using only a single parent-offspring pair. OOPS uses short-read data from the parent and both short and long-read data from the offspring to reconstruct haplotypes in the child, one of which can then be assigned to the sequenced parent. We show that candidate de novo mutations from the assigned haplotype can be identified, allowing for estimation of the mutation rate. To demonstrate the accuracy of OOPS, we apply it to a human pedigree in which mutations have also been identified using standard trio-based approaches. OOPS achieves comparable accuracy to trio-based pipelines and recovers consistent mutation rate estimates. By removing the requirement for complete trio sequencing, OOPS expands mutation rate estimation to a wider range of settings.

Isoform-resolved transcriptomics is fundamental to decoding the molecular complexity of the human brain, yet population-scale long-read RNA sequencing has remained inaccessible due to labor-intensive library preparation, sensitivity to RNA degradation in postmortem tissue, and the absence of integrated, reproducible analysis pipelines. Here we present SALRR (Scalable Analysis of Long-Read RNA-seq), an integrated wet-lab and computational platform designed to overcome these barriers. Automated ONT long-read cDNA library preparation on the Hamilton Microlab NGS STAR platform reduces hands-on time by 67% and enables 24 libraries per operator per day while maintaining performance across RNA integrity values. A modular, Snakemake-based pipeline performs end-to-end processing from ONT signal data to isoform-level quantification, incorporating SIRV spike-in calibration, multi-stage quality control, and stringent isoform validation. Applied to 10 postmortem frontal cortex samples from the North American Brain Expression Consortium, SALRR identified 31,607 high-confidence isoforms from 10,075 genes, including 8,532 novel splice variants absent from GENCODE v49, and complex splicing events systematically missed by short-read sequencing at neurodegeneration-relevant loci, including GBA1, CCNF, CHCHD10, and TREM2. All protocols and code are openly available, providing a scalable, community-ready framework for isoform-resolved transcriptomics in neurodegeneration, aging, and complex brain disease.

Spatial perturbation profiling is becoming an important tool in functional genomics because it reveals how genetic interventions reshape transcription within intact tissue contexts. However, destructive readout and limited screening capacity leave many perturbation-by-location response profiles unmeasured, motivating the task of spatial perturbation profile completion. The task is to infer the held-out response population at query locations from reported profiles of the same perturbation. Existing methods either generate responses de novo or reuse these profiles without spatial adaptation. These strategies make it difficult to preserve empirical population structure while modeling location-specific variation. Our key insight is that the reported population already defines an empirical response distribution for the target perturbation. To exploit this empirical support, we propose Reference-Anchored Dynamic Flow (RADF), which employs a Sinkhorn-balanced decoder to construct a population-valued anchor in which every reference profile has equal total contribution. Additionally, a bounded dynamic relational flow is used to recompute spatial relations from the evolving expression state and query geometry. Across diverse spatial contexts, RADF reduces macro E-distance by 70.6% compared with an existing state-of-the-art spatial method, highlighting the advantage of combining a reference-supported population anchor with bounded, location-dependent refinement. Code will be made publicly available upon acceptance.

More than twenty years ago, the FANTOM consortium uncovered that mammalian genomes are pervasively transcribed, revealing multitudes of RNAs with unknown functions. A subset of these transcripts has since then been linked to transcriptional control and to chromatin organization via their ability to interact with DNA, suggesting that chromatin-associated RNAs could be key players in genome regulation. Although recent technological advances now enable the mapping of genome-wide RNA-DNA contacts, a lack of analyses integrating these methods with other genomic features and across multiple cellular contexts hinders our comprehensive understanding of the principles underlying RNA-DNA interactions and of their biological importance. As part of the FANTOM6 project, we thus generated RNA-DNA interaction maps in 16 different human cell types, then combined these contacts with multiple layers of other genomic data to investigate how patterns of interaction between RNA and DNA relate to chromatin organization and function. We show that the RNA-DNA interactome is highly dynamic yet reproducibly organized in cell-type specific networks, constituted of a great diversity of interactions that vary in function of their distance, the nature of their sources and the chromatin state of their targets. In particular, we detected numerous regulatory elements that exhibit marked changes in activity when differentially bound by transcripts, implying that thousands of RNA-DNA interactions can play a mechanistic role in gene expression. This regulatory function correlates with RNA-protein interactions and significantly associates with cell type-relevant and disorder-related traits. In addition to providing essential resources for future research in RNA-mediated chromatin regulation, cellular biology and human diseases, our study thus establishes the RNA-DNA interactome as a new genome regulatory layer that defines and maintains cellular identity and behavior.

Most genetic diversity stems from spontaneous mutations, that is, errors in DNA repair or replication. But for dozens of organisms across the tree of life, mutations at specific loci are not spontaneous but developmentally programmed: effectively, some organisms edit their own DNA sequences. This is perhaps most common among pathogens and parasites, many of which use editing to diversify genes that produce important antigens. Plant-parasitic potato cyst nematodes are damaging agricultural pests that establish a lifelong feeding site inside the root of their host plant. We previously observed extensive diversity of rare alleles at HYP1, the most highly expressed gene that encodes a protein secreted by potato cyst nematodes during parasitism. Importantly, HYP1 alleles differ from each other by complex, in-frame rearrangements of short repeated sequence motifs within a single exon. Combining several lines of evidence, we previously hypothesized that potato cyst nematodes use developmentally-programmed mutations, or editing, to diversify HYP1 alleles in the soma. In the current work, we now test this hypothesis. We employ highly accurate long-read DNA sequencing of a simplified genetic system to identify potential rare edited alleles, we use a transgenic yeast system to describe large de novo mutations at HYP1, and we interpret our findings in light of key population genetic parameters as well as the genetic diversity surrounding HYP1 and across the genome. Significance StatementIn parasites, genetic diversity is valuable fuel for the coevolutionary arms race with their hosts. However, in parasitic nematode worms, low genetic diversity is often observed. Several other parasites and pathogens (e.g., single-celled eukaryotes) generate genetic diversity in an unusual way: they edit the DNA sequence of an important gene in their own genome, instead of waiting for rare spontaneous mutations to occur. This has never been observed in a plant parasite, but we previously described a major parasitism gene (HYP1) in plant-parasitic nematodes with such diverse DNA sequences that it looks like it could perhaps be edited. Now, we generate better data for a simplified genetic system and show that much of the observed HYP1 diversity was actually cryptic DNA sequencing errors. Although there is real genetic diversity at HYP1, it is best explained not by editing but by fundamental evolutionary forces that operate across the genome.

Mutation spectra vary among populations, and a comprehensive view of this variation requires broad sampling and fine-scale population resolution. Here, we profile mutation spectra from single-nucleotide polymorphisms and small insertions and deletions in a uniformly processed collection of 3,034 globally sampled Saccharomyces cerevisiae genomes, and an extended dataset including the previously sampled global 1,011-genome collection. Major mutation spectrum patterns reported from the 1,011 genomes remain evident in the extended dataset, including the pronounced mutation-spectrum differentiation of African Beer strains. At finer population resolution, we identify additional variation among wine-associated populations, including a Georgian Wine-associated signal that is strongest among rare variants and is therefore consistent with a comparatively recent change in mutation processes. We further apply a sequence-context-aware classification to characterize small-indel profiles across the 3,034 natural yeast genomes. Together, our analyses show that expanded sampling and refined population assignments provide a more comprehensive view of mutation-spectrum variation within S. cerevisiae.

Centromeres play essential roles in chromosome segregation and genome stability, yet they remain among the least characterized regions of the human genome. Despite advances in long-read sequencing and complete genome assembly, the extreme repetitiveness and structural complexity of these regions still challenge population-scale analysis, obscuring their mutational dynamics. The Human Pangenome Reference Consortium has now accurately assembled over 6,000 centromeres, providing an opportunity to catalog global centromere variation. However, centromeric regions have been systematically excluded from pangenome alignments due to the technical challenge of aligning their highly repetitive tandem arrays and extreme structural variability. Here we introduce Centrolign, a graph-based multiple sequence alignment tool that combines a uniqueness-driven objective function with partial-order partial-order alignment to accurately align alpha satellite higher-order repeats. By prioritizing rare matches within tandem arrays and leveraging extended centromere-spanning haplotypes formed by suppressed recombination, Centrolign produces progressive multiple sequence alignments that preserve ancestral repeat organization. Applied across human centromeres, these alignments reveal the phylogenetic structure of similar satellite array haplotypes and enable precise estimation of variation rates, structural variant frequencies, and spatial patterns of mutation within satellite arrays. Integrating Centrolign graphs with repeat annotation tools and pangenome mapping algorithms allows accurate variant calling and genotyping from long reads without prior assembly. Moreover, we show that centromere haplotypes can be accurately subtyped with k-mers alone. Together, these advances establish a robust framework for incorporating centromeres into broader pangenomes, and population genomics in general, advancing our understanding of human genome evolution and diversity.

With improvements in sequencing and assembly have come many high-quality telomere-to-telomere assemblies and reference pangenomes. However, the long-read sequencing recipes needed for high quality assemblies are expensive, and out of reach for many research groups. Here we propose ImpuT2T, a method that takes an assembly produced via inexpensive HiFi sequencing reads, and uses a panel of T2T (or near-T2T) assemblies to scaffold and fill ("patch") the gaps between the HiFi contigs. Benchmarking against reference assemblies demonstrates that ImpuT2T is highly effective at patching human HiFi assemblies, consistently outperforming existing patching approaches. Moreover, we show that including more haplotypes in the pangenome improves the quality of the patched assemblies, with the greatest gains achieved using the full HPRC Release 2 pangenome.

Replication stress is a major source of genome instability in cancer, yet the genomic features that determine where DNA double-strand breaks (DSBs) arise remain incompletely defined. Here, we establish an integrated genome-wide resource of DSBs, DNA replication, replication timing, transcription, and R-loops in two widely used cancer cell lines, U2-OS and HeLa, under steady-state conditions and following prolonged low-dose DNA polymerase inhibition. We combine these datasets with systematic statistical testing and comparative analytical approaches to define the replication environments associated with genome fragility. Endogenous DSBs preferentially accumulated at origin-rich initiation zones, where R-loops were enriched, whereas prolonged DNA polymerase inhibition redirected DSB formation toward origin-poor, late-replicating regions. Among the replication features examined, replication initiation zones and late-replicating areas were most sensitive to prolonged replication stress. R-loops were specifically enriched at initiation zones but depleted from late-replicating regions and recurrent DNA break clusters (RDCs), demonstrating that their association with genome fragility is context dependent. Replication stress further induced RDCs within long, actively transcribed genes, while their locations only partially overlapped with common fragile sites. Functional analyses identified MUS81 as a major regulator of RDC formation. MUS81 loss increased RDCs, whereas restoration of its catalytic activity suppressed them, indicating that MUS81 resolves replication intermediates before they persist into late-replicating fragile regions. Together, this resource and analytical framework provide a systematic basis for dissecting how replication architecture, transcription, and DNA processing shape genome fragility in cancer cells.

Drosophila melanogaster repeatome, comprising roughly 20% of the genome, is characterized by a large fraction of active TE families counterbalanced by efficient purifying selection. Consequently, many TE families persist at low copy numbers and are frequently population specific. Hybridization and horizontal transfer provide new families, which can spread through natural populations within decades. These dynamics, together with years of independent curation efforts, left the D. melanogaster mobilome distributed across several, partly redundant, libraries. Prompted by submissions of population-specific data, we undertook a complete overhaul of the D. melanogaster TE libraries, begun in Dfam 3.9 and finalized in Dfam 4.0. We cross-referenced the new submissions against Repbase, FlyBase, the Berkeley Drosophila Genome Project, and our own Dfam 3.8 to resolve redundancy and reconcile names, then rebuilt or newly constructed the seed alignment for most families. Seeds came from four sources: the dm6 reference itself, which supported the majority of models; insertions >100 bp from 13 samples of a recently published D. melanogaster pangenome; the genomes of other members of the D. melanogaster subgroup, which supplied copies for older families too degraded in dm6 alone; and diverged matches recovered during iterative curation, which resolved into subfamilies and previously undescribed relatives. Rebuilding the seeds corrected consensus sequences that were truncated, chimeric, or skewed by co-duplicated fragments, and lowered the mean Kimura divergence of annotated copies from their consensus. The revision also added families with no prior Dfam representation, including the DNA P-element (absent from dm6) and a collection of novel families that have recently invaded natural populations. Following manual curation, the new library contains 398 models, up from 226 in Dfam 3.8, and annotates an additional 1.5% of the dm6 reference.

Precise control of gene expression relies in part on cis-regulatory elements (CREs), including enhancers. Genetic variation within enhancers can alter their regulatory activity and contribute to variation in gene expression, yet the effects of multiple nearby variants within the same enhancer remain poorly understood. To address this, we designed a massively parallel reporter assay (MPRA) to test the regulatory effect of 7,285 pairs of close-proximity single-nucleotide variants (SNVs) selected from blood-specific and broadly active enhancers. To enrich for functionality, we required at least one variant in each pair to be annotated as an eQTL in eQTLGen dataset. We measured the regulatory activity of all four haplotypes in K562 leukemia cells, where 57% of tested variant pairs had at least one derived haplotype that differed significantly in activity from the ancestral haplotype. The effects of individual variants frequently depended on the allelic background provided by the neighboring variant, with some variants showing opposite effects in different allelic backgrounds. Among a smaller, high-confidence subset selected for analysis of additivity, 59% (105/178) of variant pairs showed non-additive effects, and non-additive pairs were located closer together than additive pairs. Among pairs in which both single-derived haplotypes increased activity, the double-derived haplotype generally showed a smaller effect than expected under additivity. Together, our results demonstrate that nearby variants within the same enhancer can jointly shape regulatory activity and highlight the importance of considering local allelic context when interpreting the functional effects of regulatory variation.

Topoisomerase cleavage complexes (TOPcc) are covalent protein-DNA intermediates formed transiently during topoisomerase catalysis and constitute a major class of DNA-protein crosslinks in human cells, yet their endogenous genomic distributions remain difficult to resolve. Here, we developed protein-DNA adduct immunoprecipitation followed by sequencing (pDAIP-seq), a highly sensitive method for detecting endogenous TOPcc that enables their genome-wide profiling under normal physiological conditions without stabilization by topoisomerase poisons. Using pDAIP-seq, we found that camptothecin treatment, required by existing TOP1cc profiling methods, not only increased TOP1cc abundance but also substantially altered its genomic distribution. Endogenous TOP1cc were globally reduced upon transcriptional suppression and increased upon TOP2A depletion, supporting TOP1cc regulation by the maintenance of genome-wide DNA topological homeostasis. Interestingly, both the regional distribution and nucleotide-scale positioning of TOP1cc, including the positions of TOP1 catalytic cleavage sites, were largely specified by local DNA sequence context. By extending pDAIP-seq to capture transient covalent protein-DNA intermediates involved in DNA repair, including TDP1-, PARP1-, and KU70-DNA adducts, we further identified endogenous TOP1cc as a potential major source of spontaneous DNA breaks in human cells. Finally, pDAIP-seq also enabled genome-wide mapping of TOP2cc, TOP3cc, and SPO11-DNA covalent intermediates in mouse testis undergoing programmed meiotic DNA cleavage. Together, these findings establish pDAIP-seq as a broadly applicable method for the mapping of covalent protein-DNA intermediates and reveal endogenous TOP1cc as a key intermediate between DNA topological regulation and human genome instability.

Z-DNA is a left-handed DNA conformation implicated in gene regulation and chromatin dynamics. Because it is usually less thermodynamically favorable than canonical B-DNA under physiological conditions, computational tools are needed to identify sequences likely to adopt the Z conformation. Legacy Z-Hunt uses a dinucleotide thermodynamic model, but searches every anti/syn assignment in a window, causing its conformation search to grow exponentially with window size. We present Z-Hunt-DP, an exact dynamic programming reformulation that preserves the original thermodynamic objective while reducing this search from O(2^d) to O(d) for a window of d dinucleotide positions. On benchmark windows, Z-Hunt-DP matched the brute-force minimum energy within numerical tolerance and achieved a 3.30x10^5 speedup at 20 dinucleotides. In an interval-localization benchmark on public human loci with experimentally mapped Z-DNA, it recovered the clipped reference interval in all 24 cases and was the most stable localizer under midpoint-core and expanded-panel analyses. Since the comparison set mixes thermodynamic, heuristic, and learned models, these cross-tool results are interpreted as localization comparisons rather than direct thermodynamic score tests. The source code and benchmark materials are available at https://github.com/Aljumaily/Z-Hunt-DP.

Genomic foundation models (GFMs) are pretrained on large genomic datasets, but their ability to predict rare disease-associated events after fine-tuning remains unclear. We tested this using TDP-43 cryptic exons, well characterized splicing events linked to neurodegenerative disease that are poorly represented in standard annotations. We compared the state-of-the-art GFM AlphaGenome with OpenSpliceAI, a specialized splice site prediction model. Fine-tuned OpenSpliceAI detected held-out cryptic exons in human and mouse and captured allele-specific splicing effects at the disease-associated UNC13A locus. In contrast, a splicing prediction head trained on AlphaGenome's frozen pretrained backbone failed to predict cryptic exons. Unfreezing and fine-tuning all model weights, however, enabled AlphaGenome to generalize to held-out data. To investigate cryptic splicing in cell types that are difficult to study experimentally, we applied fine-tuned OpenSpliceAI across the genome to study cell type-specific genes. This led to the identification of new cryptic splicing events in CLDN11 and ARHGAP23, which were subsequently validated in FTLD-TDP brain tissue. Notably, the cryptic exon in CLDN11 is predicted to generate a neoepitope that could serve as an oligodendrocyte-specific biomarker of TDP-43 pathology. Our results show that even a state-of-the-art GFM can fail to generalize to rare splicing events, even with substantial pretraining. However, both foundation and specialized models that learn to generalize to rare biological events can have broad applications in biological discovery and medicine.

Detecting somatic mutations is a cornerstone of cancer genomics and clinical genotyping, traditionally relying on whole exome (WES) or whole genome sequencing (WGS) data. While RNA sequencing (RNA-seq) is primarily used to assess gene expression, it can also be utilized for somatic variant detection. RNA-seq inherently captures expressed variants, thereby reducing the identification of passenger mutations and mitigating the annotation biases associated with WES. We performed somatic variant calling and cancer driver detection from tumor RNA-seq data from 1,359 RNA-seq samples with matched WGS from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Our approach using RNA-seq data, alone, identified at least one putative driver gene in 15 of 16 cancer types, consistent with driver predictions from WGS. We further observed that the variant allele frequency (VAF) of RNA-seq-derived variants reflects their predicted functional impact, providing information specific to transcriptomic data. Because RNA-seq data can be generated and analyzed at lower computational cost and time than WGS or WES, it represents a resource-efficient alternative for cancer driver detection. This study provides an extensive pan-cancer resource of RNA-seq-derived variants from the PCAWG consortium and supports the utility of RNA-seq as a complementary approach for characterizing expressed somatic variation in cancer.

The maternal-to-zygotic transition (MZT) is a major developmental event during which inherited transcripts are remodeled and zygotic transcription is established. Although parentally inherited long noncoding RNAs (lncRNAs) are present in early embryos, they have been thought to be dispensable. We have identified more than 2000 inherited lncRNAs in zebrafish embryos, but how these RNAs participate in regulatory programs during early development has remained unexplored. Here, the inheritance of selected zebrafish lncRNAs spanning a broad expression range were confirmed at the pre-MZT stage and full-length sequences were captured by Direct RNA nanopore sequencing. We show that 30% inherited intergenic lncRNAs are preferentially associated with active enhancers, annotated as such in DANIO CODE, whereas non-inherited intergenic lncRNAs rarely overlap with enhancers. Perturbation of five inherited intergenic lncRNAs, individually, using antisense oligonucleotides reduced the expression of their respective neighboring genes at 2.5, 4.3, and/or 6 hours post fertilization, indicating that these RNAs act as positive local regulators during MZT. Together, these findings identify inherited intergenic lncRNAs as enhancer-associated regulators with elncRNA-like properties during early embryogenesis.

Infected cells rapidly adapt their transcriptional profile to upregulate genes that limit pathogen replication and promote inflammation. Viruses also induce widespread changes in host gene expression, and both host and virus further remodel the transcriptome through targeted mRNA degradation, modulation of alternative splicing, and alterations at transcript 5 and 3 prime ends. The complexity of the response to infection makes it difficult to understand which transcriptional changes are driven by the virus and which reflect the host antiviral program. Short-read RNA sequencing has been the primary tool for studying these changes, but its inability to resolve full-length transcript isoforms limits isoform switching-based analyses, narrowing our understanding of how transcript usage shapes immunity. Here we profiled the host transcriptome in response to interferon-{beta} treatment or influenza A virus infection using both short- and long-read RNA sequencing analyzed with the SQANTI3 pipeline. Because isoform switching and transcript abundance change are distinct regulatory events, we focused our analysis on differential transcript usage to uncover a layer of transcriptome remodeling that standard expression analysis cannot detect. Pathway analysis revealed distinct regulons for genes undergoing transcriptional upregulation compared with those undergoing isoform switching. These data provide a rich resource for querying the isoform changes underlying shared and distinct transcriptional programs triggered by viral infection or innate immune stimulation. To identify putative mechanisms coordinating these stimulation-dependent isoform changes, we integrated 232 eCLIP datasets and found a small set of RNA-binding proteins localized at these isoform switches, including DDX3X. Comparing the responses to interferon and to virus highlighted RNA-binding proteins associated with infection-dependent immune remodeling. Altogether, these results demonstrate the value of hybrid sequencing for deciphering the post-transcriptional regulatory code active during the antiviral response.