Nadia M. Davidson

Long-read sequencing captures full-length transcripts but suffers elevated error rates, and PCR duplicates can distort quantification unless accounted for with UMIs. We present Nailpolish, a fast reference-free tool that error-corrects and deduplicates UMI-tagged long reads by generating consensus sequences via partial order alignment, while separating false duplicates arising from UMI collisions. Across eleven Oxford Nanopore Technologies and Pacific Biosciences datasets spanning bulk, single-cell, spatial, and targeted protocols, Nailpolish reduced per-read error rates and outperformed existing deduplication tools.

Visualizing and troubleshooting long-read sequencing data is a common task when analyzing novel protocols, but identifying several primer sequences from reads exceeding 2 kb with fuzzy matching is challenging for the human eye. Here, we present seqsizzle , a cross-platform terminal user interface (TUI) based tool for convenient visualization of long-read sequencing data directly from the command-line, applicable to all sequencing technologies that produce FASTQ or FASTA output. seqsizzle allows primer sequences to be matched with user-specified fuzzy matching thresholds and highlighted with configurable colors. Additional styling can be applied to distinguish mismatches and quality scores, allowing convenient quality control of long-read sequencing data. To further aid troubleshooting, seqsizzle includes a built-in k -mer enrichment analysis, enabling the detection of unknown primer sequences or unexpected artifacts, which can then be reviewed via manual inspection. seqsizzle is implemented in Rust and is available open-source from https://github.com/ChangqingW/SeqSizzle .

Our previous mouse genetic studies showed that loss of the transcription repressor MNT enhanced apoptosis of premalignant lymphoid cells over-expressing MYC and inhibited lymphoma development. Here, we have explored the consequences of inducing Mnt deletion in fully malignant lymphoma cells. MNT loss provoked apoptosis of p53 wt and, albeit more slowly, p53 mutant Eμ-Myc lymphoma cells, preceded by elevated levels of major BH3-only proteins BIM and PUMA. By inhibiting apoptosis, we showed that MNT loss also impaired cell cycling and increased senescence. Eμ-Myc lymphoma cells depend on the BCL-2 ortholog MCL-1 for survival and expansion and, importantly, MNT loss enhanced their sensitivity to the MCL-1 inhibitor S63845 and to several chemotherapeutic agents. In BCL-2-overexpressing Eμ-Myc lymphoma cells, which model aggressive human ‘Double Hit Lymphomas’, MNT loss enhanced sensitivity to the BCL-2 inhibitor ABT-199, even after BAX loss. Furthermore, MNT deletion improved drug responses of two long-established Burkitt Lymphoma cell lines. SIGNIFICANCE: This study establishing the MNT dependency of Eμ-Myc lymphoma cells and demonstrating that MNT loss enhances their sensitivity to apoptosis induced by conventional chemotherapeutics and BH3 mimetic drugs provides strong proof-of-concept for developing MNT inhibitors to improve treatment of MYC-driven blood cancers.

Introduction: Recently, de novo transcriptome assembly methods have been developed to utilise long-read data in cases where a reference genome is unavailable, such as in non-model organisms. Despite the potential of these tools, there remains a lack of benchmarking and established protocols for optimal reference-free, long-read transcriptome assembly and differential expression analysis. Results: Here, we evaluate the long-read de novo transcriptome assembly tools, RATTLE, RNA-Bloom2 and isONform, and compare their performance to one of the leading short-read assemblers, Trinity. We assess various metrics across a range of datasets, which include simulated data and spike-in sequin transcripts, where ground truth is known, and real data from human and pea ( Pisum sativum ) samples, using a reference-based approach to define truth. To represent contemporary analysis scenarios, the datasets cover depths from 6 to 60 million reads, Oxford Nanopore Technologies (ONT) cDNA, ONT direct RNA and Pacific Biosciences (PacBio) 10 × single-cell sequencing. Critically, we assess the downstream impact of assembly choice on the detection of differential gene and transcript expression. Conclusions: Our results confirm that long reads generate longer assembled transcripts than short-reads for reference-free analysis, though limitations remain compared to reference-guided approaches, and suggest scope for improved accuracy and reduced redundancy. Of the de novo pipelines, RNA-Bloom2, coupled with Corset for transcript clustering, was the best performing in terms of both accuracy and computational efficiency. Our findings offer guidance when selecting the most effective strategy for long-read differential expression analysis, when a high-quality reference genome is unavailable.

Background: Approximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in homologous recombination (HR) DNA repair. While there are genomic and transcriptomic methods, developed for other cancers, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR status in HGSC specifically. We have built a HGSC-specific model to predict HR status using gene expression. Methods: We separated The Cancer Genome Atlas (TCGA) cohort of HGSCs into training ( n = 288) and testing ( n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were used to train a penalised logistic regression model. Results: IdentifiHR uses the expression of 209 genes to predict HR status in HGSC. These genes preserve the genomic damage signal, capturing known regions of HR-specific copy number alteration which impact gene expression. IdentifiHR is 85% accurate in the TCGA test set and 86% accurate in an independent cohort of 99 samples, taken from primary tumours, ascites and normal fallopian tubes. Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD. Conclusions: IdentifiHR is an accurate model to predict HR status in HGSC. It is available as an open source R package, empowering researchers to robustly classify HR status when only transcriptomic sequencing data is available.

Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2 , a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation.

Arguably one of the most surprising revelations in the field of cell death research was the discovery that cellular necrosis, a lytic and inherently messy cell death with far-reaching consequences for human physiology, can be genetically encoded. There is no single necrotic pathway either, as compelling evidence exists for distinct necrotic modalities such as pyroptosis, necroptosis and ferroptosis. The recent momentum of molecular, structural and disease-relevant findings has opened the door to targeting necrotic machinery to prevent collateral tissue damage and inflammatory diseases. In this Review, we evaluate the case for targeting the necrotic cell death pathway called necroptosis. We examine the organs and cell types where the human necroptotic machinery is expressed, identifying a lymphocytic ZBP1, RIPK1, RIPK3 and MLKL signature, review knowledge into the immunogenic consequences of necroptotic signaling and highlight building evidence that necroptosis is engaged in humans and can be triggered by ischemic injuries. Finally, we note several limitations of mouse studies due to fundamental differences with the human necroptotic apparatus and critically appraise the evidence for necroptosis being a disease-driving factor that, if successfully targeted, could be of clinical benefit.

Dynamin-related protein 1 (Drp1), encoded by DNM1L , is essential for mitochondrial fission, but its functional roles remain unclear due to isoform-specific effects from alternative splicing. Short-read RNA sequencing fails to resolve full-length isoforms involving distant exons, limiting our understanding. Here, we applied targeted long-read sequencing to profile full-length DNM1L transcripts in human left ventricle and iPSC-derived cardiomyocytes, recovering all annotated isoforms with conserved expression patterns and isoforms 1-4 being most abundant. Functional assays revealed that isoform abundance does not predict enzymatic activity. Extending this to six mouse tissues, we identified distinct, tissue-enriched expression profiles. Functional rescue in Drp1-knockout mouse embryonic fibroblasts showed isoform-dependent differences in mitochondrial fission. Isoforms lacking the A-insert (e.g., b and d) robustly rescued fission, while isoforms enriched in brain or muscle showed only partial rescue, suggesting exons 2 and 3 negatively regulate Drp1 activity. Our cross-species atlas integrates long-read transcriptomics with functional validation, revealing how isoform diversity underpins tissue-specific mitochondrial dynamics and physiological roles of Drp1. Summary: Using long-read sequencing, we mapped full-length DNM1L/Dnm1l isoforms in human and mouse tissues, uncovering tissue-specific expression and isoform-dependent mitochondrial fission activity. This reveals how alternative splicing shapes Drp1 function, with implications for understanding its role in health and disease.

Long-read RNA sequencing enables full-length transcript profiling and improved isoform resolution, but variable platforms and evolving chemistries demand careful benchmarking for reliable application. We present LongBench , a matched, multi-platform reference dataset spanning bulk, single-cell, and single-nucleus transcriptomics across eight human lung cancer cell lines with synthetic spike-in controls. LongBench in-corporates three state-of-the-art long-read protocols alongside Illumina short reads: Oxford Nanopore Technologies (ONT) PCR-cDNA, ONT direct RNA, and PacBio Kinnex. We systematically evaluate transcript capture, quantification accuracy, differential expression, isoform usage, variant detection, and allele-specific analyses. Our results show high concordance in gene-level differential analyses across protocols, but reduced consistency for transcript-level and isoform analyses due to length- and platform-dependent biases. Single-cell long-read data are highly concordant with bulk for high-confidence features, though single-nuclei data show reduced feature detection. LongBench provides one of the largest publicly available long-read benchmarking resources, enabling rigorous cross-platform evaluation and guiding technology selection for transcriptomic research.

The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N ⁶-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N ⁶-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.