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A major challenge in the analysis of RNA-seq data at the transcript-level is accounting for the variability introduced during quantification of RNA sequencing reads. This variability is due to the high levels of sequence similarity among transcripts annotated to the same genomic locus and the mapping ambiguity resulting from the assignment of sequence reads to such transcripts. The quantification uncertainty associated with transcript-level estimated counts is intractable to measure analytically but represents an extra source of variation that seriously compromises differential transcript expression (DTE) analyses if standard statistical methods developed for gene-level analyses are used. Bootstrap counts, as provided by popular RNA-seq quantification tools, allow one to estimate the quantification uncertainty and account for such an effect in DTE analyses. We presentcatchSalmonandcatchKallisto, two functions included in the R/Bioconductor packageedgeR, that estimate the transcript-level quantification uncertainty, here termed mapping ambiguity overdispersion, using bootstrap counts. We discuss how the mapping ambiguity overdispersion can be effectively removed from the data in transcript-level analyses via count scaling, an approach that reduces the size of the estimated counts obtained from quantification tools to effective count sizes that reflect their true precision. The presented count scaling approach allows users to perform efficient DTE analyses within the efficientedgeRframework. A comprehensive simulation study and a DTE analysis of human lung adenocarcinoma cell lines are presented to illustrate the benefits of accounting for the mapping ambiguity overdispersion in transcript-level RNA-seq data analyses.

Supplementary Table 1. Multivariate analysis of independent prognostic factors in breast cancer patients.

Supplementary Table 5 and 6: TRA2A expression was significantly associated with poorer survival

The gene structure view of CALU (exon2 inclusion), RSRC2 (exon4 inclusion), CEACAM1 (exon7 inclusion), LMCD1 (exon3 exclusion), PALM (exon8 exclusion) and RFWD2 (exon7 exclusion).

Table S3: Primers for qRT-PCR

Supplementary Figure 2. AURKA overexpression induced extra centrosomes, ALDH1 positivity and USP44 upregulation.

Supplementary Data. Table S1 shows the information of antibodies used in this study. Table S2 shows the results of immunohistochemistry.

Supplementary Figure 3. USP44 KD inhibited IL-6 and IL-8 expression in breast CSCs.

Table S2: TRA2A-regulated AS events

Supplementary Figure 1. AURKA overexpression induced centrosome amplification and stemness development in MCF-7 cells.

This study was conducted to examine the effects of doxycycline on the survival time and proliferation of hepatocellular carcinoma (HCC) in vivo and on the biologic functions of HCC in vitro. This study was also designed to evaluate the effects of doxycycline on epithelial-to-mesenchymal transition (EMT)– and vasculogenic mimicry (VM)–related protein expression and on matrix metalloproteinase (MMP) and DNA methyltransferase (DNMT) activity in vitro. Human MHCC97H cells were injected into BALB/c mice, which were divided into treatment and control groups. Doxycycline treatment prolonged the mouse survival time and partly suppressed the growth of engrafted HCC tumor cells, with an inhibition rate of 43.39%. Higher amounts of VM and endothelium-dependent vessels were found in the control group than the treatment group. IHC indicated that epithelial (E)-cadherin expression was increased in the doxycycline-treated mice compared with the control group. In in vitro experiments, doxycycline promoted HCC cell adhesion but inhibited HCC cell viability, proliferation, migration, and invasion. Western blot analysis, semiquantitative RT-PCR, qRT-PCR, and immunofluorescence demonstrated that doxycycline inhibited the degradation of the epithelial marker E-cadherin and downregulated the expression levels of EMT promoters, the mesenchymal marker vimentin, and the VM-associated marker vascular endothelial (VE)-cadherin. Furthermore, the activities of MMPs and DNMTs were examined in different groups via gelatin zymography and a DNMT activity assay kit. A methylation-specific PCR was performed to assess the promoter methylation of CDH1 (the gene encoding E-cadherin). Doxycycline prolonged the mouse survival time by inhibiting EMT progression and VM formation. Mol Cancer Ther; 13(12); 3107–22. ©2014 AACR.

Supplementary Figure legends. Figure legends for Supplementary Figure 1-3.

Supplementary Figure legends; Table S1, S4, S7, S8, and RSRC2 minigene sequence. Table S1: The increased splicing factors expression induced by PTX treatment in MDA-MB-231 cells; Table S4. All AS events selected for validation were confirmed by VeriQuest SYBR Green qPCR; Table S7: The relationship of TRA2A, CALUl, CALUs, RSRC2l, RSRC2s, PALMl, PALMs mRNA and TRA2A protein expression with PTXR of TNBC; Table S8: The relationship of TRA2A, CALUl, CALUs, RSRC2l, RSRC2s, PALMl and PALMs mRNA with TRA2A protein expression.

Treatment of triple-negative breast cancer (TNBC) has been challenging, and paclitaxel resistance is one of the major obstacles to the better prognosis. Deregulation of alternative splicing (AS) may contribute to tumor progression and chemotherapy resistance. Human AS factor TRA2 has two separate gene paralogs encoding TRA2A and TRA2B proteins. TRA2B is associated with cancer cell survival and therapeutic sensitivity. However, the individual role of TRA2A in cancer progression has not been reported. Here we report that TRA2A facilitates proliferation and survival and migration and invasion of TNBC cells. In addition, TRA2A promotes paclitaxel resistance of TNBC by specifically controlling cancer-related splicing, which is independent of other splicing factors. TRA2A overexpression could promote AS of CALU, RSRC2, and PALM during paclitaxel treatment of TNBC cells. The isoform shift of RSRC2 from RSRC2s to RSRC2l leads to a decreased RSRC2 protein expression, which could contribute to TNBC paclitaxel resistance. TRA2A can regulate RSRC2 AS by specifically binding upstream intronic sequence of exon4. Strikingly, TRA2A expression is increased dramatically in patients with TNBC, and has a close relationship with decreased RSRC2 expression; both are associated with poor survival of TNBC. Collectively, our findings suggest that paclitaxel targets the TRA2A–RSRC2 splicing pathway, and deregulated TRA2A and RSRC2 expression may confer paclitaxel resistance. In addition to providing a novel molecular mechanism of cancer-related splicing dysregulation, our study demonstrates that expression of TRA2A in conjunction with RSRC2 may provide valuable molecular biomarker evidence for TNBC clinical treatment decisions and patient outcome. Mol Cancer Ther; 16(7); 1377–88. ©2017 AACR.

Vasculogenic mimicry (VM) is a unique blood supply pattern in malignant tumors that is closely associated with metastasis and poor prognosis. The Hippo signaling effector TAZ is upregulated in several cancers, promoting cancer proliferation and metastasis. This study aimed to identify the function of TAZ and its regulatory mechanism in promoting VM in gastric cancer (GC).The expression of TAZ and TEAD4 and their correlations with overall survival and VM-related markers were analyzed in 228 cases of GC. The regulatory mechanism of TAZ and its interaction with TEAD4 in epithelial-mesenchymal transition (EMT) and VM were investigated in vitro and in vivo.TAZ was highly expressed in GC samples and was associated with shorter patient survival time. TAZ expression was positively correlated with TEAD4 and VM in patients with GC. TAZ enhanced the migration and invasion capacity of GC cells through EMT in vitro and upregulated the expression of VM-associated proteins, including VE-cadherin, MMP2, and MMP9, thus promoting VM formation. Overexpression of TAZ accelerated the growth of subcutaneous xenograft and promoted VM formation in vivo. Co-immunoprecipitation assays showed that TAZ can directly bind to TEAD4, and in vitro experiments showed that this binding mediates the function of TAZ in regulating EMT and VM formation in GC.TAZ promotes GC metastasis and VM by upregulating TEAD4 expression. Our findings expand the role of TAZ in VM and provide new theoretical support for the use of antiangiogenic therapy in the treatment of advanced GC.

Background Hepatocellular carcinoma (HCC) often presents with satellite nodules, rendering current curative treatments ineffective in many patients. The heterogeneity of HCC is a major challenge in personalized medicine. The emergence of spatial transcriptomics (ST) provides a powerful strategy for delineating the complex molecular landscapes of tumours. Methods In this study, the heterogeneity of tissue-wide gene expression in tumour and adjacent nonneoplastic tissues using ST technology were investigated. The transcriptomes of nearly 10,820 tissue regions and identified the main gene expression clusters and their specific marker genes (differentially expressed genes, DEGs) in patients were analysed. The DEGs were analysed from two perspectives. First, two distinct gene profiles were identified to be associated with satellite nodules and conducted a more comprehensive analysis of both gene profiles. Their clinical relevance in human HCC was validated with Kaplan–Meier (KM) Plotter. Second, DEGs were screened with The Cancer Genome Atlas (TCGA) database to divide the HCC cohort into high- and low-risk groups according to Cox analysis. HCC patients from the International Cancer Genome Consortium (ICGC) cohort were used for validation. KM analysis was used to compare the overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Results Novel markers for the prediction of satellite nodules were identified and a tumour clusters-specific marker gene signature model (6 genes) for HCC prognosis was constructed. Conclusion The establishment of marker gene profiles may be an important step towards an unbiased view of HCC, and the 6-gene signature can be used for prognostic prediction in HCC. This analysis will help us to clarify one of the possible sources of HCC heterogeneity and uncover pathogenic mechanisms and novel antitumour drug targets.

Polyaluminum chloride modified clay (PAC-MC) is a safe and efficient red tide control agent that has been studied and applied worldwide. Although it is well known that the distribution of hydrolytic aluminum species in PAC affects its flocculation, little is known about the influence of particulars aluminum species on the microalgae removal efficiency of PAC-MC; this lack of knowledge creates a bottleneck in the development of more efficient MCs based on aluminum salts. The ferron method was used in this study to quantitatively analyze the distributions of and variations in different hydrolytic aluminum species during the process of microalgae removal by PAC-MC. The results showed that Ala, which made up 5%–20% of the total aluminum, and Alp, which made up 15%–55% of the total aluminum, significantly affected microalgae removal, with Pearson's correlation coefficients of 0.83 and 0.89, respectively. Most of the aluminum in the PAC-MC sank rapidly into the sediments, but the rate and velocity of settlement were affected by the dose of modified clay. The optimal dose of PAC-MC for precipitating microalgae was determined based on its aluminum profile. These results provide guidance for the precise application of PAC-MC in the control of harmful algal blooms.

Objective: Hypoxia is a significant feature of solid tumors, including pancreatic ductal adenocarcinoma (PDAC). It is associated with tumor invasion, metastasis, and drug resistance. However, the spatial distribution of hypoxia-related heterogeneity in PDAC remains unclear. Methods: Spatial transcriptomics (STs), a new technique, was used to investigate the ST features of engrafted human PDAC in the ischemic hind limbs of nude mice. Transcriptomes from ST spots in the hypoxic tumor and the control were clustered using differentially-expressed genes. These data were compared to determine the spatial organization of hypoxia-induced heterogeneity in PDAC. Clinical relevance was validated using the Tumor Cancer Genome Atlas and KM-plotter databases. The CMAP website was used to identify molecules that may serve as therapeutic targets for PDAC. Results: ST showed that the tumor cell subgroups decreased to 7 subgroups in the hypoxia group, compared to 9 subgroups in the control group. Different subgroups showed positional characteristics and different gene signatures. Subgroup 6 located at the invasive front showed a higher proliferative ability under hypoxia. Subgroup 6 had active functions including cell proliferation, invasion, and response to stress. Expressions of hypoxia-related genes, LDHA, TPI1, and ENO1, induced changes. CMAP analysis indicated that ADZ-6482, a PI3K inhibitor, was targeted by the invasive subgroup in hypoxic tumors. Conclusions: This study is the first to describe hypoxic microenvironment-induced spatial transcriptome changes in PDAC, and to identify potential treatment targets for PDAC. These data will provide the basis for further investigations of the prognoses and treatments of hypoxic tumors.

Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and when not modelled appro-priately, its presence can hamper the detection of differentially expressed genes. Most bulk RNA-seq methods assume equal group variances which will under- and/or over-estimate the true variability in such datasets. We present two methods that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold standard methods that do not account for heteroscedasticity, we show results from simulation studies and various experiments that demonstrate the superior performance of both voomByGroup and voomQWB in error control and power when group variances in pseudo-bulk scRNA-seq data are unequal. We recommend the use of either of these methods over established approaches, with voomByGroup having the advantage of accurate variance estimation since group variance trends can take on different “shapes”, whilst voomQWB has the advantage of catering to complex study designs.

The current lack of benchmark datasets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (“sequins”). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we createdin silicomixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that,StringTie2andbambuoutperformed other tools from the 6 isoform detection tools tested,DESeq2, edgeRandlimma-voomwere best amongst the 5 differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the 5 tools compared, which suggests further methods development is needed for this application.

The up‐regulation of EMT regulator Twist1 has been implicated in vasculogenic mimicry (VM) formation in human triple‐negative breast cancer (TNBC). Twist1 targets the Claudin15 promoter in hepatocellular carcinoma cells. Claudin family members are related with TNBC. However, the relationship between Claudin15 and VM formation is not clear. In this study, we first found that Claudin15 expression was frequently down‐regulated in human TNBC, and Claudin15 down‐regulation was significantly associated with VM and Twist1 nuclear expression. Claudin15 down‐regulation correlated with shorter survival compared with high levels. Claudin15 silence significantly enhanced cell motility, invasiveness and VM formation in the non‐TNBC MCF‐7 cells. Conversely, an up‐regulation of Claudin15 remarkably reduced TNBC MDA‐MB‐231 cell migration, invasion and VM formation. We also showed that down‐regulation of Claudin15 was Twist1‐dependent, and Twist1 repressed Claudin15 promoter activity. Furthermore, GeneChip analyses of mammary glands of Claudin15‐deficient mice indicated that Claudin18 and Jun might be downstream factors of Twist1‐Claudin15. Our results suggest that Twist1 induced VM through Claudin15 suppression in TNBC, and Twist1 inhibition of Claudin15 might involve Claudin18 and Jun expression.

Currently, the molecular mechanism of the interaction between lncRNAs and microRNAs (miRNAs) and the target of miRNAs in tumor vasculogenic mimicry (VM) formation have not been clarified. Our aim is to study the interaction between lncRNA n339260 and miRNA30e-5p in the formation of VM.Animal xenografts were established, 104 hepatocellular carcinoma (HCC) patients' frozen tissues were obtained and HCC cells in vitro were used to observe the role of n339260 in HCC progression.In vivo experiment showed lncRNA n339260 promoted tumor growth and VM formation. LncRNA n339260 and miRNA30e-5p were found to be associated with VM formation, metastasis and survival time in HCC patients. In vitro experiment showed that LncRNA n339260 could inhibit miRNA30e-5p expression and TP53INP1 was found to be the downstream targets of miRNA30e-5p. Snail, MMP2, MMP9, VE-cadherin, vimentin and N-cadherin overexpression and the downregulation of TP53INP1 and E-cadherin were observed in HCCLM3 and HepG2 cells overexpressing lncRNA n339260 or in cells with decreased expression of miRNA30e-5p.LncRNA n339260 promotes the development of VM, and lncRNA n339260 may enhance Snail expression by decreasing the expression of miRNA30e-5p, thereby reducing TP53INP1 expression. Therefore, a potential lncRNA n339260- miRNA30e-5p- TP53INP1 regulatory axis was associated with HCC progression.

High-throughput methodologies are the cornerstone of screening approaches to identify novel compounds that regulate immune cell function. To identify novel targeted therapeutics to treat immune disorders and haematological malignancies, there is a need to integrate functional cellular information with the molecular mechanisms that regulate changes in immune cell phenotype. We facilitate this goal by combining quantitative methods for dissecting complex simultaneous cell phenotypic effects with genomic analysis. This combination strategy we term Multiplexed Analysis of Cells sequencing (MAC-seq), a modified version of Digital RNA with perturbation of Genes (DRUGseq). We applied MAC-seq to screen compounds that target the epigenetic machinery of B cells and assess altered humoral immunity by measuring changes in proliferation, survival, differentiation and transcription. This approach revealed that polycomb repressive complex 2 (PRC2) inhibitors promote antibody secreting cell (ASC) differentiation in both murine and human B cells in vitro. This is further validated using T cell-dependent immunization in mice. Functional dissection of downstream effectors of PRC2 using arrayed CRISPR screening uncovered novel regulators of B cell differentiation, includingMybl1,Myof,Gas7andAtoh8. Together, our findings demonstrate that integrated phenotype-transcriptome analyses can be effectively combined with drug screening approaches to uncover the molecular circuitry that drives lymphocyte fate decisions.

The author wishes to make the following corrections to this paper [...]

A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02525-6.

Pulmonary sarcomatoid carcinomas (PSCs) are a rare subtype of non‑small‑cell lung cancer and are typically biphasic neoplasms. No effective treatment for PSCs is currently available in clinical practice. The expression of the epithelial‑mesenchymal transition (EMT) transcription factors, Twist1, Slug and Snail, as well as the EMT phenotype and vasculogenic mimicry (VM) were analysed in 41 PSC and 79 pulmonary squamous carcinoma (PSCC) samples. Compared with the PSCCs, the PSCs exhibited an EMT phenotype and VM, and they also exhibited an increased expression of the Twist1, Slug, Snail and VM markers. Twist1 expression was associated with metastasis and TNM stage. Twist1‑positive patients exhibited a poorer prognosis for overall survival (OS) than those with Twist1‑negative PSCs. Transforming growth factor β1 (TGFβ1) was used to induce an EMT transition in a PSCC cell line. SK‑MES‑1 cells treated with TGFβ1 exhibited an increased expression of Twist1. The EMT phenotype, VM and increased migratory and invasive abilities were induced following TGFβ1 treatment. Importantly, in cells treated with TGFβ1, the EMT phenotype was reversed, VM marker expression was decreased, and the migratory and invasive ability of the PSCC cell line was decreased following Twist1 knockdown. Collectively, this study provides a new perspective of Twist1 in the aggressiveness of PSCs. The identification of Twist1 as an independent marker of poor prognoses may lead to the development of novel strategies for improving the treatment of patients with PSC.

Gut microbiota-derived metabolite trimethylamine N-oxide (TMAO) has recently been shown to promote oxidative stress and inflammation in the peripheral tissues, contributing to the pathogenesis of many diseases. Here we examined whether pre-existing higher circulating TMAO would influence cognitive function in aged rats after anesthetic sevoflurane exposure. Aged rats received vehicle or TMAO treatment for 3 weeks. After 2 weeks of treatment, these animals were exposed to either control or 2.6% sevoflurane for 4 h. One week after exposure, freezing as measured by fear conditioning test, microglia activity, proinflammatory cytokine expression and NADPH oxidase-dependent reactive oxygen species (ROS) production in the hippocampus (a key brain structure involved in learning and memory) were comparable between vehicle-treated rats exposed to control and vehicle-treated rats exposed to sevoflurane. TMAO treatment, which increased plasma TMAO before and 1 week after control or sevoflurane exposure, significantly reduced freezing to contextual fear conditioning, which was associated with increases in microglia activity, proinflammatory cytokine expression and NADPH oxidase-dependent ROS production in the hippocampus in rats exposed to sevoflurane but not in rats exposed to control. Moreover, hippocampal expression of antioxidant enzyme methionine sulfoxide reductase A (MsrA) was reduced by TMAO treatment in both groups, and TMAO-induced reduction in MsrA expression was negatively correlated with increased proinflammatory cytokine expression in rats exposed to SEV. These findings suggest that pre-existing higher circulating TMAO downregulates antioxidant enzyme MsrA in the hippocampus, which may sensitize the hippocampus to oxidative stress, resulting in microglia-mediated neuroinflammation and cognitive impairment in aged rats after sevoflurane exposure.

N-myc downstream regulated gene 1 (NDRG1) has been well characterized as a suppressor of metastasis in numerous types of carcinoma. NDRG1 inhibits the metastatic progression of cancer cells via its inhibitory effects on a wide variety of cellular signaling pathways. Vasculogenic mimicry (VM) refers to the unique ability of aggressive tumor cells to mimic the pattern of embryonic vasculogenic networks, and is the main reason for the poor prognosis and failure of antivascular therapy in gastric carcinoma (GC). Tumor cells can mimic the function of endothelial cells to exhibit VM through epithelial-mesenchymal transition (EMT). However, the potential function of NDRG1 in metastatic GC progression in patients has not yet been fully elucidated. To date, data regarding the function of NDRG1 in VM formation in GC have not been reported. The aim of the present study was to elucidate these unknown areas. To this end, 228 samples of human GC were used to identify the protein expression levels of NDRG1, VM-associated proteins and EMT-associated proteins via immunohistochemistry, and their clinical significance was assessed. In addition, the data of 415 patients with GC were collected from The Cancer Genome Atlas database. A functional enrichment analysis concerning NDRG1 was performed using Metascape and the Gene Set Enrichment Analysis (GSEA). In conclusion, the results of the present study indicate that NDRG1 is negatively correlated with poor prognosis through suppression of VM formation in GC. The results of the present study demonstrated that NDRG1 decreases EMT-associated protein expression and that HER2 expression may serve a significant role in this process. The Metascape and GSEA results also indirectly support this conclusion. The present study discusses the status NDRG1 as a prognostic and selective biomarker in GC, as well as current and future NDRG1-targeted therapies.

Single-cell RNA sequencing (scRNA-seq) technologies and associated analysis methods have undergone rapid development in recent years. This includes methods for data preprocessing, which assign sequencing reads to genes to create count matrices for downstream analysis. Several packaged preprocessing workflows have been developed that aim to provide users with convenient tools for handling this process. How different preprocessing workflows compare to one another and influence downstream analysis has been less well studied.Here, we systematically benchmark the performance of 9 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2 and scruff) using datasets with varying levels of biological complexity generated on the CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. We find that lowly expressed genes are discordant between workflows and observe that some workflows have systematic biases towards particular classes of genomics features. While the scRNA-seq preprocessing workflows compared varied in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produced clustering results that agreed well with the known cell type labels that provided the ground truth in our analysis.In summary, the choice of preprocessing method was found to be less influential than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.