Transcriptome Reconstruction Research Articles

LineageVAE: reconstructing historical cell states and transcriptomes toward unobserved progenitors.

Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis or cell state divergence due to injury or disease. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors. LineageVAE is a deep generative model that transforms scRNA-seq observations with identical lineage barcodes into sequential trajectories toward a common progenitor in a latent cell state space. This method enables the reconstruction of unobservable cell state transitions, historical transcriptomes, and regulatory dynamics at a single-cell resolution. Applied to hematopoiesis and reprogrammed fibroblast datasets, LineageVAE demonstrated its ability to restore backward cell state transitions and infer progenitor heterogeneity and transcription factor activity along differentiation trajectories. The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https://github.com/LzrRacer/LineageVAE/.

Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches.

Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.

Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

MarkerMap: nonlinear marker selection for single-cell studies

Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intractable. Here we introduce MarkerMap, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction. MarkerMap provides a scalable framework for both supervised marker selection, aimed at identifying specific cell type populations, and unsupervised marker selection, aimed at gene expression imputation and reconstruction. We benchmark MarkerMap’s competitive performance against previously published approaches on real single cell gene expression data sets. MarkerMap is available as a pip installable package, as a community resource aimed at developing explainable machine learning techniques for enhancing interpretability in single-cell studies.

IsoTools: a flexible workflow for long-read transcriptome sequencing analysis.

Long-read transcriptome sequencing (LRTS) has the potential to enhance our understanding of alternative splicing and the complexity of this process requires the use of versatile computational tools, with the ability to accommodate various stages of the workflow with maximum flexibility. We introduce IsoTools, a Python-based LRTS analysis framework that offers a wide range of functionality for transcriptome reconstruction and quantification of transcripts. Furthermore, we integrate a graph-based method for identifying alternative splicing events and a statistical approach based on the beta-binomial distribution for detecting differential events. To demonstrate the effectiveness of our methods, we applied IsoTools to PacBio LRTS data of human hepatocytes treated with the histone deacetylase inhibitor valproic acid. Our results indicate that LRTS can provide valuable insights into alternative splicing, particularly in terms of complex and differential splicing patterns, in comparison to short-read RNA-seq. IsoTools is available on GitHub and PyPI, and its documentation, including tutorials, CLI, and API references, can be found at https://isotools.readthedocs.io/.

De novo transcriptome reconstruction in aquacultured early life stages of the cephalopod Octopus vulgaris

Cephalopods have been considered enigmatic animals that have attracted the attention of scientists from different areas of expertise. However, there are still many questions to elucidate the way of life of these invertebrates. The aim of this study is to construct a reference transcriptome in Octopus vulgaris early life stages to enrich existing databases and provide a new dataset that can be reused by other researchers in the field. For that, samples from different developmental stages were combined including embryos, newly-hatched paralarvae, and paralarvae of 10, 20 and 40 days post-hatching. Additionally, different dietary and rearing conditions and pathogenic infections were tested. At least three biological replicates were analysed per condition and submitted to RNA-seq analysis. All sequencing reads from experimental conditions were combined in a single dataset to generate a reference transcriptome assembly that was functionally annotated. The number of reads aligned to this reference was counted to estimate the transcript abundance in each sample. This dataset compiled a complete reference for future transcriptomic studies in O. vulgaris.

RATTLE: reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing

Nanopore sequencing enables the efficient and unbiased measurement of transcriptomes. Current methods for transcript identification and quantification rely on mapping reads to a reference genome, which precludes the study of species with a partial or missing reference or the identification of disease-specific transcripts not readily identifiable from a reference. We present RATTLE, a tool to perform reference-free reconstruction and quantification of transcripts using only Nanopore reads. Using simulated data and experimental data from isoform spike-ins, human tissues, and cell lines, we show that RATTLE accurately determines transcript sequences and their abundances, and shows good scalability with the number of transcripts.

Full-Length Transcriptome Reconstruction Reveals the Genetic Mechanisms of Eyestalk Displacement and Its Potential Implications on the Interspecific Hybrid Crab (Scylla serrata ♀ × S. paramamosain ♂).

Simple SummaryThe eyestalk is a key organ in crustaceans that produces neurohormones and regulates a range of physiological functions. Eyestalk displacement was discovered in some first-generation (F1) offspring of the novel interspecific hybrid crab (Scylla serrata ♀ × S. paramamosain ♂). To uncover the genetic mechanism underlying eyestalk displacement and its potential implications, high-quality transcriptome was reconstructed using single-molecule real-time (SMRT) sequencing. A total of 37 significantly differential alternative splicing (DAS) events (17 up-regulated and 20 down-regulated) and 1475 significantly differential expressed transcripts (DETs) (492 up-regulated and 983 down-regulated) were detected in hybrid crabs with displaced eyestalks (DH). The most significant DAS events and DETs were annotated as being endoplasmic reticulum chaperone BiP and leucine-rich repeat protein lrrA-like isoform X2. In addition, the top ten significant gene ontology (GO) terms were related to the cuticle or chitin. Overall, this study highlights the underlying genetic mechanisms of eyestalk displacement and provide useful knowledge for mud crab (Scylla spp.) crossbreeding.The lack of high-quality juvenile crabs is the greatest impediment to the growth of the mud crab (Scylla paramamosain) industry. To obtain high-quality hybrid offspring, a novel hybrid mud crab (S. serrata ♀ × S. paramamosain ♂) was successfully produced in our previous study. Meanwhile, an interesting phenomenon was discovered, that some first-generation (F1) hybrid offspring’s eyestalks were displaced during the crablet stage I. To uncover the genetic mechanism underlying eyestalk displacement and its potential implications, both single-molecule real-time (SMRT) and Illumina RNA sequencing were implemented. Using a two-step collapsing strategy, three high-quality reconstructed transcriptomes were obtained from purebred mud crabs (S. paramamosain) with normal eyestalks (SPA), hybrid crabs with normal eyestalks (NH), and hybrid crabs with displaced eyestalks (DH). In total, 37 significantly differential alternative splicing (DAS) events (17 up-regulated and 20 down-regulated) and 1475 significantly differential expressed transcripts (DETs) (492 up-regulated and 983 down-regulated) were detected in DH. The most significant DAS events and DETs were annotated as being endoplasmic reticulum chaperone BiP and leucine-rich repeat protein lrrA-like isoform X2. In addition, the top ten significant GO terms were related to the cuticle or chitin. Overall, high-quality reconstructed transcriptomes were obtained for the novel interspecific hybrid crab and provided valuable insights into the genetic mechanisms of eyestalk displacement in mud crab (Scylla spp.) crossbreeding.

TransMeta simultaneously assembles multisample RNA-seq reads.

Assembling RNA-seq reads into full-length transcripts is crucial in transcriptomic studies and poses computational challenges. Here we present TransMeta, a simple and robust algorithm that simultaneously assembles RNA-seq reads from multiple samples. TransMeta is designed based on the newly introduced vector-weighted splicing graph model, which enables accurate reconstruction of the consensus transcriptome via incorporating a cosine similarity–based combing strategy and a newly designed label-setting path-searching strategy. Tests on both simulated and real data sets show that TransMeta consistently outperforms PsiCLASS, StringTie2 plus its merge mode, and Scallop plus TACO, the most popular tools, in terms of precision and recall under a wide range of coverage thresholds at the meta-assembly level. Additionally, TransMeta consistently shows superior performance at the individual sample level.

Gene expression phylogenies and ancestral transcriptome reconstruction resolves major transitions in the origins of pregnancy.

Structural and physiological changes in the female reproductive system underlie the origins of pregnancy in multiple vertebrate lineages. In mammals, the glandular portion of the lower reproductive tract has transformed into a structure specialized for supporting fetal development. These specializations range from relatively simple maternal nutrient provisioning in egg-laying monotremes to an elaborate suite of traits that support intimate maternal-fetal interactions in Eutherians. Among these traits are the maternal decidua and fetal component of the placenta, but there is considerable uncertainty about how these structures evolved. Previously, we showed that changes in uterine gene expression contributes to several evolutionary innovations during the origins of pregnancy (Mika et al., 2021b). Here, we reconstruct the evolution of entire transcriptomes ('ancestral transcriptome reconstruction') and show that maternal gene expression profiles are correlated with degree of placental invasion. These results indicate that an epitheliochorial-like placenta evolved early in the mammalian stem-lineage and that the ancestor of Eutherians had a hemochorial placenta, and suggest maternal control of placental invasiveness. These data resolve major transitions in the evolution of pregnancy and indicate that ancestral transcriptome reconstruction can be used to study the function of ancestral cell, tissue, and organ systems.

High-resolution 3D spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae.

Drosophila has long been a successful model organism in multiple biomedical fields. Spatial gene expression patterns are critical for the understanding of complex pathways and interactions, whereas temporal gene expression changes are vital for studying highly dynamic physiological activities. Systematic studies in Drosophila are still impeded by the lack of spatiotemporal transcriptomic information. Here, utilizing spatialenhancedresolutionomics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. We demonstrated that Stereo-seq data can be used for the 3D reconstruction of the spatial transcriptomes of Drosophila embryos and larvae. With these 3D models, we identified functional subregions in embryonic and larval midguts, uncovered spatial cell state dynamics of larval testis, and revealed known and potential regulons of transcription factors within their topographic background. Our data provide the Drosophila research community with useful resources of organism-wide spatiotemporally resolved transcriptomic information across developmental stages.

Epimutations in both the TESK2 and MMACHC promoters in the Epi-cblC inherited disorder of intracellular metabolism of vitamin B12

Backgroundepi-cblC is a recently discovered inherited disorder of intracellular vitamin B12 metabolism associating hematological, neurological, and cardiometabolic outcomes. It is produced by an epimutation at the promoter common to CCDC163P and MMACHC, which results from an aberrant antisense transcription due to splicing mutations in the antisense PRDX1 gene neighboring MMACHC. We studied whether the aberrant transcription produced a second epimutation by encompassing the CpG island of the TESK2 gene neighboring CCDC163P.MethodsWe unraveled the methylome architecture of the CCDC163P–MMACHC CpG island (CpG:33) and the TESK2 CpG island (CpG:51) of 17 epi-cblC cases. We performed an integrative analysis of the DNA methylome profiling, transcriptome reconstruction of RNA-sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-Seq) of histone H3, and transcription expression of MMACHC and TESK2.ResultsThe PRDX1 splice mutations and activation of numerous cryptic splice sites produced antisense readthrough transcripts encompassing the bidirectional MMACHC/CCDC163P promoter and the TESK2 promoter, resulting in the silencing of both the MMACHC and TESK2 genes through the deposition of SETD2-dependent H3K36me3 marks and the generation of epimutations in the CpG islands of the two promoters.ConclusionsThe antisense readthrough transcription of the mutated PRDX1 produces an epigenetic silencing of MMACHC and TESK2. We propose using the term 'epi-digenism' to define this epigenetic disorder that affects two genes. Epi-cblC is an entity that differs from cblC. Indeed, the PRDX1 and TESK2 altered expressions are observed in epi-cblC but not in cblC, suggesting further evaluating the potential consequences on cancer risk and spermatogenesis.

Full-length transcriptome reconstruction reveals genetic differences in hybrids of Oryza sativa and Oryza punctata with different ploidy and genome compositions

BackgroundAllopolyploid breeding is an efficient technique for improving the low seed setting rate of autotetraploids in plant breeding and one of the most promising breeding methods. However, there have been few comprehensive studies of the posttranscriptional mechanism in allopolyploids.ResultsBy crossing cultivated rice (Oryza sativa, genome AA) with wild rice (Oryza punctata, genome BB), we created hybrid rice lines with different ploidy and genome compositions [diploid hybrid F01 (AB), allotetraploid hybrid F02 (AABB) and F03 (AAAB)]. The genetic differences of the hybrids and the mechanism of allopolyploid breeding dominance were revealed through morphological and cytological observations and single molecule real-time sequencing techniques. The tissues and organs of allotetraploid hybrid F02 exhibited "gigantism" and the highest levels of fertility. The numbers of non-redundant transcripts, gene loci and new isoforms in the polyploid rice lines were higher and the isoform lengths greater than those of the diploid line. Moreover, alternative splicing (AS) events occurred twice as often in the polyploid rice lines than the diploid line. During these events, intron retention dominated. Furthermore, a large number of new genes and isoforms specific to the lines of different ploidy were discovered.ConclusionsThe results indicated that alternative polyadenylation (APA) and AS events contributed to the complexity and superiority of polyploids in the activity of translation regulators, nucleic acid binding transcription factor activities and the regulation of molecular function. Therefore, these APA and AS events in allopolyploid rice were found to play a role in regulation. Our study provides new germplasm for polyploid rice breeding and reveals complex regulatory mechanisms that may be related to heterosis and fertility.

Deciphering Cell-Type-Specific Gene Expression Signatures of Cardiac Diseases Through Reconstruction of Bulk Transcriptomes.

Cardiac diseases compose a fatal disease category worldwide. Over the past decade, high-throughput transcriptome sequencing of bulk heart tissues has widened our understanding of the onset and progression of cardiac diseases. The recent rise of single-cell RNA sequencing (scRNA-seq) technology further enables deep explorations of their molecular mechanisms in a cell-type-specific manner. However, due to technical difficulties in performing scRNA-seq on heart tissues, there are still few scRNA-seq studies on cardiac diseases. In this study, we demonstrate that an effective alternative could be cell-type-specific computational reconstruction of bulk transcriptomes. An integrative bulk transcriptome dataset covering 110 samples from 12 studies was first constructed by re-analysis of raw sequencing data derived from the heart tissues of four common cardiac disease mouse models (myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy). Based on the single-cell reference covering four major cardiac component cell types and 22 immune cell subtypes, for each sample, the bulk transcriptome was reconstructed into cellular compositions and cell-type-specific expression profiles by CIBERSORTx. Variations in the estimated cell composition revealed elevated abundances of fibroblast and monocyte during myocardial infarction, which were further verified by our flow cytometry experiment. Moreover, through cell-type-specific differential gene expression and pathway enrichment analysis, we observed a series of signaling pathways that mapped to specific cell type in diseases, like MAPK and EGFR1 signaling pathways in fibroblasts in myocardial infarction. We also found an increased expression of several secretory proteins in monocytes which may serve as regulatory factors in cardiac fibrosis. Finally, a ligand–receptor analysis identified key cell types which may serve as hubs in cellular communication in cardiac diseases. Our results provide novel clues for the cell-type-specific signatures of cardiac diseases that would promote better understanding of their pathophysiological mechanisms.

Rewired glycosylation activity promotes scarless regeneration and functional recovery in spiny mice after complete spinal cord transection.

Regeneration of adult mammalian central nervous system (CNS) axons is abortive, resulting in inability to recover function after CNS lesion, including spinal cord injury (SCI). Here, we show that the spiny mouse (Acomys) is an exception to other mammals, being capable of spontaneous and fast restoration of function after severe SCI, re-establishing hind limb coordination. Remarkably, Acomys assembles a scarless pro-regenerative tissue at the injury site, providing a unique structural continuity of the initial spinal cord geometry. The Acomys SCI site shows robust axon regeneration of multiple tracts, synapse formation, and electrophysiological signal propagation. Transcriptomic analysis of the spinal cord following transcriptome reconstruction revealed that Acomys rewires glycosylation biosynthetic pathways, culminating in a specific pro-regenerative proteoglycan signature at SCI site. Our work uncovers that a glycosylation switch is critical for axon regeneration after SCI and identifies β3gnt7, a crucial enzyme of keratan sulfate biosynthesis, as an enhancer of axon growth.

Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data.

The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m6A depletion by Mettl14 knockout.

UNAGI: Yeast Transcriptome Reconstruction and Gene Discovery Using Nanopore Sequencing.

Computational approaches are the main approaches used in genome annotation. However, accuracy is low. Untranslated regions are not identified, complex isoforms are not predicted correctly and discovery rate of noncoding RNA is low. RNA-seq has revolutionized transcriptome reconstruction over the last decade. However, fragmentation included in cDNA sequencing leads to information loss, requiring transcripts to be assembled and reconstructed, thus affecting the accuracy of reconstructed transcriptome. Recently, long-read sequencing has been introduced with technologies such as Oxford Nanopore sequencing. cDNA is sequenced directly without fragmentation producing long reads that don't need to be assembled keeping the transcript structure intact and increasing the accuracy of transcriptome reconstruction.Here we present a protocol and a pipeline to reconstruct the transcriptome of compact genomes including yeasts. It involves generating full-length cDNA and using Oxford Nanopore ligation-based sequencing kit to sequence multiple samples in the same run. The pipeline (1) strands the generated long reads, (2) corrects the reads by mapping them to the reference genome, (3) identifies transcripts including 5'UTR and 3'UTR, (4) profiles the isoforms, filtering out artifacts resulting from low accuracy in sequencing, and (5) improves accuracy of provided annotations. Using long reads improves the accuracy of transcriptome reconstruction and helps in discovering a significant number of novel RNAs.

De Novo Reconstruction of Transcriptome Identified Long Non-Coding RNA Regulator of Aging-Related Brown Adipose Tissue Whitening in Rabbits

Simple SummaryBrown adipose tissues (BATs) undergo the conversion to white adipose tissues (WATs) with age. Long non-coding RNAs (lncRNAs) were widely involved in adipose biology. Rabbit is an ideal model for studying the dynamics of the transformation from BATs to WATs. However, our knowledge of lncRNAs that mediate the transformation remains unknown in rabbits. By histological analysis and sequencing, we found rabbit interscapular adipose tissues (iATs) from BATs to WATs within two years and identified a total of 631 differentially expressed lncRNAs (DELs) during the transformation process. Several signal pathways were involved in the transformation from BAT to WAT. A novel lncRNA that was highly expressed in iATs of aged rabbits was validated to impair brown adipocyte differentiation in vitro. Our study provided a comprehensive catalog of lncRNAs involved in the transformation from BATs to WATs in rabbits, facilitating a better understanding of adipose biology.Brown adipose tissues (BATs) convert to a “white-like” phenotype with age, which is also known as “aging-related BAT whitening (ARBW)”. Emerging evidence suggested that long non-coding RNAs (lncRNAs) were widely involved in adipose biology. Rabbit is an ideal model for studying the dynamics of ARBW. In this study, we performed histological analysis and strand-specific RNA-sequencing (ssRNA-seq) of rabbit interscapular adipose tissues (iATs). Our data indicated that the rabbit iATs underwent the ARBW from 0 days to 2 years and a total of 2281 novel lncRNAs were identified in the iATs. The classical rabbit BATs showed low lncRNA transcriptional complexity compared to white adipose tissues (WATs). A total of 631 differentially expressed lncRNAs (DELs) were identified in four stages. The signal pathways of purine metabolism, Wnt signaling pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway, cyclic guanosine monophosphate (cGMP)/cGMP-dependent protein kinase (cGMP-PKG) signaling pathway and lipid and atherosclerosis were significantly enriched by the DELs with unique expression patterns. A novel lncRNA that was highly expressed in the iATs of aged rabbits was validated to impair brown adipocyte differentiation in vitro. Our study provided a comprehensive catalog of lncRNAs involved in ARBW in rabbits, which facilitates a better understanding of adipose biology.

Ryūtō: improved multi-sample transcript assembly for differential transcript expression analysis and more.

Accurate assembly of RNA-seq is a crucial step in many analytic tasks such as gene annotation or expression studies. Despite ongoing research, progress on traditional single sample assembly has brought no major breakthrough. Multi-sample RNA-Seq experiments provide more information than single sample datasets and thus constitute a promising area of research. Yet, this advantage is challenging to utilize due to the large amount of accumulating errors. We present an extension to Ryūtō enabling the reconstruction of consensus transcriptomes from multiple RNA-seq datasets, incorporating consensus calling at low level features. We report stable improvements already at three replicates. Ryūtō outperforms competing approaches, providing a better and user-adjustable sensitivity-precision trade-off. Ryūtō's unique ability to utilize a (incomplete) reference for multi sample assemblies greatly increases precision. We demonstrate benefits for differential expression analysis.Ryūtō consistently improves assembly on replicates of the same tissue independent of filter settings, even when mixing conditions or time series. Consensus voting in Ryūtō is especially effective at high precision assembly, while Ryūtō's conventional mode can reach higher recall. Ryūtō is available at https://github.com/studla/RYUTO. Supplementary data are available at Bioinformatics online.

Reconstruction of the full-length transcriptome of cigar tobacco without a reference genome and characterization of anion channel/transporter transcripts

BackgroundCigar wrapper leaves are the most important raw material of cigars. Studying the genomic information of cigar tobacco is conducive to improving cigar quality from the perspective of genetic breeding. However, no reference genome or full-length transcripts at the genome-wide scale have been reported for cigar tobacco. In particular, anion channels/transporters are of high interest for their potential application in regulating the chloride content of cigar tobacco growing on coastal lands, which usually results in relatively high Cl− accumulation, which is unfavorable. Here, the PacBio platform and NGS technology were combined to generate a full-length transcriptome of cigar tobacco used for cigar wrappers.ResultsHigh-quality RNA isolated from the roots, leaves and stems of cigar tobacco were subjected to both the PacBio platform and NGS. From PacBio, a total of 11,652,432 subreads (19-Gb) were generated, with an average read length of 1,608 bp. After corrections were performed in conjunction with the NGS reads, we ultimately identified 1,695,064 open reading frames including 21,486 full-length ORFs and 7,342 genes encoding transcription factors from 55 TF families, together with 2,230 genes encoding long non-coding RNAs. Members of gene families related to anion channels/transporters, including members of the SLAC and CLC families, were identified and characterized.ConclusionsThe full-length transcriptome of cigar tobacco was obtained, annotated, and analyzed, providing a valuable genetic resource for future studies in cigar tobacco.