A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs

Uncovering the isoform-resolution kinetic landscape of nonsense-mediated mRNA decay with EZbakR

Cellular RNA levels are a product of synthesis and degradation kinetics, which can differ among transcripts of the same gene. An important cause of isoform-specific decay is the nonsense-mediated mRNA decay (NMD) pathway, which degrades transcripts with premature termination codons (PTCs) and other features. Understanding NMD functions requires strategies to quantify isoform kinetics; however, current approaches remain limited. Methods like nucleotide-recoding RNA-seq (NR-seq) enable insights into RNA kinetics, but existing bioinformatic tools do not provide robust, isoform-specific degradation rate constant estimates. We extend the EZbakR-suite by implementing a strategy to infer isoform-level kinetics from short-read NR-seq data. This approach uncovers unexpected variability in NMD efficiency among transcripts with conserved PTC-containing exons and rapid decay of a subset of mRNAs lacking PTCs. Our findings highlight the effects of competition between NMD and other decay pathways, provide mechanistic insights into established NMD efficiency correlates, and identify transcript features promoting efficient decay.

Improved characterization of 3' single-cell RNA-seq libraries with paired-end avidity sequencing

Prevailing poly(dT)-primed 3' single-cell RNA-seq protocols generate barcoded cDNA fragments containing the reverse transcriptase priming site or in principle the polyadenylation site. Direct sequencing across this site was historically difficult because of DNA sequencing errors induced by the homopolymeric primer at the 'barcode' end. Here, we evaluate the capability of 'avidity base chemistry' DNA sequencing from Element Biosciences to sequence through the primer and enable accurate paired-end read alignment and precise quantification of polyadenylation sites. We find that the Element Aviti instrument sequences through the thymine homopolymer into the subsequent cDNA sequence without detectable loss of accuracy. The additional sequence enables direct and independent assignment of reads to polyadenylation sites, which bypasses the complexities and limitations of conventional approaches but does not consistently improve read mapping rates compared to single-end alignment. We also characterize low-level artifacts and demonstrate necessary adjustments to adapter trimming and sequence alignment regardless of platform, particularly in the context of extended read lengths. Our analyses confirm that Element avidity sequencing is an effective alternative to Illumina sequencing for standard single-cell RNA-seq, particularly for polyadenylation site measurement but do not rule out the potential for similar performance from other emerging platforms.

Improved characterization of single-cell RNA-seq libraries with paired-end avidity sequencing

Prevailing poly(dT)-primed 3' single-cell RNA-seq protocols generate barcoded cDNA fragments containing the reverse transcriptase priming site, which is expected to be the poly(A) tail or a genomic adenine homopolymer. Direct sequencing across this priming site was historically difficult because of DNA sequencing errors induced by the homopolymeric primer at the 'barcode' end. Here, we evaluate the capability of "avidity base chemistry" DNA sequencing from Element Biosciences to sequence through this homopolymer accurately, and the impact of the additional cDNA sequence on read alignment and precise quantification of polyadenylation site usage. We find that the Element Aviti instrument sequences through the thymine homopolymer into the subsequent cDNA sequence without detectable loss of accuracy. The resulting paired-end alignments enable direct and independent assignment of reads to polyadenylation sites, which bypasses complexities and limitations of conventional approaches but does not consistently improve read mapping rates compared to single-end alignment. We also characterize low-level artifacts and arrive at an adjusted adapter trimming and alignment workflow that significantly improves the alignment of sequence data from Element and Illumina, particularly in the context of extended read lengths. Our analyses confirm that Element avidity sequencing is an effective alternative to Illumina sequencing for standard single-cell RNA-seq, particularly for polyadenylation site analyses but do not rule out the potential for similar performance from other emerging platforms.

Targeting CCR7-PI3Kγ overcomes resistance to tyrosine kinase inhibitors in ALK-rearranged lymphoma

Anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors (TKIs) show potent efficacy in several ALK-driven tumors, but the development of resistance limits their long-term clinical impact. Although resistance mechanisms have been studied extensively in ALK-driven non-small cell lung cancer, they are poorly understood in ALK-driven anaplastic large cell lymphoma (ALCL). Here, we identify a survival pathway supported by the tumor microenvironment that activates phosphatidylinositol 3-kinase γ (PI3K-γ) signaling through the C-C motif chemokine receptor 7 (CCR7). We found increased PI3K signaling in patients and ALCL cell lines resistant to ALK TKIs. PI3Kγ expression was predictive of a lack of response to ALK TKI in patients with ALCL. Expression of CCR7, PI3Kγ, and PI3Kδ were up-regulated during ALK or STAT3 inhibition or degradation and a constitutively active PI3Kγ isoform cooperated with oncogenic ALK to accelerate lymphomagenesis in mice. In a three-dimensional microfluidic chip, endothelial cells that produce the CCR7 ligands CCL19/CCL21 protected ALCL cells from apoptosis induced by crizotinib. The PI3Kγ/δ inhibitor duvelisib potentiated crizotinib activity against ALCL lines and patient-derived xenografts. Furthermore, genetic deletion of CCR7 blocked the central nervous system dissemination and perivascular growth of ALCL in mice treated with crizotinib. Thus, blockade of PI3Kγ or CCR7 signaling together with ALK TKI treatment reduces primary resistance and the survival of persister lymphoma cells in ALCL.

Hyaluronan nanoscale clustering and Hyaluronan synthase 2 expression are linked to the invasion of child fibroblasts and infantile fibrosarcoma in vitro and in vivo

Infantile fibrosarcoma is a rare childhood tumour that originates in the fibrous connective tissue of the long bones for which there is an urgent need to identify novel therapeutic targets. This study aims to clarify the role of the extracellular matrix component hyaluronan in the invasion of child fibroblasts and Infantile fibrosarcoma into the surrounding environment. Using nanoscale super-resolution STED (Stimulated emission depletion) microscopy followed by computational image analysis, we observed, for the first time, that invasive child fibroblasts showed increased nanoscale clustering of hyaluronan at the cell periphery, as compared to control cells. Hyaluronan was not observed within focal adhesions. Bioinformatic analyses further revealed that the increased nanoscale hyaluronan clustering was accompanied by increased gene expression of Hyaluronan synthase 2, reduced expression of Hyaluronidase 2 and CD44, and no change of Hyaluronan synthase 1 and Hyaluronidases 1, 3, 4 or 5. We further observed that the expression of the Hyaluronan synthase 1, 2 and 3, and the Hyaluronidase 3 and 5 genes was linked to reduced life expectancy of fibrosarcoma patients. The invasive front of infantile fibrosarcoma tumours further showed increased levels of hyaluronan, as compared to the tumour centre. Taken together, our findings are consistent with the possibility that while Hyaluronan synthase 2 increases the levels, the Hyaluronidases 3 and 5 reduce the weight of hyaluronan, resulting in the nanoscale clustering of hyaluronan at the leading edge of cells, cell invasion and the spread of Infantile fibrosarcoma.

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for most crop plants are far from complete. For example, Oryza sativa indica (O. sativa indica) is reported to have 40,759 transcripts in the Ensembl database without alternative transcript isoforms and alternative splicing (AS) events. To generate a high-quality RTD, we conducted RNA sequencing of rice leaf samples collected at various time points during Rhizoctonia solani infection. The obtained reads were analyzed by adopting the recently developed computational analysis pipeline to assemble the RTD with increased transcript and AS diversity for O. sativa indica (IndicaRTD). After stringent quality filtering, the newly constructed transcriptome annotation was comprised of 122,968 non-redundant transcripts from 53,695 genes. This study identified many novel transcripts compared to Ensembl deposited data that are important for regulating molecular and physiological processes in the plant system. Currently, the assembled IndicaRTD must allow fast quantification of transcript and gene expression with high precision.

3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists

RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the '3D RNA-seq' App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists

RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the '3D RNA-seq' App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

Chromatin loop anchors predict transcript and exon usage

Epigenomics and transcriptomics data from high-throughput sequencing techniques such as RNA-seq and ChIP-seq have been successfully applied in predicting gene transcript expression. However, the locations of chromatin loops in the genome identified by techniques such as Chromatin Interaction Analysis with Paired End Tag sequencing (ChIA-PET) have never been used for prediction tasks. Here, we developed machine learning models to investigate if ChIA-PET could contribute to transcript and exon usage prediction. In doing so, we used a large set of transcription factors as well as ChIA-PET data. We developed different Gradient Boosting Trees models according to the different tasks with the integrated datasets from three cell lines, including GM12878, HeLaS3 and K562. We validated the models via 10-fold cross validation, chromosome-split validation and cross-cell validation. Our results show that both transcript and splicing-derived exon usage can be effectively predicted with at least 0.7512 and 0.7459 of accuracy, respectively, on all cell lines from all kinds of validations. Examining the predictive features, we found that RNA Polymerase II ChIA-PET was one of the most important features in both transcript and exon usage prediction, suggesting that chromatin loop anchors are predictive of both transcript and exon usage.

A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. However, while a global change in transcription is recognized as a defining feature of cancer, the contribution of alternative promoters still remains largely unexplored. Here, we infer active promoters using RNA-seq data from 18,468 cancer and normal samples, demonstrating that alternative promoters are a major contributor to context-specific regulation of transcription. We find that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates. For genes with independently regulated promoters, we demonstrate that promoter activity provides a more accurate predictor of patient survival than gene expression. Our study suggests that a dynamic landscape of active promoters shapes the cancer transcriptome, opening new diagnostic avenues and opportunities to further explore the interplay of regulatory mechanisms with transcriptional aberrations in cancer.

Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification

Because of incomplete reference transcriptomes, incomplete sequencing bias models, or other modeling defects, algorithms to infer isoform expression from RNA sequencing (RNA-seq) sometimes do not accurately model expression. We present a computational method to detect instances where a quantification algorithm could not completely explain the input reads. Our approach identifies regions where the read coverage significantly deviates from expectation. We call these regions "expression anomalies." We further present a method to attribute their cause to either the incompleteness of the reference transcriptome or algorithmic mistakes. We detect anomalies for 30 GEUVADIS and 16 Human Body Map samples. By correcting anomalies when possible, we reduce the number of falsely predicted instances of differential expression. Anomalies that cannot be corrected are suspected to indicate the existence of isoforms unannotated by the reference. We detected 88 common anomalies of this type and find that they tend to have a lower-than-expected coverage toward their 3' ends.

BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq

BACKGROUND

The time required to analyse RNA-seq data varies considerably, due to discrete steps for computational assembly, quantification of gene expression and splicing analysis. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants.

RESULTS

A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts - BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al. Nature; 544: 427-433, 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al. Plant Physiol; 156: 20-28, 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5' and 3' UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2791 differentially alternatively spliced genes and 2768 transcripts with differential transcript usage.

CONCLUSION

A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq

BACKGROUND

The time required to analyse RNA-seq data varies considerably, due to discrete steps for computational assembly, quantification of gene expression and splicing analysis. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants.

RESULTS

A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts - BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al. Nature; 544: 427-433, 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al. Plant Physiol; 156: 20-28, 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5' and 3' UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2791 differentially alternatively spliced genes and 2768 transcripts with differential transcript usage.

CONCLUSION

A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

Effect of de novo transcriptome assembly on transcript quantification

Correct quantification of transcript expression is essential to understand the functional elements in different physiological conditions. For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation. In this regard, this study investigates how assembly quality affects the performance of quantification based on de novo transcriptome assembly. We examined the over-extended and incomplete contigs, and demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Then we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally presented in the transcriptome or accidentally produced by assemblers. The results suggested that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remains challenging to detect the inaccurate estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read proportion of estimated abundance (RPEA) of contigs in the connected component inferenced by the quantifiers. In addition, we suggest that the estimated quantification results on the connected component level have better accuracy over sequence level quantification. The analytic results conducted in this study provides valuable insights for future development of transcriptome assembly and quantification.

Genetic effects on promoter usage are highly context-specific and contribute to complex traits

Genetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have been mapped across multiple cell types and contexts, it is challenging to distinguish between the main molecular mechanisms controlling transcript usage: promoter choice, splicing and 3' end choice. Here, we analysed RNA-seq data from human macrophages exposed to three inflammatory and one metabolic stimulus. In addition to conventional gene-level and transcript-level analyses, we also directly quantified promoter usage, splicing and 3' end usage. We found that promoters, splicing and 3' ends were predominantly controlled by independent genetic variants enriched in distinct genomic features. Promoter usage QTLs were also 50% more likely to be context-specific than other tuQTLs and constituted 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be an underappreciated molecular mechanism mediating complex trait associations in a context-specific manner.

Correction of gene model annotations improves isoform abundance estimates: the example of ketohexokinase ( Khk)

Next generation sequencing protocols such as RNA-seq have made the genome-wide characterization of the transcriptome a crucial part of many research projects in biology. Analyses of the resulting data provide key information on gene expression and in certain cases on exon or isoform usage. The emergence of transcript quantification software such as Salmon has enabled researchers to efficiently estimate isoform and gene expressions across the genome while tremendously reducing the necessary computational power. Although overall gene expression estimations were shown to be accurate, isoform expression quantifications appear to be a more challenging task. Low expression levels and uneven or insufficient coverage were reported as potential explanations for inconsistent estimates. Here, through the example of the ketohexokinase ( Khk) gene in mouse, we demonstrate that the use of an incorrect gene annotation can also result in erroneous isoform quantification results. Manual correction of the input Khk gene model provided a much more accurate estimation of relative Khk isoform expression when compared to quantitative PCR (qPCR measurements). In particular, removal of an unexpressed retained intron and a proper adjustment of the 5’ and 3’ untranslated regions both had a strong impact on the correction of erroneous estimates. Finally, we observed a better concordance in isoform quantification between datasets and sequencing strategies when relying on the newly generated Khk annotations. These results highlight the importance of accurate gene models and annotations for correct isoform quantification and reassert the need for orthogonal methods of estimation of isoform expression to confirm important findings.