SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions

tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing

Recent advances in long-read sequencing solve inaccuracies in alternative transcript identification of full-length transcripts in short-read RNA-Seq data, which encourages the development of methods for isoform-centered functional analysis. Here, we present tappAS, the first framework to enable a comprehensive Functional Iso-Transcriptomics (FIT) analysis, which is effective at revealing the functional impact of context-specific post-transcriptional regulation. tappAS uses isoform-resolved annotation of coding and non-coding functional domains, motifs, and sites, in combination with novel analysis methods to interrogate different aspects of the functional readout of transcript variants and isoform regulation. tappAS software and documentation are available at https://app.tappas.org.

GC-AG Introns Features in Long Non-coding and Protein-Coding Genes Suggest Their Role in Gene Expression Regulation

Long non-coding RNAs (lncRNAs) are recognized as an important class of regulatory molecules involved in a variety of biological functions. However, the regulatory mechanisms of long non-coding genes expression are still poorly understood. The characterization of the genomic features of lncRNAs is crucial to get insight into their function. In this study, we exploited recent annotations by GENCODE to characterize the genomic and splicing features of long non-coding genes in comparison with protein-coding ones, both in human and mouse. Our analysis highlighted differences between the two classes of genes in terms of their gene architecture. Significant differences in the splice sites usage were observed between long non-coding and protein-coding genes (PCG). While the frequency of non-canonical GC-AG splice junctions represents about 0.8% of total splice sites in PCGs, we identified a significant enrichment of the GC-AG splice sites in long non-coding genes, both in human (3.0%) and mouse (1.9%). In addition, we found a positional bias of GC-AG splice sites being enriched in the first intron in both classes of genes. Moreover, a significant shorter length and weaker donor and acceptor sites were found comparing GC-AG introns to GT-AG introns. Genes containing at least one GC-AG intron were found conserved in many species, more prone to alternative splicing and a functional analysis pointed toward their enrichment in specific biological processes such as DNA repair. Our study shows for the first time that GC-AG introns are mainly associated with lncRNAs and are preferentially located in the first intron. Additionally, we discovered their regulatory potential indicating the existence of a new mechanism of non-coding and PCGs expression regulation.

The changing paradigm of intron retention: regulation, ramifications and recipes

Intron retention (IR) is a form of alternative splicing that has long been neglected in mammalian systems although it has been studied for decades in non-mammalian species such as plants, fungi, insects and viruses. It was generally assumed that mis-splicing, leading to the retention of introns, would have no physiological consequence other than reducing gene expression by nonsense-mediated decay. Relatively recent landmark discoveries have highlighted the pivotal role that IR serves in normal and disease-related human biology. Significant technical hurdles have been overcome, thereby enabling the robust detection and quantification of IR. Still, relatively little is known about the cis- and trans-acting modulators controlling this phenomenon. The fate of an intron to be, or not to be, retained in the mature transcript is the direct result of the influence exerted by numerous intrinsic and extrinsic factors at multiple levels of regulation. These factors have altered current biological paradigms and provided unexpected insights into the transcriptional landscape. In this review, we discuss the regulators of IR and methods to identify them. Our focus is primarily on mammals, however, we broaden the scope to non-mammalian organisms in which IR has been shown to be biologically relevant.

The Arabidopsis lncRNA ASCO modulates the transcriptome through interaction with splicing factors

Alternative splicing (AS) is a major source of transcriptome diversity. Long noncoding RNAs (lncRNAs) have emerged as regulators of AS through different molecular mechanisms. In Arabidopsis thaliana, the AS regulators NSRs interact with the ALTERNATIVE SPLICING COMPETITOR (ASCO) lncRNA. Here, we analyze the effect of the knock-down and overexpression of ASCO at the genome-wide level and find a large number of deregulated and differentially spliced genes related to flagellin responses and biotic stress. In agreement, ASCO-silenced plants are more sensitive to flagellin. However, only a minor subset of deregulated genes overlaps with the AS defects of the nsra/b double mutant, suggesting an alternative way of action for ASCO. Using biotin-labeled oligonucleotides for RNA-mediated ribonucleoprotein purification, we show that ASCO binds to the highly conserved spliceosome component PRP8a. ASCO overaccumulation impairs the recognition of specific flagellin-related transcripts by PRP8a. We further show that ASCO also binds to another spliceosome component, SmD1b, indicating that it interacts with multiple splicing factors. Hence, lncRNAs may integrate a dynamic network including spliceosome core proteins, to modulate transcriptome reprogramming in eukaryotes.

Multiomics Analyses Identify Genes and Pathways Relevant to Essential Tremor

INTRODUCTION

The genetic factors and molecular mechanisms predisposing to essential tremor (ET) remains largely unknown.

OBJECTIVE

The objective of this study was to identify pathways and genes relevant to ET by integrating multiomics approaches.

METHODS

Case-control RNA sequencing of 2 cerebellar regions was done for 64 samples. A phenome-wide association study (pheWAS) of the differentially expressed genes was conducted, and a genome-wide gene association study (GWGAS) was done to identify pathways overlapping with the transcriptomic data. Finally, a transcriptome-wide association study (TWAS) was done to identify novel risk genes for ET.

RESULTS

We identified several novel dysregulated genes, including CACNA1A and SHF. Pathways including axon guidance, olfactory loss, and calcium channel activity were significantly enriched. The ET GWGAS data found calcium ion-regulated exocytosis of neurotransmitters to be significantly enriched. The TWAS also found calcium and olfactory pathways enriched. The pheWAS identified that the underexpressed differentially expressed gene, SHF, is associated with a blood pressure medication (P = 9.3E-08), which is used to reduce tremor in ET patients. Treatment of cerebellar DAOY cells with the ET drug propranolol identified increases in SHF when treated, suggesting it may rescue the underexpression.

CONCLUSION

We found that calcium-related pathways were enriched across the GWGAS, TWAS, and transcriptome. SHF was shown to have significantly decreased expression, and the pheWAS showed it was associated with blood pressure medication. The treatment of cells with propranolol showed that the drug restored levels of SHF. Overall, our findings highlight the power of integrating multiple different approaches to prioritize ET pathways and genes. © 2020 International Parkinson and Movement Disorder Society.

Organismal benefits of transcription speed control at gene boundaries

RNA polymerase II (RNAPII) transcription is crucial for gene expression. RNAPII density peaks at gene boundaries, associating these key regions for gene expression control with limited RNAPII movement. The connections between RNAPII transcription speed and gene regulation in multicellular organisms are poorly understood. Here, we directly modulate RNAPII transcription speed by point mutations in the second largest subunit of RNAPII in Arabidopsis thaliana. A RNAPII mutation predicted to decelerate transcription is inviable, while accelerating RNAPII transcription confers phenotypes resembling auto-immunity. Nascent transcription profiling revealed that RNAPII complexes with accelerated transcription clear stalling sites at both gene ends, resulting in read-through transcription. The accelerated transcription mutant NRPB2-Y732F exhibits increased association with 5' splice site (5'SS) intermediates and enhanced splicing efficiency. Our findings highlight potential advantages of RNAPII stalling through local reduction in transcription speed to optimize gene expression for the development of multicellular organisms.

dSreg: a Bayesian model to integrate changes in splicing and RNA-binding protein activity

MOTIVATION

Alternative splicing (AS) is an important mechanism in the generation of transcript diversity across mammals. AS patterns are dynamically regulated during development and in response to environmental changes. Defects or perturbations in its regulation may lead to cancer or neurological disorders, among other pathological conditions. The regulatory mechanisms controlling AS in a given biological context are typically inferred using a two-step framework: differential AS analysis followed by enrichment methods. These strategies require setting rather arbitrary thresholds and are prone to error propagation along the analysis.

RESULTS

To overcome these limitations, we propose dSreg, a Bayesian model that integrates RNA-seq with data from regulatory features, e.g. binding sites of RNA-binding proteins. dSreg identifies the key underlying regulators controlling AS changes and quantifies their activity while simultaneously estimating the changes in exon inclusion rates. dSreg increased both the sensitivity and the specificity of the identified AS changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RNA-binding proteins' experiments from ENCODE, as opposed to traditional enrichment methods, such as over-representation analysis and gene set enrichment analysis. dSreg opens the possibility to integrate a large amount of readily available RNA-seq datasets at low coverage for AS analysis and allows more cost-effective RNA-seq experiments.

AVAILABILITY AND IMPLEMENTATION

dSreg was implemented in python using stan and is freely available to the community at https://bitbucket.org/cmartiga/dsreg.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CuAS: a database of annotated transcripts generated by alternative splicing in cucumbers

BACKGROUND

Alternative splicing (AS) plays a critical regulatory role in modulating transcriptome and proteome diversity. In particular, it increases the functional diversity of proteins. Recent genome-wide analysis of AS using RNA-Seq has revealed that AS is highly pervasive in plants. Furthermore, it has been suggested that most AS events are subject to tissue-specific regulation.

DESCRIPTION

To reveal the functional characteristics induced by AS and tissue-specific splicing events, a database for exploring these characteristics is needed, especially in plants. To address these goals, we constructed a database of annotated transcripts generated by alternative splicing in cucumbers (CuAS: http://cmb.bnu.edu.cn/alt_iso/index.php) that integrates genomic annotations, isoform-level functions, isoform-level features, and tissue-specific AS events among multiple tissues. CuAS supports a retrieval system that identifies unique IDs (gene ID, isoform ID, UniProt ID, and gene name), chromosomal positions, and gene families, and a browser for visualization of each gene.

CONCLUSION

We believe that CuAS could be helpful for revealing the novel functional characteristics induced by AS and tissue-specific AS events in cucumbers. CuAS is freely available at http://cmb.bnu.edu.cn/alt_iso/index.php.

SEQing: web-based visualization of iCLIP and RNA-seq data in an interactive python framework

Background

RNA-binding proteins interact with their target RNAs at specific sites. These binding sites can be determined genome-wide through individual nucleotide resolution crosslinking immunoprecipitation (iCLIP). Subsequently, the binding sites have to be visualized. So far, no visualization tool exists that is easily accessible but also supports restricted access so that data can be shared among collaborators.

Results

Here we present SEQing, a customizable interactive dashboard to visualize crosslink sites on target genes of RNA-binding proteins that have been obtained by iCLIP. Moreover, SEQing supports RNA-seq data that can be displayed in a different window tab. This allows, e.g. crossreferencing the iCLIP data with genes differentially expressed in mutants of the RBP and thus obtain some insights into a potential functional relevance of the binding sites. Additionally, detailed information on the target genes can be incorporated in another tab.

Conclusion

SEQing is written in Python3 and runs on Linux. The web-based access makes iCLIP data easily accessible, even with mobile devices. SEQing is customizable in many ways and has also the option to be secured by a password. The source code is available at https://github.com/malewins/SEQing.

BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty

Alternative splicing is a biological process during gene expression that allows a single gene to code for multiple proteins. However, splicing patterns can be altered in some conditions or diseases. Here, we present BANDITS, a R/Bioconductor package to perform differential splicing, at both gene and transcript level, based on RNA-seq data. BANDITS uses a Bayesian hierarchical structure to explicitly model the variability between samples and treats the transcript allocation of reads as latent variables. We perform an extensive benchmark across both simulated and experimental RNA-seq datasets, where BANDITS has extremely favourable performance with respect to the competitors considered.

Dynamic Alternative Splicing During Mouse Preimplantation Embryo Development

The mechanism of alternative pre-mRNA splicing (AS) during preimplantation development is largely unknown. In order to capture the dynamic changes of AS occurring during embryogenesis, we carried out bioinformatics analysis based on scRNA-seq data over the time-course preimplantation development in mouse. We detected numerous previously-unreported differentially expressed genes at specific developmental stages and investigated the nature of AS at both minor and major zygotic genome activation (ZGA). The AS and differential AS atlas over preimplantation development were established. The differentially alternatively spliced genes (DASGs) are likely to be key splicing factors (SFs) during preimplantation development. We also demonstrated that there is a regulatory cascade of AS events in which some key SFs are regulated by differentially AS of their own gene transcripts. Moreover, 212 isoform switches (ISs) during preimplantation development were detected, which may be critical for decoding the mechanism of early embryogenesis. Importantly, we uncovered that zygotic AS activation (ZASA) is in conformity with ZGA and revealed that AS is coupled with transcription during preimplantation development. Our results may provide a deeper insight into the regulation of early embryogenesis.

Differential splicing analysis based on isoforms expression with NBSplice

Alternative splicing alterations have been widely related to several human diseases revealing the importance of their study for the success of translational medicine. Differential splicing (DS) occurrence has been mainly analyzed through exon-based approaches over RNA-seq data. Although these strategies allow identifying differentially spliced genes, they ignore the identity of the affected gene isoforms which is crucial to understand the underlying pathological processes behind alternative splicing changes. Moreover, despite several isoform quantification tools for RNA-seq data have been recently developed, DS tools have not taken advantage of them. Here, the NBSplice R package for differential splicing analysis by means of isoform expression data is presented. It estimates differences on relative expressions of gene transcripts between experimental conditions to infer changes in gene alternative splicing patterns. The developed tool was evaluated using a synthetic RNA-seq dataset with controlled differential splicing. NBSplice accurately predicted DS occurrence, outperforming current methods in terms of accuracy, sensitivity, F-score, and false discovery rate control. The usefulness of our development was demonstrated by the analysis of a real cancer dataset, revealing new differentially spliced genes that could be studied pursuing new colorectal cancer biomarkers discovery.

ASCOT identifies key regulators of neuronal subtype-specific splicing

Public archives of next-generation sequencing data are growing exponentially, but the difficulty of marshaling this data has led to its underutilization by scientists. Here, we present ASCOT, a resource that uses annotation-free methods to rapidly analyze and visualize splice variants across tens of thousands of bulk and single-cell data sets in the public archive. To demonstrate the utility of ASCOT, we identify novel cell type-specific alternative exons across the nervous system and leverage ENCODE and GTEx data sets to study the unique splicing of photoreceptors. We find that PTBP1 knockdown and MSI1 and PCBP2 overexpression are sufficient to activate many photoreceptor-specific exons in HepG2 liver cancer cells. This work demonstrates how large-scale analysis of public RNA-Seq data sets can yield key insights into cell type-specific control of RNA splicing and underscores the importance of considering both annotated and unannotated splicing events.

The complexity of alternative splicing and landscape of tissue-specific expression in lotus (Nelumbo nucifera) unveiled by Illumina- and single-molecule real-time-based RNA-sequencing

Alternative splicing (AS) plays a critical role in regulating different physiological and developmental processes in eukaryotes, by dramatically increasing the diversity of the transcriptome and the proteome. However, the saturation and complexity of AS remain unclear in lotus due to its limitation of rare obtainment of full-length multiple-splice isoforms. In this study, we apply a hybrid assembly strategy by combining single-molecule real-time sequencing and Illumina RNA-seq to get a comprehensive insight into the lotus transcriptomic landscape. We identified 211,802 high-quality full-length non-chimeric reads, with 192,690 non-redundant isoforms, and updated the lotus reference gene model. Moreover, our analysis identified a total of 104,288 AS events from 16,543 genes, with alternative 3ʹ splice-site being the predominant model, following by intron retention. By exploring tissue datasets, 370 tissue-specific AS events were identified among 12 tissues. Both the tissue-specific genes and isoforms might play important roles in tissue or organ development, and are suitable for ‘ABCE’ model partly in floral tissues. A large number of AS events and isoform variants identified in our study enhance the understanding of transcriptional diversity in lotus, and provide valuable resource for further functional genomic studies.

Beyond mass spectrometry, the next step in proteomics

Proteins can be the root cause of a disease, and they can be used to cure it. The need to identify these critical actors was recognized early (1951) by Sanger; the first biopolymer sequenced was a peptide, insulin. With the advent of scalable, single-molecule DNA sequencing, genomics and transcriptomics have since propelled medicine through improved sensitivity and lower costs, but proteomics has lagged behind. Currently, proteomics relies mainly on mass spectrometry (MS), but instead of truly sequencing, it classifies a protein and typically requires about a billion copies of a protein to do it. Here, we offer a survey that illuminates a few alternatives with the brightest prospects for identifying whole proteins and displacing MS for sequencing them. These alternatives all boast sensitivity superior to MS and promise to be scalable and seem to be adaptable to bioinformatics tools for calling the sequence of amino acids that constitute a protein.

Systematic evaluation of differential splicing tools for RNA-seq studies

Differential splicing (DS) is a post-transcriptional biological process with critical, wide-ranging effects on a plethora of cellular activities and disease processes. To date, a number of computational approaches have been developed to identify and quantify differentially spliced genes from RNA-seq data, but a comprehensive intercomparison and appraisal of these approaches is currently lacking. In this study, we systematically evaluated 10 DS analysis tools for consistency and reproducibility, precision, recall and false discovery rate, agreement upon reported differentially spliced genes and functional enrichment. The tools were selected to represent the three different methodological categories: exon-based (DEXSeq, edgeR, JunctionSeq, limma), isoform-based (cuffdiff2, DiffSplice) and event-based methods (dSpliceType, MAJIQ, rMATS, SUPPA). Overall, all the exon-based methods and two event-based methods (MAJIQ and rMATS) scored well on the selected measures. Of the 10 tools tested, the exon-based methods performed generally better than the isoform-based and event-based methods. However, overall, the different data analysis tools performed strikingly differently across different data sets or numbers of samples.

Full-length transcriptome sequencing from multiple tissues of duck, Anas platyrhynchos

Duck (Anas platyrhynchos), one of the most economically important waterfowl, is an ideal model for studying the immune protection mechanism of birds. An incomplete duck reference genome and very limited availability of full-length cDNAs has hindered the identification of alternatively spliced transcripts and slowed down many basic studies in ducks. We applied PacBio Iso-Seq technologies to multiple tissues from duck for use in transcriptome sequencing. We obtained 199,993 full-length transcripts and comprehensively annotated these transcripts. 23,755 lncRNAs were predicted from all identified transcripts and 35,031 alternative splicing events, which divided into 5 models, were accurately predicted from 3,346 genes. Our data constitute a large increase in the known number of both lncRNA, and alternatively spliced transcripts of duck and plays an important role in improving current genome annotation. In addition, the data will be extremely useful for functional studies in other birds.

A unicellular relative of animals generates a layer of polarized cells by actomyosin-dependent cellularization

In animals, cellularization of a coenocyte is a specialized form of cytokinesis that results in the formation of a polarized epithelium during early embryonic development. It is characterized by coordinated assembly of an actomyosin network, which drives inward membrane invaginations. However, whether coordinated cellularization driven by membrane invagination exists outside animals is not known. To that end, we investigate cellularization in the ichthyosporean Sphaeroforma arctica, a close unicellular relative of animals. We show that the process of cellularization involves coordinated inward plasma membrane invaginations dependent on an actomyosin network and reveal the temporal order of its assembly. This leads to the formation of a polarized layer of cells resembling an epithelium. We show that this stage is associated with tightly regulated transcriptional activation of genes involved in cell adhesion. Hereby we demonstrate the presence of a self-organized, clonally-generated, polarized layer of cells in a unicellular relative of animals.

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis

Background

Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantification. This coupling precludes the direct usage of pseudo-alignment to other expression analyses, including alternative splicing or differential gene expression analysis, without including a non-essential transcript quantification step.

Results

In this paper, we introduce a transcriptome segmentation approach to decouple these two tasks. We propose an efficient algorithm to generate maximal disjoint segments given a transcriptome reference library on which ultra-fast pseudo-alignment can be used to produce per-sample segment counts. We show how to apply these maximally unambiguous count statistics in two specific expression analyses – alternative splicing and gene differential expression – without the need of a transcript quantification step. Our experiments based on simulated and experimental data showed that the use of segment counts, like other methods that rely on local coverage statistics, provides an advantage over approaches that rely on transcript quantification in detecting and correctly estimating local splicing in the case of incomplete transcript annotations.

Conclusions

The transcriptome segmentation approach implemented in Yanagi exploits the computational and space efficiency of pseudo-alignment approaches. It significantly expands their applicability and interpretability in a variety of RNA-seq analyses by providing the means to model and capture local coverage variation in these analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2947-6) contains supplementary material, which is available to authorized users.

Alternative mRNA splicing in cancer immunotherapy

Immunotherapies are yielding effective treatments for several previously untreatable cancers. Still, the identification of suitable antigens specific to the tumour that can be targets for cancer vaccines and T cell therapies is a challenge. Alternative processing of mRNA, a phenomenon that has been shown to alter the proteomic diversity of many cancers, may offer the potential of a broadened target space. Here, we discuss the promise of analysing mRNA processing events in cancer cells, with an emphasis on mRNA splicing, for the identification of potential new targets for cancer immunotherapy. Further, we highlight the challenges that must be overcome for this new avenue to have clinical applicability.

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.

TGStools: A Bioinformatics Suit to Facilitate Transcriptome Analysis of Long Reads from Third Generation Sequencing Platform

Recent analyses show that transcriptome sequencing can be utilized as a diagnostic tool for rare Mendelian diseases. The third generation sequencing de novo detects long reads of thousands of base pairs, thus greatly expanding the isoform discovery and identification of novel long noncoding RNAs. In this study, we developed TGStools, a bioinformatics suite to facilitate routine tasks such as characterizing full-length transcripts, detecting shifted types of alternative splicing, and long noncoding RNAs (lncRNAs) identification in transcriptome analysis. It also prioritizes the transcripts with a visualization framework that automatically integrates rich annotation with known genomic features. TGStools is a Python package freely available at Github.

Long-read direct RNA sequencing by 5'-Cap capturing reveals the impact of Piwi on the widespread exonization of transposable elements in locusts

The large genome of the migratory locust (Locusta migratoria) genome accumulates massive amount of accumulated transposable elements (TEs), which show intrinsic transcriptional activities. Hampering the ability to precisely determine full-length RNA transcript sequences are exonized TEs, which produce numerous highly similar fragments that are difficult to resolve using short-read sequencing technology. Here, we applied a 5'-Cap capturing method using Nanopore long-read direct RNA sequencing to characterize full-length transcripts in their native RNA form and to analyze the TE exonization pattern in the locust transcriptome. Our results revealed the widespread establishment of TE exonization and a substantial contribution of TEs to RNA splicing in the locust transcriptome. The results of the transcriptomic spectrum influenced by Piwi expression indicated that TE-derived sequences were the main targets of Piwi-mediated repression. Furthermore, our study showed that Piwi expression regulates the length of RNA transcripts containing TE-derived sequences, creating an alternative UTR usage. Overall, our results reveal the transcriptomic characteristics of TE exonization in the species characterized by large and repetitive genomes.

Microcystin-LR-regulated transcriptome dynamics in ZFL cells

Microcystin-LR (MC-LR) is a highly toxic hepatotoxin that poses great hazards to aquatic organisms and even human health. The zebrafish liver cell line (ZFL) is a valuable model for investigating toxicity and metabolism of toxicants. However, the toxicity of MC-LR and its effects on gene transcription of ZFL cells remains to be characterized. In this study, we determined the toxicity of MC-LR for ZFL cells and investigated the effects of MC-LR on cellular transcriptome dynamics. The EC₅₀ of MC-LR for ZFL cells was 80.123 μg/mL. The ZFL cells were exposed to 10 μg/mL MC-LR for 0, 1, 3, 6, 12 or 24 h, and RNA-sequencing was performed to analyze gene transcription. A total of 10,209 genes were found to be regulated by MC-LR. The numbers of up- and down-regulated genes at different time points ranged from 2179 to 3202 and from 1501 to 2597, respectively. Furthermore, 1543 genes underwent differential splicing (AS) upon MC-LR exposure, of which 620 were not identified as differentially expressed gene (DEG). The effects of MC-LR on cellular functions were highly time-dependent. MAPK (mitogen-activated protein kinase) and FoxO (forkhead box O) signaling pathways were the most prominent pathways activated by MC-LR. Steroid biosynthesis and terpenoid backbone biosynthesis were the most enriched for the down-regulated genes. A gene regulatory network was constructed from the expression profile datasets and the candidate master transcription factors were identified. Our results shed light on the molecular mechanisms of MC-LR cellular toxicity and the transcriptome landscapes of ZFL cells upon MC-LR toxicity.

Deep-learning augmented RNA-seq analysis of transcript splicing

A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions with empirical RNA-seq evidence to infer differential alternative splicing between biological samples. DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulation via deep learning, thereby helping researchers better characterize alternative splicing using RNA-seq datasets even with modest coverage.

Relative Abundance of Transcripts ( RATs): Identifying differential isoform abundance from RNA-seq

The biological importance of changes in RNA expression is reflected by the wide variety of tools available to characterise these changes from RNA-seq data. Several tools exist for detecting differential transcript isoform usage (DTU) from aligned or assembled RNA-seq data, but few exist for DTU detection from alignment-free RNA-seq quantifications. We present the RATs, an R package that identifies DTU transcriptome-wide directly from transcript abundance estimates. RATs is unique in applying bootstrapping to estimate the reliability of detected DTU events and shows good performance at all replication levels (median false positive fraction < 0.05). We compare RATs to two existing DTU tools, DRIM-Seq & SUPPA2, using two publicly available simulated RNA-seq datasets and a published human RNA-seq dataset, in which 248 genes have been previously identified as displaying significant DTU. RATs with default threshold values on the simulated Human data has a sensitivity of 0.55, a Matthews correlation coefficient of 0.71 and a false discovery rate (FDR) of 0.04, outperforming both other tools. Applying the same thresholds for SUPPA2 results in a higher sensitivity (0.61) but poorer FDR performance (0.33). RATs and DRIM-seq use different methods for measuring DTU effect-sizes complicating the comparison of results between these tools, however, for a likelihood-ratio threshold of 30, DRIM-Seq has similar FDR performance to RATs (0.06), but worse sensitivity (0.47). These differences persist for the simulated drosophila dataset. On the published human RNA-seq dataset the greatest agreement between the tools tested is 53%, observed between RATs and SUPPA2. The bootstrapping quality filter in RATs is responsible for removing the majority of DTU events called by SUPPA2 that are not reported by RATs. All methods, including the previously published qRT-PCR of three of the 248 detected DTU events, were found to be sensitive to annotation differences between Ensembl v60 and v87.

Gene editing of the multi-copy H2A.B gene and its importance for fertility

BACKGROUND

Altering the biochemical makeup of chromatin by the incorporation of histone variants during development represents a key mechanism in regulating gene expression. The histone variant H2A.B, H2A.B.3 in mice, appeared late in evolution and is most highly expressed in the testis. In the mouse, it is encoded by three different genes. H2A.B expression is spatially and temporally regulated during spermatogenesis being most highly expressed in the haploid round spermatid stage. Active genes gain H2A.B where it directly interacts with polymerase II and RNA processing factors within splicing speckles. However, the importance of H2A.B for gene expression and fertility are unknown.

RESULTS

Here, we report the first mouse knockout of this histone variant and its effects on fertility, nuclear organization, and gene expression. In view of the controversy related to the generation of off-target mutations by gene editing approaches, we test the specificity of TALENs by disrupting the H2A.B multi-copy gene family using only one pair of TALENs. We show that TALENs do display a high level of specificity since no off-target mutations are detected by bioinformatics analyses of exome sequences obtained from three consecutive generations of knockout mice and by Sanger DNA sequencing. Male H2A.B.3 knockout mice are subfertile and display an increase in the proportion of abnormal sperm and clogged seminiferous tubules. Significantly, a loss of proper RNA Pol II targeting to distinct transcription-splicing territories and changes to pre-mRNA splicing are observed.

CONCLUSION

We have produced the first H2A.B knockout mouse using the TALEN approach.

Conserved regulation of RNA processing in somatic cell reprogramming

BACKGROUND

Along with the reorganization of epigenetic and transcriptional networks, somatic cell reprogramming brings about numerous changes at the level of RNA processing. These include the expression of specific transcript isoforms and 3' untranslated regions. A number of studies have uncovered RNA processing factors that modulate the efficiency of the reprogramming process. However, a comprehensive evaluation of the involvement of RNA processing factors in the reprogramming of somatic mammalian cells is lacking.

RESULTS

Here, we used data from a large number of studies carried out in three mammalian species, mouse, chimpanzee and human, to uncover consistent changes in gene expression upon reprogramming of somatic cells. We found that a core set of nine splicing factors have consistent changes across the majority of data sets in all three species. Most striking among these are ESRP1 and ESRP2, which accelerate and enhance the efficiency of somatic cell reprogramming by promoting isoform expression changes associated with mesenchymal-to-epithelial transition. We further identify genes and processes in which splicing changes are observed in both human and mouse.

CONCLUSIONS

Our results provide a general resource for gene expression and splicing changes that take place during somatic cell reprogramming. Furthermore, they support the concept that splicing factors with evolutionarily conserved, cell type-specific expression can modulate the efficiency of the process by reinforcing intermediate states resembling the cell types in which these factors are normally expressed.

Hybrid sequencing-based personal full-length transcriptomic analysis implicates proteostatic stress in metastatic ovarian cancer

Comprehensive molecular characterization of myriad somatic alterations and aberrant gene expressions at personal level is key to precision cancer therapy, yet limited by current short-read sequencing technology, individualized catalog of complete genomic and transcriptomic features is thus far elusive. Here, we integrated second- and third-generation sequencing platforms to generate a multidimensional dataset on a patient affected by metastatic epithelial ovarian cancer. Whole-genome and hybrid transcriptome dissection captured global genetic and transcriptional variants at previously unparalleled resolution. Particularly, single-molecule mRNA sequencing identified a vast array of unannotated transcripts, novel long noncoding RNAs and gene chimeras, permitting accurate determination of transcription start, splice, polyadenylation and fusion sites. Phylogenetic and enrichment inference of isoform-level measurements implicated early functional divergence and cytosolic proteostatic stress in shaping ovarian tumorigenesis. A complementary imaging-based high-throughput drug screen was performed and subsequently validated, which consistently pinpointed proteasome inhibitors as an effective therapeutic regime by inducing protein aggregates in ovarian cancer cells. Therefore, our study suggests that clinical application of the emerging long-read full-length analysis for improving molecular diagnostics is feasible and informative. An in-depth understanding of the tumor transcriptome complexity allowed by leveraging the hybrid sequencing approach lays the basis to reveal novel and valid therapeutic vulnerabilities in advanced ovarian malignancies.

Long Read Single-Molecule Real-Time Sequencing Elucidates Transcriptome-Wide Heterogeneity and Complexity in Esophageal Squamous Cells

Esophageal squamous cell carcinoma is a leading cause of cancer death. Mapping the transcriptional landscapes such as isoforms, fusion transcripts, as well as long noncoding RNAs have played a central role to understand the regulating mechanism during malignant processes. However, canonical methods such as short-read RNA-seq are difficult to define the entire polyadenylated RNA molecules. Here, we combined single-molecule real-time sequencing with RNA-seq to generate high-quality long reads and to survey the transcriptional program in esophageal squamous cells. Compared with the recent annotations of human transcriptome (Ensembl 38 release 91), single-molecule real-time data identified many unannotated transcripts, novel isoforms of known genes and an expanding repository of long intergenic noncoding RNAs (lincRNAs). By integrating with annotation of lincRNA catalog, 1,521 esophageal-cancer-specific lincRNAs were defined from single-molecule real-time reads. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these lincRNAs and their target genes are involved in a variety of cancer signaling pathways. Isoform usage analysis revealed the shifted alternative splicing patterns, which can be recaptured from clinical samples or supported by previous studies. Utilizing vigorous searching criteria, we also detected multiple transcript fusions, which are not documented in current gene fusion database or readily identified from RNA-seq reads. Two novel fusion transcripts were verified based on real-time PCR and Sanger sequencing. Overall, our long-read single-molecule sequencing largely expands current understanding of full-length transcriptome in esophageal cells and provides novel insights on the transcriptional diversity during oncogenic transformation.

Androgen-regulated transcription of ESRP2 drives alternative splicing patterns in prostate cancer

Prostate is the most frequent cancer in men. Prostate cancer progression is driven by androgen steroid hormones, and delayed by androgen deprivation therapy (ADT). Androgens control transcription by stimulating androgen receptor (AR) activity, yet also control pre-mRNA splicing through less clear mechanisms. Here we find androgens regulate splicing through AR-mediated transcriptional control of the epithelial-specific splicing regulator ESRP2. Both ESRP2 and its close paralog ESRP1 are highly expressed in primary prostate cancer. Androgen stimulation induces splicing switches in many endogenous ESRP2-controlled mRNA isoforms, including splicing switches correlating with disease progression. ESRP2 expression in clinical prostate cancer is repressed by ADT, which may thus inadvertently dampen epithelial splice programmes. Supporting this, treatment with the AR antagonist bicalutamide (Casodex) induced mesenchymal splicing patterns of genes including FLNB and CTNND1. Our data reveals a new mechanism of splicing control in prostate cancer with important implications for disease progression.

ASGAL: aligning RNA-Seq data to a splicing graph to detect novel alternative splicing events

Background

While the reconstruction of transcripts from a sample of RNA-Seq data is a computationally expensive and complicated task, the detection of splicing events from RNA-Seq data and a gene annotation is computationally feasible. This latter task, which is adequate for many transcriptome analyses, is usually achieved by aligning the reads to a reference genome, followed by comparing the alignments with a gene annotation, often implicitly represented by a graph: the splicing graph.

Results

We present ASGAL (Alternative Splicing Graph ALigner): a tool for mapping RNA-Seq data to the splicing graph, with the specific goal of detecting novel splicing events, involving either annotated or unannotated splice sites. ASGAL takes as input the annotated transcripts of a gene and a RNA-Seq sample, and computes (1) the spliced alignments of each read in input, and (2) a list of novel events with respect to the gene annotation.

Conclusions

An experimental analysis shows that ASGAL allows to enrich the annotation with novel alternative splicing events even when genes in an experiment express at most one isoform. Compared with other tools which use the spliced alignment of reads against a reference genome for differential analysis, ASGAL better predicts events that use splice sites which are novel with respect to a splicing graph, showing a higher accuracy. To the best of our knowledge, ASGAL is the first tool that detects novel alternative splicing events by directly aligning reads to a splicing graph.

Availability

Source code, documentation, and data are available for download at http://asgal.algolab.eu.

The Splicing Factor RNA-Binding Fox Protein 1 Mediates the Cellular Immune Response in Drosophila melanogaster

The uptake and destruction of bacteria by phagocytic cells is an essential defense mechanism in metazoans. To identify novel genes involved in the phagocytosis of Staphylococcus aureus, a major human pathogen, we assessed the phagocytic capacity of adult blood cells (hemocytes) of the fruit fly, Drosophila melanogaster, by testing several lines of the Drosophila Genetic Reference Panel. Natural genetic variation in the gene RNA-binding Fox protein 1 (Rbfox1) correlated with low phagocytic capacity in hemocytes, pointing to Rbfox1 as a candidate regulator of phagocytosis. Loss of Rbfox1 resulted in increased expression of the Ig superfamily member Down syndrome adhesion molecule 4 (Dscam4). Silencing of Dscam4 in Rbfox1-depleted blood cells rescued the fly's cellular immune response to S. aureus, indicating that downregulation of Dscam4 by Rbfox1 is critical for S. aureus phagocytosis in Drosophila To our knowledge, this study is the first to demonstrate a link between Rbfox1, Dscam4, and host defense against S. aureus.

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

Gene Regulatory Network Perturbation by Genetic and Epigenetic Variation

Gene regulatory networks underlie biological function and cellular physiology. Alternative splicing (AS) is a fundamental step in gene regulatory networks and plays a key role in development and disease. In addition to the identification of aberrant AS events, an increasing number of studies are focusing on molecular determinants of AS, including genetic and epigenetic regulators. We review here recent efforts to identify various deregulated AS events as well as their molecular determinants that alter biological functions, and discuss clinical features of AS and their druggable potential.