ASpedia: a comprehensive encyclopedia of human alternative splicing

ASpedia-R: a package to retrieve junction-incorporating features and knowledge-based functions of human alternative splicing events

Motivation

Alternative splicing (AS) is a key regulatory mechanism that confers genetic diversity and phenotypic plasticity of human. The exons and their flanking regions include comprehensive junction-incorporating sequence features like splicing factor-binding sites and protein domains. These elements involve in exon usage and finally contribute to isoform-specific biological functions. Splicing-associated sequence features are involved in the multilayered regulation encompassing DNA and proteins. However, most analysis applications have investigated limited sequence features, like protein domains. It is insufficient to explain the comprehensive cause and effect of exon-specific biological processes.

Results

With the advent of RNA-seq technology, global AS event analysis has deduced more precise results. As accumulating analysis results, it could be a challenge to identify multi-omics sequence features for AS events. Therefore, application to investigate multi-omics sequence features is useful to scan critical evidence. ASpedia-R is an R package to interrogate junction-incorporating sequence features for human genes. Our database collected the heterogeneous profile encompassed from DNA to protein. Additionally, knowledge-based splicing genes were collected using text-mining to test the association with specific pathway terms. Our package retrieves AS events for high-throughput data analysis results via AS event ID converter. Finally, result profile could be visualized and saved to multiple formats: sequence feature result table, genome track figure, protein-protein interaction network, and gene set enrichment test result table. Our package is a convenient tool to understand global regulation mechanisms by splicing.

Availability and implementation

The package source code is freely available to non-commercial users at https://github.com/ncc-bioinfo/ASpedia-R.

RNA splicing analysis deciphers developmental hierarchies and reveals therapeutic targets in adult glioma

Widespread alterations in RNA alternative splicing (AS) have been identified in adult gliomas. However, their regulatory mechanism, biological significance, and therapeutic potential remain largely elusive. Here, using a computational approach with both bulk and single-cell RNA-Seq, we uncover a prognostic AS signature linked with neural developmental hierarchies. Using advanced iPSC glioma models driven by glioma driver mutations, we show that this AS signature could be enhanced by EGFRvIII and inhibited by in situ IDH1 mutation. Functional validations of 2 isoform switching events in CERS5 and MPZL1 show regulations of sphingolipid metabolism and SHP2 signaling, respectively. Analysis of upstream RNA binding proteins reveals PTBP1 as a key regulator of the AS signature where targeting of PTBP1 suppresses tumor growth and promotes the expression of a neuron marker TUJ1 in glioma stem-like cells. Overall, our data highlights the role of AS in affecting glioma malignancy and heterogeneity and its potential as a therapeutic vulnerability for treating adult gliomas.

SplicingLore: a web resource for studying the regulation of cassette exons by human splicing factors

One challenge faced by scientists from the alternative RNA splicing field is to decode the cooperative or antagonistic effects of splicing factors (SFs) to understand and eventually predict splicing outcomes on a genome-wide scale. In this manuscript, we introduce SplicingLore, an open-access database and web resource that help to fill this gap in a straightforward manner. The database contains a collection of RNA-sequencing-derived lists of alternative exons regulated by a total of 75 different SFs. All datasets were processed in a standardized manner, ensuring valid comparisons and correlation analyses. The user can easily retrieve a factor-specific set of differentially included exons from the database or provide a list of exons and search which SF(s) control(s) their inclusion. Our simple workflow is fast and easy to run, and it ensures a reliable calculation of correlation scores between the tested datasets. As a proof of concept, we predicted and experimentally validated a novel functional cooperation between the RNA helicases DDX17 and DDX5 and the heterogeneous nuclear ribonucleoprotein C (HNRNPC) protein. SplicingLore is available at https://splicinglore.ens-lyon.fr/. Database URL:  https://splicinglore.ens-lyon.fr/.

Alternative splicing: a new breakthrough for understanding tumorigenesis and potential clinical applications

BACKGROUND

Alternative splicing (AS) is a post-transcriptional process that produces transcript variants, thus leading to transcriptome complexity. Recently, the scope of AS studies has been greatly expanded toward clinical applications owing to the abundance of RNA sequencing data.

OBJECTIVE

This review consists of two parts. We first summarize bioinformatic resources that are useful for large-scale cancer-related AS studies. We then highlight the research efforts to utilize AS events for predicting clinical outcomes and planning therapeutic strategies.

RESULTS

Computational approaches to interrogate AS events have been reviewed under three categories: (1) databases to provide functional and clinical annotation of AS events, (2) analytical tools to identify cancer-associated AS event, and (3) methods to identify splicing-related DNA variants and splicing-derived neoantigens. We also present the recent progress in exploring the clinical utility of AS under four categories: (1) identification of AS events for cancer prognosis, (2) utilization of AS events in molecular classification of various cancers, (3) regulatory mechanisms of AS underlying drug resistance, and (4) potential use of AS in cancer therapy.

CONCLUSION

This review will be helpful for understanding the biological implications of AS in cancer and facilitate the development of AS markers for cancer prognosis and treatment. We anticipate that future studies will lead to the application of genome-wide AS profiles in cancer precision medicine.

IntroVerse: a comprehensive database of introns across human tissues

Dysregulation of RNA splicing contributes to both rare and complex diseases. RNA-sequencing data from human tissues has shown that this process can be inaccurate, resulting in the presence of novel introns detected at low frequency across samples and within an individual. To enable the full spectrum of intron use to be explored, we have developed IntroVerse, which offers an extensive catalogue on the splicing of 332,571 annotated introns and a linked set of 4,679,474 novel junctions covering 32,669 different genes. This dataset has been generated through the analysis of 17,510 human control RNA samples from 54 tissues provided by the Genotype-Tissue Expression Consortium. IntroVerse has two unique features: (i) it provides a complete catalogue of novel junctions and (ii) each novel junction has been assigned to a specific annotated intron. This unique, hierarchical structure offers multiple uses, including the identification of novel transcripts from known genes and their tissue-specific usage, and the assessment of background splicing noise for introns thought to be mis-spliced in disease states. IntroVerse provides a user-friendly web interface and is freely available at https://rytenlab.com/browser/app/introverse.

Splicing signature database development to delineate cancer pathways using literature mining and transcriptome machine learning

Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

FishExp: A comprehensive database and analysis platform for gene expression and alternative splicing of fish species

The publicly archived RNA-seq data has grown exponentially, while its valuable information has not yet been fully discovered and utilized, such as alternative splicing and its integration with gene expression. This is especially true for fish species which play important roles in ecology, research and the food industry. Furthermore, there is a lack of online platform to analyze users’ new data individually and jointly with existing data for the comprehensive analysis of alternative splicing and gene expression. Here, we present FishExp, a web-based data platform covering gene expression and alternative splicing in 26,081 RNA-seq experiments from 44 fishes. It allows users to query the data in a variety of ways, including gene identifier/symbol, functional term, and BLAST alignment. Moreover, users can customize experiments and tools to perform differential/specific expression and alternative splicing analysis, co-expression and cross-species analysis. In addition, functional enrichment is provided to confer biological significance. Notably, users are allowed to submit their own data and perform various analyses using the new data alone or alongside existing data in FishExp. Results of retrieval and analysis can be visualized on the gene-, transcript- and splicing event-level webpage in a highly interactive and intuitive manner. All data in FishExp can be downloaded for more in-depth analysis. The manually curated sample information, uniform data processing and various tools make it efficient for users to gain new insights from these large data sets, facilitating scientific hypothesis generation. FishExp is freely accessible at https://bioinfo.njau.edu.cn/fishExp.

ASpediaFI: Functional Interaction Analysis of Alternative Splicing Events

Alternative splicing (AS) regulates biological processes governing phenotypes and diseases. Differential AS (DAS) gene test methods have been developed to investigate important exonic expression from high-throughput datasets. However, the DAS events extracted using statistical tests are insufficient to delineate relevant biological processes. In this study, we developed a novel application, Alternative Splicing Encyclopedia: Functional Interaction (ASpediaFI), to systemically identify DAS events and co-regulated genes and pathways. ASpediaFI establishes a heterogeneous interaction network of genes and their feature nodes (i.e., AS events and pathways) connected by co-expression or pathway gene set knowledge. Next, ASpediaFI explores the interaction network using the random walk with restart algorithm and interrogates the proximity from a query gene set. Finally, ASpediaFI extracts significant AS events, genes, and pathways. To evaluate the performance of our method, we simulated RNA sequencing (RNA-seq) datasets to consider various conditions of sequencing depth and sample size. The performance was compared with that of other methods. Additionally, we analyzed three public datasets of cancer patients or cell lines to evaluate how well ASpediaFI detects biologically relevant candidates. ASpediaFI exhibits strong performance in both simulated and public datasets. Our integrative approach reveals that DAS events that recognize a global co-expression network and relevant pathways determine the functional importance of spliced genes in the subnetwork. ASpediaFI is publicly available at https://bioconductor.org/packages/ASpediaFI.

Transcriptomic Evidence of the Immune Response Activation in Individuals With Limb Girdle Muscular Dystrophy Dominant 2 (LGMDD2) Contributes to Resistance to HIV-1 Infection

LGMDD2 is a rare form of muscular dystrophy characterized by one of the three heterozygous deletions described within the TNPO3 gene that result in the addition of a 15-amino acid tail in the C-terminus.TNPO3 is involved in the nuclear import of splicing factors and acts as a host cofactor for HIV-1 infection by mechanisms not yet deciphered. Further characterization of the crosstalk between HIV-1 infection and LGMDD2 disease may contribute to a better understanding of both the cellular alterations occurring in LGMDD2 patients and the role of TNPO3 in the HIV-1 cycle. To this regard, transcriptome profiling of PBMCs from LGMDD2 patients carrying the deletion c.2771delA in the TNPO3 gene was compared to healthy controls. A total of 545 differentially expressed genes were detected between LGMDD2 patients and healthy controls, with a high representation of G protein-coupled receptor binding chemokines and metallopeptidases among the most upregulated genes in LGMDD2 patients. Plasma levels of IFN-β and IFN-γ were 4.7- and 2.7-fold higher in LGMDD2 patients, respectively. An increase of 2.3-fold in the expression of the interferon-stimulated gene MxA was observed in activated PBMCs from LGMDD2 patients after ex vivo HIV-1 pseudovirus infection. Thus, the analysis suggests a pro-inflammatory state in LGMDD2 patients also described for other muscular dystrophies, that is characterized by the alteration of IL-17 signaling pathway and the consequent increase of metallopeptidases activity and TNF response. In summary, the increase in interferons and inflammatory mediators suggests an antiviral environment and resistance to HIV-1 infection but that could also impair muscular function in LGMDD2 patients, worsening disease evolution. Biomarkers of disease progression and therapeutic strategies based on these genes and mechanisms should be further investigated for this type of muscular dystrophy.

Dissection of molecular and histological subtypes of papillary thyroid cancer using alternative splicing profiles

Despite growing evidence of the relevance of alternative splicing (AS) to cancer development and progression, the biological implications of AS for tumor behaviors, including papillary thyroid cancer (PTC), remain elusive. With the aim of further understanding the molecular and histological subtypes of PTC, we in this study explored whether AS events might act as new molecular determinants. For this purpose, AS profiles were analyzed in RNA-sequencing data from The Cancer Genome Atlas (TCGA) and from a Korean patient dataset. A total of 23 distinct exon-skipping (ES) events that correlated significantly with PTC oncogenic activity and differentiation scores were identified. The two top-ranked ES events, NUMA1_17515 in exon 18 of NUMA1 and TUBB3_38175 in exon 6 of TUBB3, showed high correlations with oncogenic activities and discriminated histological and molecular subtypes of PTC. Furthermore, two novel intron-retention (IR) events for TUBB3 were uncovered. All ES and IR events for the TUBB3 gene were predicted to induce nonsense-mediated mRNA decay. The relative abundances of intron reads in the PTC dataset from TCGA showed IR levels to differ significantly among PTC subtypes, possibly reflecting their different tumor behaviors. This study provides a landscape of AS changes among PTC subtypes and identified two significant AS events, NUMA1_17515 and TUBB3_38175, as potential AS biomarkers for PTC subclassification and characterization. The AS events identified in this study may be involved in the development of phenotypic differences underlying the functional characteristics and histological differentiation of PTCs.

Two potential biomarkers uncovered by scientists in South Korea may help more accurately classify subtypes of papillary thyroid cancer, the most common form of thyroid cancer, and improve treatment regimens. Ascertaining the correct papillary thyroid cancer (PTC) subtype is important for patient prognoses and treatment plans. Growing evidence suggests that cancer progression may be influenced by ‘alternative splicing’ events, alterations to mRNA that change the structure of mRNA transcripts and affect the function of encoded proteins. Yeun-Jun Chung and Sug Hyung Lee at the Catholic University of Korea, Seoul, and co-workers explored alternative splicing events in PTC patient samples. They identified 25 distinct events associated with oncogenic activity and differentiation between PTC subtypes. Of these, two events associated with two separate genes are particularly significant and could prove useful as biomarkers for disease classification and characterisation.

Measuring and interpreting pervasive heterogeneity, poikilosis

Measurements are widely used in science, engineering, industry, and trade. They form the basis for experimental scientific research, approach, and progress; however, their foundations are seldom thought or questioned. Recently poikilosis, pervasive heterogeneity ranging from subatomic level to biosphere, was introduced. Poikilosis makes single point measurements and estimates obsolete and irrelevant as measurands display intervals of magnitudes. Consideration of poikilosis requires new lines of thinking in experimental design, conduction of studies, data analysis and interpretation. Measurements of poikilosis must consider lagom, normal, variation extent. Measurements, measures, and measurands as well as the measuring systems and uncertainties are discussed from the perspective of poikilosis. New systematics is introduced for description of uncertainty in measurements and for types of experimental designs. Poikilosis-aware experimenting, data analysis and interpretation are discussed. Instructions are provided for how to measure lagom and non-lagom effects of poikilosis. Consideration of poikilosis can solve scientific controversies and enigmas and can allow novel insight into systems, processes, mechanisms, and reactions and their interpretation, understanding, and manipulation. Furthermore, it will increase reproducibility of measurements and studies.

Identification of prognostic alternative splicing events in sarcoma

Sarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.

A machine learning-based framework for modeling transcription elongation

RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gene expression regulation. Characterizing the transcriptional elongation dynamics can thus help us understand many essential biological processes in eukaryotic cells. However, experimentally measuring Pol II elongation rates is generally time and resource consuming. We developed PEPMAN (polymerase II elongation pausing modeling through attention-based deep neural network), a deep learning-based model that accurately predicts Pol II pausing sites based on the native elongating transcript sequencing (NET-seq) data. Through fully taking advantage of the attention mechanism, PEPMAN is able to decipher important sequence features underlying Pol II pausing. More importantly, we demonstrated that the analyses of the PEPMAN-predicted results around various types of alternative splicing sites can provide useful clues into understanding the cotranscriptional splicing events. In addition, associating the PEPMAN prediction results with different epigenetic features can help reveal important factors related to the transcription elongation process. All these results demonstrated that PEPMAN can provide a useful and effective tool for modeling transcription elongation and understanding the related biological factors from available high-throughput sequencing data.

Systematics for types and effects of RNA variations

Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.

LncAS2Cancer: a comprehensive database for alternative splicing of lncRNAs across human cancers

Accumulating studies demonstrated that the roles of lncRNAs for tumorigenesis were isoform-dependent and their aberrant splicing patterns in cancers contributed to function specificity. However, there is no existing database focusing on cancer-related alternative splicing of lncRNAs. Here, we developed a comprehensive database called LncAS2Cancer, which collected 5335 bulk RNA sequencing and 1826 single-cell RNA sequencing samples, covering over 30 cancer types. By applying six state-of-the-art splicing algorithms, 50 859 alternative splicing events for 8 splicing types were identified and deposited in the database. In addition, the database contained the following information: (i) splicing patterns of lncRNAs under seven different conditions, such as gene interference, which facilitated to infer potential regulators; (ii) annotation information derived from eight sources and manual curation, to understand the functional impact of affected sequences; (iii) survival analysis to explore potential biomarkers; as well as (iv) a suite of tools to browse, search, visualize and download interesting information. LncAS2Cancer could not only confirm the known cancer-associated lncRNA isoforms but also indicate novel ones. Using the data deposited in LncAS2Cancer, we compared gene model and transcript overlap between lncRNAs and protein-coding genes and discusses how these factors, along with sequencing depth, affected the interpretation of splicing signals. Based on recurrent signals and potential confounders, we proposed a reliable score to prioritize splicing events for further elucidation. Together, with the broad collection of lncRNA splicing patterns and annotation, LncAS2Cancer will provide important new insights into the diverse functional roles of lncRNA isoforms in human cancers. LncAS2Cancer is freely available at https://lncrna2as.cd120.com/.

CancerSplicingQTL: a database for genome-wide identification of splicing QTLs in human cancer

Alternative splicing (AS) is a widespread process that increases structural transcript variation and proteome diversity. Aberrant splicing patterns are frequently observed in cancer initiation, progress, prognosis and therapy. Increasing evidence has demonstrated that AS events could undergo modulation by genetic variants. The identification of splicing quantitative trait loci (sQTLs), genetic variants that affect AS events, might represent an important step toward fully understanding the contribution of genetic variants in disease development. However, no database has yet been developed to systematically analyze sQTLs across multiple cancer types. Using genotype data from The Cancer Genome Atlas and corresponding AS values calculated by TCGASpliceSeq, we developed a computational pipeline to identify sQTLs from 9 026 tumor samples in 33 cancer types. We totally identified 4 599 598 sQTLs across all cancer types. We further performed survival analyses and identified 17 072 sQTLs associated with patient overall survival times. Furthermore, using genome-wide association study (GWAS) catalog data, we identified 1 180 132 sQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerSplicingQTL (http://www.cancersplicingqtl-hust.com/) for users to conveniently browse, search and download data of interest. This database provides an informative sQTL resource for further characterizing the potential functional roles of SNPs that control transcript isoforms in human cancer.

The Emerging Role of the RBM20 and PTBP1 Ribonucleoproteins in Heart Development and Cardiovascular Diseases

Alternative splicing is a regulatory mechanism essential for cell differentiation and tissue organization. More than 90% of human genes are regulated by alternative splicing events, which participate in cell fate determination. The general mechanisms of splicing events are well known, whereas only recently have deep-sequencing, high throughput analyses and animal models provided novel information on the network of functionally coordinated, tissue-specific, alternatively spliced exons. Heart development and cardiac tissue differentiation require thoroughly regulated splicing events. The ribonucleoprotein RBM20 is a key regulator of the alternative splicing events required for functional and structural heart properties, such as the expression of TTN isoforms. Recently, the polypyrimidine tract-binding protein PTBP1 has been demonstrated to participate with RBM20 in regulating splicing events. In this review, we summarize the updated knowledge relative to RBM20 and PTBP1 structure and molecular function; their role in alternative splicing mechanisms involved in the heart development and function; RBM20 mutations associated with idiopathic dilated cardiovascular disease (DCM); and the consequences of RBM20-altered expression or dysfunction. Furthermore, we discuss the possible application of targeting RBM20 in new approaches in heart therapies.

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.

ASlive: a database for alternative splicing atlas in livestock animals

Background

Alternative splicing is an important biological process whose precision must be tightly regulated during growth and development. Although there are species, disease (e.g. cancers), or study specific databases available in many organisms, no database exists in livestock animals specifically tailored for alternative splicing.

Description

We present in this study the development and implementation of a database for alternative splicing atlas in livestock animals (ASlive.org). Using publicly available RNASeq data sets across many tissues, cell types, and biological conditions totaling 28.6 T bases, we built a database of alternative splicing events in five major livestock and poultry animal species (cattle, sheep, pigs, horses, and chickens). The database contains many types of information on alternative splicing events, including basic information such as genomic locations, genes, and event types, quantitative measurements of alternative splicing in the form of percent spliced in (PSI), overlap with known DNA variants, as well as orthologous events across different lineage groups.

Conclusions

This database, the first of its kind in livestock animals, will provide a useful exploratory tool to assist functional annotation of animal genomes.

SRSF3-Regulated RNA Alternative Splicing Promotes Glioblastoma Tumorigenicity by Affecting Multiple Cellular Processes

Misregulated alternative RNA splicing (AS) contributes to the tumorigenesis and progression of human cancers, including glioblastoma (GBM). Here, we showed that a major splicing factor, serine and arginine rich splicing factor 3 (SRSF3), was frequently upregulated in clinical glioma specimens and that elevated SRSF3 was associated with tumor progression and a poor prognosis for patients with glioma. In patient-derived glioma stem-like cells (GSC), SRSF3 expression promoted cell proliferation, self-renewal, and tumorigenesis. Transcriptomic profiling identified more than 1,000 SRSF3-affected AS events, with a preference for exon skipping in genes involved with cell mitosis. Motif analysis identified the sequence of CA(G/C/A)CC(C/A) as a potential exonic splicing enhancer for these SRSF3-regulated exons. To evaluate the biological impact of SRSF3-affected AS events, four candidates were selected whose AS correlated with SRSF3 expression in glioma tissues, and their splicing pattern was modified using a CRISPR/Cas9 approach. Two functionally validated AS candidates were further investigated for the mechanisms underlying their isoform-specific functions. Specifically, following knockout of SRSF3, transcription factor ETS variant 1 (ETV1) gene showed exon skipping at exon 7, while nudE neurodevelopment protein 1 (NDE1) gene showed replacement of terminal exon 9 with a mutually exclusive exon 9'. SRSF3-regulated AS of these two genes markedly increased their oncogenic activity in GSCs. Taken together, our data demonstrate that SRSF3 is a key regulator of AS in GBM and that understanding mechanisms of misregulated AS could provide critical insights for developing effective therapeutic strategies against GBMs. SIGNIFICANCE: SRSF3 is a significant regulator of glioma-associated alternative splicing, implicating SRSF3 as an oncogenic factor that contributes to the tumor biology of GBM.

Sequence and Evolutionary Features for the Alternatively Spliced Exons of Eukaryotic Genes

Alternative splicing of pre-mRNAs is a crucial mechanism for maintaining protein diversity in eukaryotes without requiring a considerable increase of genes in the number. Due to rapid advances in high-throughput sequencing technologies and computational algorithms, it is anticipated that alternative splicing events will be more intensively studied to address different kinds of biological questions. The occurrences of alternative splicing mean that all exons could be classified to be either constitutively or alternatively spliced depending on whether they are virtually included into all mature mRNAs. From an evolutionary point of view, therefore, the alternatively spliced exons would have been associated with distinctive biological characteristics in comparison with constitutively spliced exons. In this paper, we first outline the representative types of alternative splicing events and exon classification, and then review sequence and evolutionary features for the alternatively spliced exons. The main purpose is to facilitate understanding of the biological implications of alternative splicing in eukaryotes. This knowledge is also helpful to establish computational approaches for predicting the splicing pattern of exons.

When one becomes many-Alternative splicing in β-cell function and failure

Pancreatic β-cell dysfunction and death are determinant events in type 1 diabetes (T1D), but the molecular mechanisms behind β-cell fate remain poorly understood. Alternative splicing is a post-transcriptional mechanism by which a single gene generates different mRNA and protein isoforms, expanding the transcriptome complexity and enhancing protein diversity. Neuron-specific and certain serine/arginine-rich RNA binding proteins (RBP) are enriched in β-cells, playing crucial roles in the regulation of insulin secretion and β-cell survival. Moreover, alternative exon networks, regulated by inflammation or diabetes susceptibility genes, control key pathways and processes for the correct function and survival of β-cells. The challenge ahead of us is to understand the precise role of alternative splicing regulators and splice variants on β-cell function, dysfunction and death and develop tools to modulate it.

Systematic evaluation of isoform function in literature reports of alternative splicing

BACKGROUND

Although most genes in mammalian genomes have multiple isoforms, an ongoing debate is whether these isoforms are all functional as well as the extent to which they increase the functional repertoire of the genome. To ground this debate in data, it would be helpful to have a corpus of experimentally-verified cases of genes which have functionally distinct splice isoforms (FDSIs).

RESULTS

We established a curation framework for evaluating experimental evidence of FDSIs, and analyzed over 700 human and mouse genes, strongly biased towards genes that are prominent in the alternative splicing literature. Despite this bias, we found experimental evidence meeting the classical definition for functionally distinct isoforms for ~ 5% of the curated genes. If we relax our criteria for inclusion to include weaker forms of evidence, the fraction of genes with evidence of FDSIs remains low (~ 13%). We provide evidence that this picture will not change substantially with further curation and conclude there is a large gap between the presumed impact of splicing on gene function and the experimental evidence. Furthermore, many functionally distinct isoforms were not traceable to a specific isoform in Ensembl, a database that forms the basis for much computational research.

CONCLUSIONS

We conclude that the claim that alternative splicing vastly increases the functional repertoire of the genome is an extrapolation from a limited number of empirically supported cases. We also conclude that more work is needed to integrate experimental evidence and genome annotation databases. Our work should help shape research around the role of splicing on gene function from presuming large general effects to acknowledging the need for stronger experimental evidence.