A survey of computational methods in transcriptome-wide alternative splicing analysis

Alternative RNA Splicing in the Retina: Insights and Perspectives

Alternative splicing is a fundamental and highly regulated post-transcriptional process that enhances transcriptome and proteome diversity. This process is particularly important in neuronal tissues, such as the retina, which exhibit some of the highest levels of differentially spliced genes in the body. Alternative splicing is regulated both temporally and spatially during neuronal development, can be cell-type-specific, and when altered can cause a number of pathologies, including retinal degeneration. Advancements in high-throughput sequencing technologies have facilitated investigations of the alternative splicing landscape of the retina in both healthy and disease states. Additionally, innovations in human stem cell engineering, specifically in the generation of 3D retinal organoids, which recapitulate many aspects of the in vivo retinal microenvironment, have aided studies of the role of alternative splicing in human retinal development and degeneration. Here we review these advances and discuss the ongoing development of strategies for the treatment of alternative splicing-related retinal disease.

Targeting alternative splicing in cancer immunotherapy

Tumor immunotherapy has made great progress in cancer treatment but still faces several challenges, such as a limited number of targetable antigens and varying responses among patients. Alternative splicing (AS) is an essential process for the maturation of nearly all mammalian mRNAs. Recent studies show that AS contributes to expanding cancer-specific antigens and modulating immunogenicity, making it a promising solution to the above challenges. The organoid technology preserves the individual immune microenvironment and reduces the time/economic costs of the experiment model, facilitating the development of splicing-based immunotherapy. Here, we summarize three critical roles of AS in immunotherapy: resources for generating neoantigens, targets for immune-therapeutic modulation, and biomarkers to guide immunotherapy options. Subsequently, we highlight the benefits of adopting organoids to develop AS-based immunotherapies. Finally, we discuss the current challenges in studying AS-based immunotherapy in terms of existing bioinformatics algorithms and biological technologies.

Transcriptomic analysis reveals Aspergillus oryzae responds to temperature stress by regulating sugar metabolism and lipid metabolism

Aspergillus oryzae is widely used in industrial applications, which always encounter changes within multiple environmental conditions during fermentation, such as temperature stress. However, the molecular mechanisms by which A. oryzae protects against temperature stress have not been elucidated. Therefore, this study aimed to characterize the fermentative behavior, transcriptomic profiles, and metabolic changes of A. oryzae in response to temperature stress. Both low and high temperatures inhibited mycelial growth and conidial formation of A. oryzae. Transcriptomic analysis revealed that most differentially expressed genes (DEGs) were involved in sugar metabolism and lipid metabolism under temperature stress. Specifically, the DEGs in trehalose synthesis and starch metabolism were upregulated under low-temperature stress, while high temperatures inhibited the expression of genes involved in fructose, galactose, and glucose metabolism. Quantitative analysis of intracellular sugar further revealed that low temperature increased trehalose accumulation, while high temperature increased the contents of intracellular trehalose, galactose, and glucose, consistent with transcriptome analysis. In addition, most DEGs involved in lipid metabolism were significantly downregulated under low-temperature stress. Furthermore, the metabolomic analysis revealed that linoleic acid, triacylglycerol, phosphatidylethanolamine, and phosphoribosyl were significantly decreased in response to low-temperature stress. These results increase our understanding of the coping mechanisms of A. oryzae in response to temperature stress, which lays the foundation for future improvements through genetic modification to enhance A. oryzae against extreme temperature stress.

Maternal methionine supplementation during gestation alters alternative splicing and DNA methylation in bovine skeletal muscle

Background

The evaluation of alternative splicing, including differential isoform expression and differential exon usage, can provide some insights on the transcriptional changes that occur in response to environmental perturbations. Maternal nutrition is considered a major intrauterine regulator of fetal developmental programming. The objective of this study was to assess potential changes in splicing events in the longissimus dorsi muscle of beef calves gestated under control or methionine-rich diets. RNA sequencing and whole-genome bisulfite sequencing were used to evaluate muscle transcriptome and methylome, respectively.

Results

Alternative splicing patterns were significantly altered by maternal methionine supplementation. Most of the altered genes were directly implicated in muscle development, muscle physiology, ATP activities, RNA splicing and DNA methylation, among other functions. Interestingly, there was a significant association between DNA methylation and differential exon usage. Indeed, among the set of genes that showed differential exon usage, significant differences in methylation level were detected between significant and non-significant exons, and between contiguous and non-contiguous introns to significant exons.

Conclusions

Overall, our findings provide evidence that a prenatal diet rich in methyl donors can significantly alter the offspring transcriptome, including changes in isoform expression and exon usage, and some of these changes are mediated by changes in DNA methylation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08065-4.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Computing the Role of Alternative Splicing in Cancer

Most human genes undergo alternative splicing (AS), and dysregulation of alternative splicing contributes to tumor initiation and progression. Computational analysis of genomic and transcriptomic data enables the systematic characterization of alternative splicing and its functional role in cancer. In this review, we summarize the latest computational approaches to studying alternative splicing in cancer and the current limitations of the most popular tools in this field. Finally, we describe some of the current computational challenges in the characterization of the role of alternative splicing in cancer.

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.

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.

Visualization and analysis of RNA-Seq assembly graphs

RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript diversity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce an RNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies

Over the last few years, RNA-seq has been used to study alterations in alternative splicing related to several diseases. Bioinformatics workflows used to perform these studies can be divided into two groups, those finding changes in the absolute isoform expression and those studying differential splicing. Many computational methods for transcriptomics analysis have been developed, evaluated and compared; however, there are not enough reports of systematic and objective assessment of processing pipelines as a whole. Moreover, comparative studies have been performed considering separately the changes in absolute or relative isoform expression levels. Consequently, no consensus exists about the best practices and appropriate workflows to analyse alternative and differential splicing. To assist the adequate pipeline choice, we present here a benchmarking of nine commonly used workflows to detect differential isoform expression and splicing. We evaluated the workflows performance over different experimental scenarios where changes in absolute and relative isoform expression occurred simultaneously. In addition, the effect of the number of isoforms per gene, and the magnitude of the expression change over pipeline performances were also evaluated. Our results suggest that workflow performance is influenced by the number of replicates per condition and the conditions heterogeneity. In general, workflows based on DESeq2, DEXSeq, Limma and NOISeq performed well over a wide range of transcriptomics experiments. In particular, we suggest the use of workflows based on Limma when high precision is required, and DESeq2 and DEXseq pipelines to prioritize sensitivity. When several replicates per condition are available, NOISeq and Limma pipelines are indicated.

Haplosaurus computes protein haplotypes for use in precision drug design

Selecting the most appropriate protein sequences is critical for precision drug design. Here we describe Haplosaurus, a bioinformatic tool for computation of protein haplotypes. Haplosaurus computes protein haplotypes from pre-existing chromosomally-phased genomic variation data. Integration into the Ensembl resource provides rapid and detailed protein haplotypes retrieval. Using Haplosaurus, we build a database of unique protein haplotypes from the 1000 Genomes dataset reflecting real-world protein sequence variability and their prevalence. For one in seven genes, their most common protein haplotype differs from the reference sequence and a similar number differs on their most common haplotype between human populations. Three case studies show how knowledge of the range of commonly encountered protein forms predicted in populations leads to insights into therapeutic efficacy. Haplosaurus and its associated database is expected to find broad applications in many disciplines using protein sequences and particularly impactful for therapeutics design.

Transcriptome analysis of different growth stages of Aspergillus oryzae reveals dynamic changes of distinct classes of genes during growth

Background

The gene expression profile and metabolic pathways of Aspergillus oryzae underlying the anatomical and morphological differentiation across different growth stages have not been fully characterized. The rapid development of next-generation sequencing technologies provides advanced knowledge of the genomic organization of A. oryzae.

Results

In this study, we characterized the growth and development of A. oryzae at different growth stages, including the adaptive phase, logarithmic phase, and stationary phase. Our results revealed that A. oryzae undergoes physiological and morphological differentiation across the different stages. RNA-seq was employed to analyze the three stages of A. oryzae, which generated more than 27 million high-quality reads per sample. The analysis of differential gene expression showed more genes expressed differentially upon transition from the adaptive phase to the logarithmic and stationary phases, while relatively steady trend was observed during the transition from the logarithmic phase to the stationary phase. GO classification of the differentially expressed genes among different growth stages revealed that most of these genes were enriched for single-organism process, metabolic process, and catalytic activity. These genes were then subjected to a clustering analysis. The results showed that the cluster with the majority of genes with increased expression upon transition from the adaptive phase to the logarithmic phase, and steady expression from the logarithmic phase to the stationary phase was mainly involved in the carbohydrate and amino acid metabolism.

Conclusion

Our results provide a foundation for identifying developmentally important genes and understanding the biological processes across various growth stages.

Electronic supplementary material

The online version of this article (10.1186/s12866-018-1158-z) contains supplementary material, which is available to authorized users.

Multiplex PCR and NGS-based identification of mRNA splicing variants: Analysis of BRCA1 splicing pattern as a model

Alternative pre-mRNA splicing increases transcriptome plasticity by forming naturally-occurring alternative splicing variants (ASVs). Alterations of splicing processes, caused by DNA mutations, result in aberrant splicing and the formation of aberrant mRNA isoforms. Analyses of hereditary cancer predisposition genes reveal many DNA variants with unknown clinical significance (VUS) that potentially affect pre-mRNA splicing. Therefore, a comprehensive description of ASVs is an essential prerequisite for the interpretation of germline VUS in high-risk individuals. To identify ASVs in a gene of interest, we have proposed an approach based on multiplex PCR (mPCR) amplification of all theoretically possible exon-exon junctions and subsequent characterization of size-selected and pooled mPCR products by next-generation sequencing (NGS). The efficiency of this method is illustrated by a comprehensive analysis of BRCA1 ASVs in human leukocytes, normal mammary, and adipose tissues and stable cell lines. We revealed 94 BRCA1 ASVs, including 29 variants present in all tested samples. While differences in the qualitative expression of BRCA1 ASVs among the analyzed human tissues were minor, larger differences were detected between tissue and cell line samples. Compared with other ASV analysis methods, this approach represents a highly sensitive and rapid alternative for the identification of ASVs in any gene of interest.

Using Synthetic Mouse Spike-In Transcripts to Evaluate RNA-Seq Analysis Tools

One of the key applications of next-generation sequencing (NGS) technologies is RNA-Seq for transcriptome genome-wide analysis. Although multiple studies have evaluated and benchmarked RNA-Seq tools dedicated to gene level analysis, few studies have assessed their effectiveness on the transcript-isoform level. Alternative splicing is a naturally occurring phenomenon in eukaryotes, significantly increasing the biodiversity of proteins that can be encoded by the genome. The aim of this study was to assess and compare the ability of the bioinformatics approaches and tools to assemble, quantify and detect differentially expressed transcripts using RNA-Seq data, in a controlled experiment. To this end, in vitro synthesized mouse spike-in control transcripts were added to the total RNA of differentiating mouse embryonic bodies, and their expression patterns were measured. This novel approach was used to assess the accuracy of the tools, as established by comparing the observed results versus the results expected of the mouse controlled spiked-in transcripts. We found that detection of differential expression at the gene level is adequate, yet on the transcript-isoform level, all tools tested lacked accuracy and precision.