Differential mRNA Alternative Splicing

Lahat, Albert; Grellscheid, Sushma Nagaraja

doi:10.1007/978-3-319-31350-4_5

Cited by 4 publications

(3 citation statements)

References 64 publications

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“…However, it can work with multiple conditions and includes the possibility to perform hierarchical clustering on differentially spliced events to identify common regulatory mechanisms. Other tools capable of detecting AS and compare AS patterns between sample groups are DEXSEq, SplicingCompass, Altanalyze, BitSeq, EBSeq, and Cuffdiff2 whose performances are extensively reviewed in Lahat and Grellsheid [158].…”

Section: Bioinformatic Tools To Predict the Effect Of Splicing Modulation Towards Mirna Profilesmentioning

confidence: 99%

Interrelationship between miRNA and splicing factors in pancreatic ductal adenocarcinoma

et al. 2021

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Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers because of diagnosis at late stage and inherent/acquired chemoresistance. Recent advances in genomic profiling and biology of this disease have not yet been translated to a relevant improvement in terms of disease management and patient's survival. However, new possibilities for treatment may emerge from studies on key epigenetic factors. Deregulation of microRNA (miRNA) dependent gene expression and mRNA splicing are epigenetic processes that modulate the protein repertoire at the transcriptional level. These processes affect all aspects of PDAC pathogenesis and have great potential to unravel new therapeutic targets and/or biomarkers. Remarkably, several studies showed that they actually interact with each other in influencing PDAC progression. Some splicing factors directly interact with specific miRNAs and either facilitate or inhibit their expression, such as Rbfox2, which cleaves the well-known oncogenic miRNA miR-21. Conversely, miR-15a-5p and miR-25-3p significantly downregulate the splicing factor hnRNPA1 which acts also as a tumour suppressor gene and is involved in processing of miR-18a, which in turn, is a negative regulator of KRAS expression. Therefore, this review describes the interaction between splicing and miRNA, as well as bioinformatic tools to explore the effect of splicing modulation towards miRNA profiles, in order to exploit this interplay for the development of innovative treatments. Targeting aberrant splicing and deregulated miRNA, alone or in combination, may hopefully provide novel therapeutic approaches to fight the complex biology and the common treatment recalcitrance of PDAC.

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Section: Bioinformatic Tools To Predict the Effect Of Splicing Modulation Towards Mirna Profilesmentioning

confidence: 99%

Interrelationship between miRNA and splicing factors in pancreatic ductal adenocarcinoma

et al. 2021

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“…Alignments within exons are represented as read densities and paired reads connect exons, where the connection is weighted relative to the number of reads crossing a splice junction. Numerous other tools focus more on the quantitative and comparative visualization of differentially expressed spliced exons and isoforms in different samples (reviewed in (23)). In general, the majority of existing visualization approaches for splice variant analysis commonly involve the ‘stacking’ of reads onto a genomic reference.…”

Section: Introductionmentioning

confidence: 99%

Visualization and analysis of RNA-Seq assembly graphs

Nazarie

Shih

Angus

et al. 2019

Nucleic Acids Research

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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.

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“…Alternative splicing is related to a change in the relative abundance of transcript isoforms produced from the same gene (Lee and Rio, 2015). Multiple approaches have been proposed to study differential splicing from RNA sequencing (RNA-seq) data (Alamancos et al, 2014;Lahat and Grellscheid, 2016). These methods generally involve the analysis of either transcript isoforms (Froussios et al, 2017;Nowicka and Robinson, 2016;Sebestyen et al, 2015;Trapnell et al, 2013), clusters of splice-junctions (Hu et al, 2013;Vaquero-Garcia et al, 2016), alternative splicing events (Katz et al, 2010;Shen et al, 2014) or exonic regions (Anders et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Trincado

Entizne

Hysenaj

et al. 2016

Preprint

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Despite the many approaches to study differential splicing from RNA-seq, many challenges remain unsolved, including computing capacity and sequencing depth requirements. Here we present SUPPA2, a new method for differential splicing analysis that addresses these challenges and enables streamlined analysis across multiple conditions taking into account biological variability. Using experimental and simulated data SUPPA2 achieves higher accuracy compared to other methods; especially at low sequencing depth and short read length, with important implications for cost-effective use of RNA-seq for splicing; and was able to identify novel Transformer2-regulated exons. We further analyzed two differentiation series to support the applicability of SUPPA2 beyond binary comparisons. This identified clusters of alternative splicing events enriched in microexons induced during differentiation of bipolar neurons, and a cluster enriched in intron retention events that are present at late stages during erythroblast differentiation. Our data suggest that SUPPA2 is a valuable tool for the robust investigation of the biological complexity of alternative splicing.

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Differential mRNA Alternative Splicing

Cited by 4 publications

References 64 publications

Interrelationship between miRNA and splicing factors in pancreatic ductal adenocarcinoma

Interrelationship between miRNA and splicing factors in pancreatic ductal adenocarcinoma

Visualization and analysis of RNA-Seq assembly graphs

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

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