Systematic evaluation of differential splicing tools for RNA-seq studies

Mehmood, Arfa; Laiho, Asta; Venäläinen, Mikko S; McGlinchey, Aidan; Wang, Ning; Elo, Laura L.

doi:10.1093/bib/bbz126

Cited by 146 publications

(121 citation statements)

References 52 publications

(81 reference statements)

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“…1a-b ). Importantly, this depth falls within the recommended depth (~60 million reads) for robust and reproducible splicing and differential expression analyses (Mehmood et al, 2019; Shen et al, 2012).…”

Section: Resultsmentioning

confidence: 57%

“…Host splicing changes have been observed during infection with DNA viruses (HSV-1 (Hu et al, 2016), HCMV (Batra et al, 2016)) as well as RNA viruses such as reovirus (Boudreault et al, 2016), dengue virus (Sessions et al, 2013), and zika virus (Hu et al, 2017). However, these studies represent only the tip of the iceberg as they have mostly lacked the sequencing depth, and subsequent orthogonal validation, that is needed for a comprehensive quantitative analysis of host splicing (Mehmood et al, 2019; Shen et al, 2012)(Vaquero-Garcia et al, 2016). Therefore, we set out to comprehensively define the scope of host splicing changes during IAV infection in order to begin to address the underlying mechanisms and functional implications of such viral-induced alterations to the host transcriptome.…”

Section: Introductionmentioning

confidence: 99%

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Viral-Induced Alternative Splicing of Host Genes Promotes Influenza Replication

Thompson

Dittmar

Mallory

et al. 2020

Preprint

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Viral infection induces the expression of numerous host genes that impact the outcome of infection. Here we show that infection of human lung epithelial cells with Influenza A virus (IAV) also induces a broad program of alternative splicing of host genes. While these splicing-regulated genes are not enriched for canonical regulators of viral infection, we find that many of these genes do impact replication of IAV. Moreover, specific inhibition of the IAV-induced splicing in several cases also attenuates viral infection. We further show that approximately a quarter of the IAV-induced splicing events are regulated by hnRNP K, a host protein that required for efficient splicing of the IAV M transcript in nuclear speckles. Notably, we find that hnRNP K accumulates in nuclear speckles upon IAV infection, which is likely to alter the accessibility of hnRNP K for host transcripts thereby leading to a program of host splicing changes that promote IAV replication.

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Section: Resultsmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Viral-Induced Alternative Splicing of Host Genes Promotes Influenza Replication

Thompson

Dittmar

Mallory

et al. 2020

Preprint

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“…Compared to SplAdder, which is limited to the detection of simple types of splicing events, MAJIQ introduced a novel approach that additionally captures more complex transcript variations. MAJIQ was shown in a recent benchmark [31] to compare favorably to existing stateof-the-art methods and the authors demonstrated in [49] that MAJIQ also outperforms LeafCutter and rMATS.…”

Section: Resultsmentioning

confidence: 99%

McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data

Alqassem

Sonthalia

Klitzke-Feser

et al. 2020

Preprint

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Alternative splicing removes intronic sequences from transcripts in alternative ways to produce different forms (isoforms) of mature mRNA. The composition of expressed transcripts and their alternative forms give specific functionalities to cells in a particular condition or developmental stage. In addition, a large fraction of human disease mutations affect splicing and lead to aberrant mRNA and protein products. Current methods that interrogate the transcriptome based on RNA-seq either suffer from short read length when trying to infer full-length transcripts, or are restricted to predefined units of alternative splicing that they quantify from local read evidence. Instead of attempting to quantify individual outcomes of the splicing process such as local splicing events or full-length transcripts, we propose to quantify alternative splicing using a simplified probabilistic model of the underlying splicing process. Our model is based on the usage of individual splice sites and can generate arbitrarily complex types of splicing patterns. In our method, McSplicer, we estimate the parameters of our model using all read data at once and we demonstrate in our experiments that this yields more accurate estimates compared to competing methods. Our model is able to describe multiple effects of splicing mutations using few, easy to interpret parameters, as we illustrate in an experiment on RNA-seq data from autism spectrum disorder patients. McSplicer is implemented in Python and available as open-source at https://github.com/canzarlab/McSplicer.

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“…However, a previous study showed that the use of ten differential splicing analysis tools resulted in the identification of different numbers of DAS genes. The number of identified DAS genes ranged from 0 (cuffdiff2) to 4506 (edgeR) in a human prostate cancer dataset, with 2962 and 0 DAS genes identified using rMATS in human and mouse datasets, respectively [49]. In addition, 252, 171, and 42 DAS genes were identified in comparisons between control and intolerant, control and tolerant and intolerant and tolerant catfish following heat stress [50].…”

Section: Discussionmentioning

confidence: 99%

Differential Alternative Splicing Genes and Isoform Regulation Networks of Rapeseed (Brassica napus L.) Infected with Sclerotinia sclerotiorum

et al. 2020

Genes

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Alternative splicing (AS) is a post-transcriptional level of gene expression regulation that increases transcriptome and proteome diversity. How the AS landscape of rapeseed (Brassica napus L.) changes in response to the fungal pathogen Sclerotinia sclerotiorum is unknown. Here, we analyzed 18 RNA-seq libraries of mock-inoculated and S. sclerotiorum-inoculated susceptible and tolerant B. napus plants. We found that infection increased AS, with intron retention being the main AS event. To determine the key genes functioning in the AS response, we performed a differential AS (DAS) analysis. We identified 79 DAS genes, including those encoding splicing factors, defense response proteins, crucial transcription factors and enzymes. We generated coexpression networks based on the splicing isoforms, rather than the genes, to explore the genes’ diverse functions. Using this weighted gene coexpression network analysis alongside a gene ontology enrichment analysis, we identified 11 modules putatively involved in the pathogen defense response. Within these regulatory modules, six DAS genes (ascorbate peroxidase 1, ser/arg-rich protein 34a, unknown function 1138, nitrilase 2, v-atpase f, and amino acid transporter 1) were considered to encode key isoforms involved in the defense response. This study provides insight into the post-transcriptional response of B. napus to S. sclerotiorum infection.

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Systematic evaluation of differential splicing tools for RNA-seq studies

Cited by 146 publications

References 52 publications

Viral-Induced Alternative Splicing of Host Genes Promotes Influenza Replication

Viral-Induced Alternative Splicing of Host Genes Promotes Influenza Replication

McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data

Differential Alternative Splicing Genes and Isoform Regulation Networks of Rapeseed (Brassica napus L.) Infected with Sclerotinia sclerotiorum

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