SplicingFactory – Splicing diversity analysis for transcriptome data

Szikora, Peter; Pór, Tamás; Sebestyén, Endre

doi:10.1101/2021.02.03.429568

Cited by 3 publications

(4 citation statements)

References 45 publications

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“…Additionally, components of the spliceosome are differently expressed leading to differential splicing of target genes in A. thaliana during stress conditions 60 . In animals, stressed conditions are reported to have increased amount of alternatively spliced isoforms with high non-specific expression, thus creating a higher level of disorderdness in isoform expression 46 which can be quantified using Shannon entropy 73,74 . We predicted that plants will show a similar trend in increased disorderdness in isoform expression at the transcriptome level during stress conditions.…”

Section: Discussionmentioning

confidence: 99%

Alternative splicing preferentially increases transcript diversity associated with stress responses in the extremophyteSchrenkiella parvula

Wijesinghege

Tran

Dassanayake

2022

Preprint

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Alternative splicing extends the coding potential of genomes by creating multiple isoforms from one gene. Isoforms can render transcript specificity and diversity to initiate multiple responses required during transcriptome adjustments in stressed environments. Although the prevalence of alternative splicing is widely recognized, how diverse isoforms facilitate stress adaptation in plants that thrive in extreme environments are unexplored. Here we examine how an extremophyte model, Schrenkiella parvula, coordinates alternative splicing in response to high salinity compared to a salt-stress sensitive model, Arabidopsis thaliana. We use Iso-Seq to generate full length reference transcripts and RNA-seq to quantify differential isoform usage in response to salinity changes. We find that single-copy orthologs where S. parvula has a higher number of isoforms than A. thaliana as well as S. parvula genes observed and predicted using machine learning to have multiple isoforms are enriched in stress associated functions. Genes that showed differential isoform usage were largely mutually exclusive from genes that were differentially expressed in response to salt. S. parvula transcriptomes maintained specificity in isoform usage assessed via a measure of expression disorderdness during transcriptome reprogramming under salt. Our study adds a novel resource and insight to study plant stress tolerance evolved in extreme environments.

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Section: Discussionmentioning

confidence: 99%

Alternative splicing preferentially increases transcript diversity associated with stress responses in the extremophyteSchrenkiella parvula

Wijesinghege

Tran

Dassanayake

2022

Preprint

View full text Add to dashboard Cite

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“…Several software approaches have been developed for quantifying isoform diversity with Shannon entropy, including Cuffdiff (from Cufflinks) [137][138][139], Whippet [140], and SplicingFactory [141]. Cuffdiff uses Jensen-Shannon divergence, which, like Shannon entropy, relies on probability to compare the distribution of transcript expression across conditions [139].…”

Section: B Methods For Quantifying Isoform Diversitymentioning

confidence: 99%

“…Whippet [140] applies Shannon entropy to define the entropy of individual AS events instead of at the gene level, meaning that each alternatively spliced exon is given a value based on PSI. SplicingFactory [141] is unique because it also includes multiple other methods for assaying diversity across isoforms of the same gene, like the Gini Index (originally developed for describing wealth inequalities) and the Simpson Index (originally developed to measure ecological diversity). The aforementioned Tsallis entropy could also potentially be used in an isoform context to describe biological variability since it has been shown to provide more information than Shannon entropy or Simpson Index alone by including a parameter (q) that can be manipulated to adjust the weight of highly-expressed genes [83], though it has not yet been applied to study alternativelyspliced isoform distributions.…”

Section: B Methods For Quantifying Isoform Diversitymentioning

confidence: 99%

“…SpliZ [142] 2022 Single cell DEU (PSI) DTUrtle [143] 2021 Both DTU NanoCount [144] 2021 Bulk DTU SplicingFactory [141] 2021 Bulk Other -Diversity scisorseqr [145] 2021 Single cell DTU (modified) ASCOT [113] 2020 Single cell DEU (PSI) BANDITS [146] 2020 Bulk DTU Sierra [147] 2020 Single cell DTU RATs [148] 2019 Bulk DTU SUPPA2 [149] 2018 Bulk DEU (PSI)…”

Section: Package Name Year Bulk or Single Cell Differential Analysis ...mentioning

confidence: 99%

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Quantifying Variability in Gene Expression Profiles using Transcriptome and Isoform Diversity

Jones¹,

Haldar²,

Oza³

et al. 2023

Preprint

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Following the central dogma of molecular biology, gene expression variability can aid in predicting and explaining the wide variety of protein products, functions, and, ultimately, variability in phenotypes. There is currently overlapping terminology used to describe the types of variability in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptional diversity as quantifying transcriptional changes as a measure of the variability in 1) the total expression of all genes or a gene across samples (transcriptome diversity) or 2) the isoform-specific expression of a given gene (isoform diversity). We first overview modulators and quantification of gene expression variability. Then, we discuss the role alternative splicing plays in driving transcript isoform expression variability and how isoform diversity can be quantified. Additionally, we overview computational resources for calculating transcriptome and isoform diversity for short- and long-read sequencing data. Finally, we discuss future applications of transcriptional diversity. This review provides a comprehensive overview of how gene expression variability arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms, and species

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Recent Applications of RNA Sequencing in Food and Agriculture

Sripathi¹,

Anche²,

Gossett³

et al. 2021

Applications of RNA-Seq in Biology and Medicine

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RNA sequencing (RNA-Seq) is the leading, routine, high-throughput, and cost-effective next-generation sequencing (NGS) approach for mapping and quantifying transcriptomes, and determining the transcriptional structure. The transcriptome is a complete collection of transcripts found in a cell or tissue or organism at a given time point or specific developmental or environmental or physiological condition. The emergence and evolution of RNA-Seq chemistries have changed the landscape and the pace of transcriptome research in life sciences over a decade. This chapter introduces RNA-Seq and surveys its recent food and agriculture applications, ranging from differential gene expression, variants calling and detection, allele-specific expression, alternative splicing, alternative polyadenylation site usage, microRNA profiling, circular RNAs, single-cell RNA-Seq, metatranscriptomics, and systems biology. A few popular RNA-Seq databases and analysis tools are also presented for each application. We began to witness the broader impacts of RNA-Seq in addressing complex biological questions in food and agriculture.

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SplicingFactory – Splicing diversity analysis for transcriptome data

Cited by 3 publications

References 45 publications

Alternative splicing preferentially increases transcript diversity associated with stress responses in the extremophyteSchrenkiella parvula

Alternative splicing preferentially increases transcript diversity associated with stress responses in the extremophyteSchrenkiella parvula

Quantifying Variability in Gene Expression Profiles using Transcriptome and Isoform Diversity

Recent Applications of RNA Sequencing in Food and Agriculture

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