The contribution of alternative splicing probability to the coding expansion of the genome

Abstract

Alternative splicing results in the inclusion or exclusion of exons in an RNA, thereby allowing a single gene to code for multiple RNA isoforms. Genes are often composed of many exons, allowing combinatorial choice to significantly expand the coding potential of the genome. How much coding potential is gained by alternative splicing and what is the main contributor: alternative-splicing-depth or exon-count? Here we develop a splice-site-centric quantification method, allowing us to characterize transcriptome-wide alternative splicing with a simple probabilistic model, enabling species-wide comparison. We use information theory to quantify the coding potential gain and show that an increase in alternative splicing probability contributes more to transcriptome expansion than exon-count. Our results suggest that dominant isoforms are co-expressed alongside many minor isoforms. We propose that this solves two problems simultaneously, that is, expression of functional isoforms and expansion of the transcriptome landscape potentially without a direct function, but available for evolution. GlossaryTranscriptome: Set of all RNA molecules in a sample (e.g. cell, tissue, organism). Transcriptome expansion: Increase of coding expansion of the genome. Gene annotation: Meta information added to the raw DNA sequence, such as exon-intron structure. Gene architecture: Exon-intron structure of genes. RNA Splicing: RNA maturation event leading to removal of introns and joining of exons. Intron: Sequence removed by splicing, often non-coding for proteins. Exon: Sequence retained by splicing, often coding. Splice site: Exon-intron (5' splice site) or intron-exon boundary (3' splice site). Constitutive splicing: The process that results in the joining of two splice-sites in all observed situation. Alternative splicing: The process that results that one splice site can be joined to distinct partner splice sites. RNA-seq experiment: Qualitative and quantitative profile of transcriptome by deep sequencing. Extent: A parameter used to characterize the amount of alternative splicing in any given transcriptome; technically, the extent = ! ! , where is the exponent in the power law distribution that describes the amount of alternative splicing in the transcriptome. Splice site expression: Number of RNA-Seq observations per splice site. Shannon Entropy: Metric of the expected information content. True Diversity: An ecological concept which measures both the number of distinct species (richness) and how uniformly they are distributed in a sample (evenness). Machine Learning: Computational algorithms which learn rules (model) to predict an output from an input. Random Forest: A non-linear machine learning model based on an ensemble of decision trees with random feature subset selection at each decision node. Lasso Regression: Linear regression regularized by absolute value of the sum of all regression coefficients (L1 norm). Bootstrapping: Resampling technique to infer the confidence in a population measurement. Probability density function (p...

Alternative splicing modulation by G-quadruplexes

“…In eukaryotes pre-mRNA processing is key to gene regulation and the generation of isoform diversity. Alternative splicing is arguably the most pivotal mRNA processing mechanism in higher eukaryotes, and in humans it contributes substantially to the protein diversity, affecting 95% of mRNA transcripts (Pan et al 2008), , (Licatalosi & Darnell 2010;Oesterreich et al 2016). Moreover, alternative splicing is essential for normal cell growth, cell death, differentiation, development, sex, circadian rhythms, responses to environmental changes and pathogen responses (Bell et al 1988), (Kalsotra & Cooper 2011), (Irimia & Blencowe 2012).…”

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

“…Compared to these simple types of splicing events, complex events involve multiple alternative splice sites or exons and according to Vaquero-Garcia et al (2016) constitute at least one-third of AS events observed in human and mouse tissues. Methods such as JUM ( Wang and Rio, 2018 ), MAJIQ ( Vaquero-Garcia et al , 2016 ) and the method proposed in Oesterreich et al (2016) , therefore consider AS units that generalize simple events to more complex patterns. They quantify the relative usage of an arbitrary number of introns that share a common splice site.…”

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

“…Complex events, on the other hand, involve multiple alternative splice sites or exons and according to [48] constitute at least one-third of AS events observed in human and mouse tissues. Methods such as JUM [52], MAJIQ [48] and the splice-site-centric quantification method proposed in [33], therefore consider AS units that generalize simple events to more complex patterns. They quantify the relative usage of an arbitrary number of introns that share a common splice site, such as the three introns sharing common donor site s 1 in Fig.…”