Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences

Rosenberg, Alexander; Patwardhan, Rupali P; Shendure, Jay; Seelig, Georg

doi:10.1016/j.cell.2015.09.054

Cited by 252 publications

(334 citation statements)

References 46 publications

Supporting

Mentioning

313

Contrasting

Unclassified

Order By: Relevance

“…′ splice sites, which has been previously shown to affect splicing efficiency (Rosenberg et al 2015). These distances were greater in lincRNAs than in mRNAs, as gauged both by in silico mapping the canonical branch motif ( Fig.…”

Section: Wwwgenomeorgmentioning

confidence: 81%

Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs

et al. 2016

View full text Add to dashboard Cite

While long intergenic noncoding RNAs (lincRNAs) and mRNAs share similar biogenesis pathways, these transcript classes differ in many regards. LincRNAs are less evolutionarily conserved, less abundant, and more tissue-specific, suggesting that their pre-and post-transcriptional regulation is different from that of mRNAs. Here, we perform an in-depth characterization of the features that contribute to lincRNA regulation in multiple human cell lines. We find that lincRNA promoters are depleted of transcription factor (TF) binding sites, yet enriched for some specific factors such as GATA and FOS relative to mRNA promoters. Surprisingly, we find that H3K9me3-a histone modification typically associated with transcriptional repression-is more enriched at the promoters of active lincRNA loci than at those of active mRNAs. Moreover, H3K9me3-marked lincRNA genes are more tissue-specific. The most discriminant differences between lincRNAs and mRNAs involve splicing. LincRNAs are less efficiently spliced, which cannot be explained by differences in U1 binding or the density of exonic splicing enhancers but may be partially attributed to lower U2AF65 binding and weaker splicing-related motifs. Conversely, the stability of lincRNAs and mRNAs is similar, differing only with regard to the location of stabilizing protein binding sites. Finally, we find that certain transcriptional properties are correlated with higher evolutionary conservation in both DNA and RNA motifs and are enriched in lincRNAs that have been functionally characterized.

show abstract

Section: Wwwgenomeorgmentioning

confidence: 81%

Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs

et al. 2016

View full text Add to dashboard Cite

show abstract

“…MAVEs for splicing have already been implemented for both direct genome editing 15 and minigene assays. 14,47,48 MAVEs have also been developed to probe the effects of variants in untranslated regions of mRNAs on message stability and protein expression. [16][17][18] Variation in transcriptional regulatory elements is also important, and pathogenic regulatory variants have been identified for a number of Mendelian disorders.…”

Section: Annotating Every Possible Variant In Disease-related Functiomentioning

confidence: 99%

“…For example, massively parallel reporter assays (MPRAs) query the effects of regulatory DNA variants on the expression of reporter genes, 13 whereas splicing assays reveal variant effects on mRNA processing. 14,15 The effect of variants on mRNA stability and translation can also be measured by multiplex assays. [16][17][18] Finally, deep mutational scans query the effect of amino acid substitutions on protein function.…”

Section: Introductionmentioning

confidence: 99%

Variant Interpretation: Functional Assays to the Rescue

Starita

Ahituv

Dunham

et al. 2017

The American Journal of Human Genetics

Self Cite

316

294

View full text Add to dashboard Cite

Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of ''lookup tables'' of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.

show abstract

“…In humans, genetic and biochemical studies show that exons are first recognized in a process called exon definition, and then introns between them are removed [7][8][9][10][11] . The major exon recognition elements, including the splice donor, acceptor, branchpoint and polypyrimidine tract, taken together are too degenerate alone to discriminate true exons from those not utilized in vivo [12][13][14] .…”

Section: Main Textmentioning

confidence: 99%

“…These sequences are short motifs that are broadly classified as exonic splicing enhancers (ESEs) and suppressors (ESSs) as well as their intronic counterparts 16 , 17 (ISEs & ISSs). Machine learning methods use these and other genomic features trained against genome-wide RNA sequencing datasets to build predictive models of splicing regulation [7][8][9] . However, the predictive power of these models may come almost entirely from sequence conservation rather than the mechanistic understanding of splicing 18,19 .…”

Section: Main Textmentioning

confidence: 99%

Many rare genetic variants have unrecognized large-effect disruptions to exon recognition

Cheung

Yao

et al. 2017

Preprint

View full text Add to dashboard Cite

Any individual’s genome contains ∼4-5 million genetic variants that differ from reference, and understanding how these variants give rise to trait diversity and disease susceptibility is a central goal of human genetics1. A vast majority (96-99%) of an individual’s variants are common, though at a population level the overwhelming majority of variants are rare2–5. Because of their scarcity in an individual’s genome, rare variants that play important roles in complex traits are likely to have large functional effects6,7. Mutations that cause an exon to be skipped can have severe functional consequences on gene function, and many known disease-causing mutations reduce or eliminate exon recognition8. Here we explore the extent to which rare genetic variation in humans results in near complete loss of exon recognition. We developed a Multiplexed Functional Assay of Splicing using Sort-seq (MFASS) that allows us to measure exon inclusion in thousands of human exons and surrounding intronic sequence simultaneously. We assayed 27,733 extant variants in the Exome Aggregation Consortium (ExAC)9 within or adjacent to 2,339 human exons, and found that 3.8% (1,050) of the variants, almost all of which were extremely rare, led to large-effect defects in exon recognition. Importantly, we find that 83% of these splice-disrupting variants (SDVs) are located outside of canonical splice sites, are distributed evenly across distinct exonic and intronic regions, and are difficult to predict a priori. Our results indicate that loss of exon recognition is an important and underappreciated means by which rare variants exert large functional effects, and that MFASS enables their empirical assessment for splicing defects at scale.

show abstract

Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences

Cited by 252 publications

References 46 publications

Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs

Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs

Variant Interpretation: Functional Assays to the Rescue

Many rare genetic variants have unrecognized large-effect disruptions to exon recognition

Contact Info

Product

Resources

About