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LeafCutter: annotation-free quantification of RNA splicing

View ORCID ProfileYang I Li, View ORCID ProfileDavid A Knowles, View ORCID ProfileJack Humphrey, Alvaro N. Barbeira, Scott P. Dickinson, Hae Kyung Im, View ORCID ProfileJonathan K Pritchard
doi: https://doi.org/10.1101/044107
Yang I Li
1Department of Genetics, Stanford University, Stanford, CA
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David A Knowles
1Department of Genetics, Stanford University, Stanford, CA
2Department of Computer Science, Stanford University, Stanford, CA
3Department of Radiology, Stanford University, Stanford, CA
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Jack Humphrey
4University College London Genetics Institute, London, UK
5Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
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Alvaro N. Barbeira
6Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
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Scott P. Dickinson
6Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
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Hae Kyung Im
6Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA
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Jonathan K Pritchard
1Department of Genetics, Stanford University, Stanford, CA
7Department of Biology, Stanford University, Stanford, CA
8Howard Hughes Medical Institute, Stanford University, CA.
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Abstract

The excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable intron splicing events from short-read RNA-seq data and finds alternative splicing events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs). Compared to contemporary methods, we find 1.4–2.1 times more sQTLs, many of which help us ascribe molecular effects to disease-associated variants. Strikingly, transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at 5% FDR by an average of 2.1-fold as compared to using gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available at https://github.com/davidaknowles/leafcutter.

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Posted September 07, 2017.
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LeafCutter: annotation-free quantification of RNA splicing
Yang I Li, David A Knowles, Jack Humphrey, Alvaro N. Barbeira, Scott P. Dickinson, Hae Kyung Im, Jonathan K Pritchard
bioRxiv 044107; doi: https://doi.org/10.1101/044107
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LeafCutter: annotation-free quantification of RNA splicing
Yang I Li, David A Knowles, Jack Humphrey, Alvaro N. Barbeira, Scott P. Dickinson, Hae Kyung Im, Jonathan K Pritchard
bioRxiv 044107; doi: https://doi.org/10.1101/044107

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