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Annotation-free quantification of RNA splicing using LeafCutter
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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 splicing events from short-read RNA-seq data and finds 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 to detect differential splicing between sample groups and to map splicing quantitative trait loci (sQTLs). Compared with contemporary methods, our approach identified 1.4-2.1 times more sQTLs, many of which helped us ascribe molecular effects to disease-associated variants. Transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at a 5% false discovery rate by an average of 2.1-fold compared with that detected through the use of gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available online.
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“We used LeafCutter (29) to assess local differential splicing (DS) differences in ASD, SCZ and BD using de novo aligned RNA-seq reads, controlling for the same covariates as DGE/DTE (Fig S7).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [29]
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“Using de novo junctions increased the confidence of detection of differential splicing 41,42 , and our procedure took into account known and novel splice sites.”
From Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells (2018) · cites this paper as [42]
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Metadata
- Authors
- Li, Y. I., Knowles, D. A., Humphrey, J., Barbeira, A. N., Dickinson, S. P., Im, H. K., & Pritchard, J. K.
- Affiliations
- University of Chicago (US)
- Stanford Medicine (US)
- UCL Queen Square Institute of Neurology (GB)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Li, Yang I.
- Knowles, David A.
- Pritchard, Jonathan K.
- ORCID
- Venue
- Nature Genetics
- Published
- 2017-12-11
- Volume
- 50
- Issue
- 1
- Pages
- 151-158
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC5742080
- OpenAlex
- W2774883395
- Cited by
- 765
- References
- 45
- Citations by year
- 2026: 79 · 2025: 113 · 2024: 95 · 2023: 109 · 2022: 95 · 2021: 131 · 2020: 51 · 2019: 45 · 2018: 33 · 2017: 8
- FWCI
- 15.29
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Center for Computational, Evolutionary and Human Genomics, Stanford University
- National Institute of Mental Health (R01MH107666)
- National Human Genome Research Institute (U01HG009431, U01HG007036, R01HG008140)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK020595)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- Leafcutter, alternative splicing, intron splicing, splicing quantitative trait loci, differential splicing, transcriptome-wide association study, disease-associated variants, pre-mRNA processing
- MeSH terms
- Animals, Disease, Introns, Software, Genetic Variation, Alternative Splicing, Sequence Analysis, RNA, Gene Expression Profiling, Quantitative Trait Loci, Molecular Sequence Annotation
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