Cited by 792RNA Research and SplicingGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

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Annotation-free quantification of RNA splicing using LeafCutter

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Li, Yang I., Knowles, David A., Humphrey, Jack, Barbeira, Alvaro N., Dickinson, Scott P., Im, Hae Kyung, Pritchard, Jonathan K.·2018·Nature Genetics

DOI 10.1038/s41588-017-0004-9PMC PMC5742080

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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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Self-citation
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Self-citation

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: Li, Yang I.
Knowles, David A.
Humphrey, Jack
Barbeira, Alvaro N.
Im, Hae Kyung
Pritchard, Jonathan K.
Venue: Nature Genetics
Publisher: Springer Science and Business Media LLC
Published: 2017-12-11
Volume: 50
Issue: 1
Pages: 151-158
ISSN: 1061-4036 (ISSN-L)
1546-1718
DOI: 10.1038/s41588-017-0004-9
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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Annotation-free quantification of RNA splicing using LeafCutter

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