Annotation-free quantification of RNA splicing using LeafCutter

By Yang I Li, David A. Knowles, J. Humphrey, A. Barbeira, Scott P. Dickinson, Hae Kyung Im, J. K. Pritchard. Nature Genetics, 2017

647 citations recorded by the source corpus.

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. LeafCutter is a new tool that identifies variable intron splicing events from RNA-seq data for analysis of complex alternative splicing. The method does not require transcript annotation and can be used to map splicing quantitative trait loci.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d1c2faa14fb8b8091ad9cf3393711c44353aac7e

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Citations in the corpus

The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, methodology): “, 2013) and used the LeafCutter tool to identify splicing quantitative trait loci (sQTLs) (Li et al., 2018).”
Predicting Splicing from Primary Sequence with Deep Learning. (2019, methodology): “Detection of splice junctions We used leafcutter_cluster, a utility in the leafcutter package (Li et al., 2018), to detect and count splice junctions in each sample.”
The GTEx Consortium atlas of genetic regulatory effects across human tissues (2019, methodology): “We mapped splicing QTLs (sQTLs) in cis with intron excision ratios from LeafCutter (11, 13), and discovered 12,828 (66.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Li YI, Knowles DA, Humphrey J, Barbeira AN, Dickinson SP, Im HK, et al. Annotation-free quantification of RNA splicing using LeafCutter.”
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018, neutral): “Y. I. Li et al., Annotation-free quantification of RNA splicing using LeafCutter.”
Haplotype-resolved diverse human genomes and integrated analysis of structural variation (2021, methodology): “RNA-seq data QC was conducted with Trim Galore! (78) and mapped to the reference genome by using STAR (79), followed by gene-level quantification by using FeatureCounts (80) and quantification of splice events by using leafCutter (81).”