Annotation-free quantification of RNA splicing using LeafCutter

Nature Genetics

Volumn 50, Issue 1, 2018, Pages 151-158

  Li, Yang I ^a   Knowles, David A ^a,b,c   Humphrey, Jack ^d,e   Barbeira, Alvaro N ^f   Dickinson, Scott P ^f   Im, Hae Kyung ^f   Pritchard, Jonathan K ^a  

^a STANFORD UNIVERSITY   (United States)

^b Stanford University   (United States)

^c STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

^e UNIVERSITY COLLEGE LONDON   (United Kingdom)

^f UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

RNA ISOFORM; TRANSCRIPTOME;

ARTICLE; EXON; GENE CLUSTER; GENE EXPRESSION; GENETIC VARIABILITY; INTRON; METHODOLOGY; PRIORITY JOURNAL; QUANTITATIVE TRAIT LOCUS; QUANTITATIVE TRAIT LOCUS MAPPING; RNA ANALYSIS; RNA SEQUENCE; RNA SPLICING; SINGLE NUCLEOTIDE POLYMORPHISM; ALTERNATIVE RNA SPLICING; ANIMAL; DISEASES; GENE EXPRESSION PROFILING; GENETIC VARIATION; GENETICS; MOLECULAR GENETICS; PROCEDURES; SEQUENCE ANALYSIS; SOFTWARE;

ALTERNATIVE SPLICING; ANIMALS; DISEASE; GENE EXPRESSION PROFILING; GENETIC VARIATION; INTRONS; MOLECULAR SEQUENCE ANNOTATION; QUANTITATIVE TRAIT LOCI; SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 85037717006     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/s41588-017-0004-9     Document Type: Article

References (37)

1. Han, H. et al. MBNL proteins repress ES-cell-specific alternative splicing and reprogramming. Nature 498, 241-245 (2013)
2. Calarco, J. A. et al. Regulation of vertebrate nervous system alternative splicing and development by an SR-related protein. Cell 138, 898-910 (2009)
3. Brett, D., Pospisil, H., Valcarcel, J., Reich, J. &Bork, P. Alternative splicing and genome complexity. Nat. Genet. 30, 29-30 (2002)
4. Pai, A. A. et al. Widespread shortening of 3 untranslated regions and increased exon inclusion are evolutionarily conserved features of innate immune responses to infection. PLoS Genet. 12, e1006338 (2016)
5. Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46-53 (2013)
6. Leng, N. et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29, 1035-1043 (2013)
7. Patro, R., Mount, S. M. &Kingsford, C. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat. Biotechnol. 32, 462-464 (2014)
8. Bray, N., Pimentel, H., Melsted, P. &Pachter, L. Near-optimal RNA-Seq quantification. Preprint available at https://arxiv.org/abs/1505.02710 (2015)
9. Katz, Y., Wang, E. T., Airoldi, E. M. &Burge, C. B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009-1015 (2010)
10. Anders, S., Reyes, A. &Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008-2017 (2012)
11. Lacroix, V., Sammeth, M., Guigo, R. &Bergeron, A. Exact transcriptome reconstruction from short sequence reads. In Algorithms in Bioinformatics (eds. Crandall, K.A. &Lagergren, J) 50-63 (Springer, Berlin, Heidelberg, 2008)
12. Vaquero-Garcia, J. et al. A new view of transcriptome complexity and regulation through the lens of local splicing variations. eLife 5, e11752 (2016)
13. Stein, S., Lu, Z. X., Bahrami-Samani, E., Park, J. W. &Xing, Y. Discover hidden splicing variations by mapping personal transcriptomes to personal genomes. Nucleic Acids Res. 43, 10612-10622 (2015)
14. Zhao, K., Lu, Z. X., Park, J. W., Zhou, Q. &Xing, Y. GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data. Genome Biol. 14, R74 (2013)
15. Monlong, J., Calvo, M., Ferreira, P. G. &Guigo, R. Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nat. Commun. 5, 4698 (2014)
16. Ongen, H. &Dermitzakis, E. T. Alternative splicing QTLs in European and African populations. Am. J. Hum. Genet. 97, 567-575 (2015)
17. Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600-604 (2016)
18. Tilgner, H. et al. Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs. Genome Res. 22, 1616-1625 (2012)
19. Wu, J., Anczukow, O., Krainer, A. R., Zhang, M. Q. &Zhang, C. OLego: fast and sensitive mapping of spliced mRNA-Seq reads using small seeds. Nucleic Acids Res. 41, 5149-5163 (2013)
20. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648-660 (2015)
21. Soumillon, M. et al. Cellular source and mechanisms of high transcriptome complexity in the mammalian testis. Cell Rep. 3, 2179-2190 (2013)
22. Kaessmann, H. Origins, evolution, and phenotypic impact of new genes. Genome Res. 20, 1313-1326 (2010)
23. Nellore, A. et al. Human splicing diversity and the extent of unannotated splice junctions across human RNA-seq samples on the Sequence Read Archive. Genome Biol. 17, 266 (2016)
24. Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl. Acad. Sci. USA 111, E5593-E5601 (2014)
25. Merkin, J., Russell, C., Chen, P. &Burge, C. B. Evolutionary dynamics of gene and isoform regulation in mammalian tissues. Science 338, 1593-1599 (2012)
26. Barbosa-Morais, N. L. et al. The evolutionary landscape of alternative splicing in vertebrate species. Science 338, 1587-1593 (2012)
27. Reyes, A. et al. Drift and conservation of differential exon usage across tissues in primate species. Proc. Natl. Acad. Sci. USA 110, 15377-15382 (2013)
28. Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. &Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479-1485 (2016)
29. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506-511 (2013)
30. Hsiao, Y. H. et al. Alternative splicing modulated by genetic variants demonstrates accelerated evolution regulated by highly conserved proteins. Genome Res. 26, 440-450 (2016)
31. Barbeira, A.N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Preprint available at https://www.biorxiv.org/content/early/2017/10/03/045260 (2017)
32. Orozco, G. et al. Association of CD40 with rheumatoid arthritis confirmed in a large UK case-control study. Ann. Rheum. Dis. 69, 813-816 (2010)
33. van Geijn, B., McVicker, G., Gilad, Y. &Pritchard, J. K. WASP: allelespecific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061-1063 (2015)
34. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013)
35. Wheeler, H. E. et al. Survey of the heritability and sparse architecture of gene expression traits across human tissues. PLoS Genet 12, e1006423 (2016)
36. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091-1098 (2015)
37. Ellis, S.E., Collado Torres, L. &Leek, J. Improving the value of public RNA-seq expression data by phenotype prediction. Preprint available at http://www.biorxiv.org/content/early/2017/06/03/145656.full.pdf (2017)