Annotation-free quantification of RNA splicing using LeafCutter
Open accessPublished version of a preprint
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Local differential splicing was assessed with LeafCutter using de novo aligned RNA-seq reads and the same covariates as DGE/DTE.
“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]
Using de novo junctions increased confidence of detection of differential splicing, and the procedure took into account known and novel splice sites.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692 · see citing works
<i>Stan</i> : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754 · see citing works
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works
Self-citationDifferential analysis of gene regulation at transcript resolution with RNA-seq
Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013) · Nature Biotechnology · doi:10.1038/nbt.2450 · Cited by 3,492 · see citing works
Shen, S., Park, J. W., Lu, Z.-X., Lin, L., Henry, M. D., Wu, Y. N., Zhou, Q., & Xing, Y. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419161111 · Cited by 2,713 · see citing works
Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis
The International Multiple Sclerosis Genetics Consortium & The Wellcome Trust Case Control Consortium 2 (2011) · Nature · doi:10.1038/nature10251 · Cited by 2,697 · see citing works
Genetics of rheumatoid arthritis contributes to biology and drug discovery
the RACI consortium, Okada, Y., the GARNET consortium, Wu, D., Trynka, G., Raj, T., Terao, C., Ikari, K., Kochi, Y., Ohmura, K., et al. (2014) · Nature · doi:10.1038/nature12873 · Cited by 2,310 · see citing works
Transcriptome and genome sequencing uncovers functional variation in humans
The Geuvadis Consortium, Lappalainen, T., Sammeth, M., Friedländer, M. R., ‘t Hoen, P. A. C., Monlong, J., Rivas, M. A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., et al. (2013) · Nature · doi:10.1038/nature12531 · Cited by 1,888 · see citing works
A gene-based association method for mapping traits using reference transcriptome data
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3367 · Cited by 1,762 · see citing works
Self-citationDetecting differential usage of exons from RNA-seq data
Anders, S., Reyes, A., & Huber, W. (2012) · Genome Research · doi:10.1101/gr.133744.111 · Cited by 1,545 · see citing works
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Katz, Y., Wang, E. T., Airoldi, E. M., & Burge, C. B. (2010) · Nature Methods · doi:10.1038/nmeth.1528 · Cited by 1,353 · see citing works
EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments
Leng, N., Dawson, J. A., Thomson, J. A., Ruotti, V., Rissman, A. I., Smits, B. M. G., Haag, J. D., Gould, M. N., Stewart, R. M., & Kendziorski, C. M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt087 · Cited by 1,304 · see citing works
Hou, L., Kellis, M., Liu, Y., Park, Y., & Rinaldi, N. J. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.34944/dspace/4648 · Cited by 1,082 · see citing works
The Evolutionary Landscape of Alternative Splicing in Vertebrate Species
Barbosa-Morais, N. L., Irimia, M., Pan, Q., Xiong, H. Y., Gueroussov, S., Lee, L. J., Slobodeniuc, V., Kutter, C., Watt, S., Çolak, R., et al. (2012) · Science · doi:10.1126/science.1230612 · Cited by 1,063 · see citing works
Evolutionary Dynamics of Gene and Isoform Regulation in Mammalian Tissues
Merkin, J., Russell, C., Chen, P., & Burge, C. B. (2012) · Science · doi:10.1126/science.1228186 · Cited by 972 · see citing works
Origins, evolution, and phenotypic impact of new genesPreprint
Kaessmann, H. (2010) · Genome Research · doi:10.1101/gr.101386.109 · Cited by 818 · see citing works
RNA splicing is a primary link between genetic variation and disease
Li, Y. I., van de Geijn, B., Raj, A., Knowles, D. A., Petti, A. A., Golan, D., Gilad, Y., & Pritchard, J. K. (2016) · Science · doi:10.1126/science.aad9417 · Cited by 660 · see citing works
Self-citationDNase I sensitivity QTLs are a major determinant of human expression variation
Degner, J. F., Pai, A. A., Piqué-Regi, R., Veyrieras, J., Gaffney, D. J., Pickrell, J. K., Leon, S. D., Michelini, K., Lewellen, N., Crawford, G. E., et al. (2012) · Nature · doi:10.1038/nature10808 · Cited by 633 · see citing works
Self-citationCellular Source and Mechanisms of High Transcriptome Complexity in the Mammalian Testis
Soumillon, M., Necşulea, A., Weier, M., Brawand, D., Zhang, X., Gu, H., Barthès, P., Kokkinaki, M., Nef, S., Gnirke, A., et al. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.05.031 · Cited by 606 · see citing works
Patro, R., Mount, S. M., & Kingsford, C. (2014) · Nature Biotechnology · doi:10.1038/nbt.2862 · Cited by 586 · see citing works
WASP: allele-specific software for robust molecular quantitative trait locus discovery
van de Geijn, B., McVicker, G., Gilad, Y., & Pritchard, J. K. (2015) · Nature Methods · doi:10.1038/nmeth.3582 · Cited by 575 · see citing works
Self-citationFast and efficient QTL mapper for thousands of molecular phenotypes
Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T., & Delaneau, O. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv722 · Cited by 571 · see citing works
Alternative splicing and genome complexity
Brett, D. J., Pospisil, H., Valcárcel, J., Jg, R., & Peer Bork, B. (2001) · Nature Genetics · doi:10.1038/ng803 · Cited by 537 · see citing works
A new view of transcriptome complexity and regulation through the lens of local splicing variations
Vaquero-Garcia, J., Barrera, A., Gazzara, M. R., González-Vallinas, J., Lahens, N. F., Hogenesch, J. B., Lynch, K. W., & Barash, Y. (2016) · eLife · doi:10.7554/elife.11752 · Cited by 488 · see citing works
Tilgner, H., Knowles, D. G., Johnson, R., Davis, C., Chakrabortty, S. K., Djebali, S., Curado, J., Snyder, M. P., Gingeras, T., & Guigó, R. (2012) · Genome Research · doi:10.1101/gr.134445.111 · Cited by 463 · see citing works
Self-citationMBNL proteins repress ES-cell-specific alternative splicing and reprogramming
Han, H. R., Irimia, M., Ross, P. J., Sung, H., Alipanahi, B., David, L., Golipour, A., Gabut, M., Michael, I. P., Nachman, E. N., et al. (2013) · Nature · doi:10.1038/nature12270 · Cited by 326 · see citing works
<i>Polyester</i> : simulating RNA-seq datasets with differential transcript expression
Frazee, A. C., Jaffe, A. E., Langmead, B., & Leek, J. T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv272 · Cited by 313 · see citing works
RBFOX and PTBP1 proteins regulate the alternative splicing of micro-exons in human brain transcriptsPreprint
Li, Y., Sánchez‐Pulido, L., Haerty, W., & Ponting, C. P. (2014) · Genome Research · doi:10.1101/gr.181990.114 · Cited by 231 · see citing works
Self-citationCalarco, J. A., Superina, S., O'Hanlon, D., Gabut, M., Raj, B., Pan, Q., Skalska, U., Clarke, L., Gelinas, D., van der Kooy, D., et al. (2009) · Cell · doi:10.1016/j.cell.2009.06.012 · Cited by 225 · see citing works
Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues
Wheeler, H. E., Shah, K. P., Brenner, J., Garcia, T., Aquino-Michaels, K., Cox, N. J., Nicolae, D. L., & Im, H. K. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006423 · Cited by 135 · see citing works
Self-citationDrift and conservation of differential exon usage across tissues in primate species
Reyes, A., Anders, S., Weatheritt, R. J., Gibson, T. J., Steinmetz, L. M., & Huber, W. K. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1307202110 · Cited by 132 · see citing works
OLego: fast and sensitive mapping of spliced mRNA-Seq reads using small seeds
Wu, J., Anczuków, O., Krainer, A. R., Zhang, M. Q., & Zhang, C. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt216 · Cited by 123 · see citing works
Identification of genetic variants associated with alternative splicing using sQTLseekeR
Monlong, J., Calvo, M., Ferreira, P. G., & Guigó, R. (2014) · Nature Communications · doi:10.1038/ncomms5698 · Cited by 118 · see citing works
Nellore, A., Jaffe, A. E., Fortin, J., Alquicira-Hernández, J., Collado‐Torres, L., Wang, S., Phillips, R. A., Karbhari, N., Hansen, K. D., Langmead, B., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1118-6 · Cited by 98 · see citing works
Pai, A. A., Baharian, G., Sabourin, A. P., Brinkworth, J. F., Nédélec, Y., Foley, J. W., Grenier, J., Siddle, K. J., Dumaine, A., Yotova, V., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006338 · Cited by 91 · see citing works
Association of CD40 with rheumatoid arthritis confirmed in a large UK case-control study
Orozco, G., Eyre, S., Hinks, A., Ke, X., Wilson, A. G., Bax, D. E., Morgan, A. W., Emery, P., Steer, S. E., Hocking, L. J., et al. (2009) · Annals of the Rheumatic Diseases · doi:10.1136/ard.2009.109579 · Cited by 78 · see citing works
Zhao, K., Lu, Z., Park, J. W., Zhou, Q., & Xing, Y. (2013) · Genome biology · doi:10.1186/gb-2013-14-7-r74 · Cited by 72 · see citing works
Barbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03621-1 · Cited by 69 · see citing works
Self-citationRail-RNA: scalable analysis of RNA-seq splicing and coverage
Nellore, A., Collado‐Torres, L., Jaffe, A. E., Alquicira-Hernández, J., Wilks, C., Pritt, J., Morton, J. T., Leek, J. T., & Langmead, B. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw575 · Cited by 58 · see citing works
Improving the value of public RNA-seq expression data by phenotype prediction
Ellis, S., Collado‐Torres, L., Jaffe, A. E., & Leek, J. T. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky102 · Cited by 54 · see citing works
Hsiao, Y.-H. E., Bahn, J. H., Lin, X., Chan, T.-M., Wang, R., & Xiao, X. G. (2016) · Genome Research · doi:10.1101/gr.193359.115 · Cited by 53 · see citing works
Exact Transcriptome Reconstruction from Short Sequence Reads
Lacroix, V., Sammeth, M., Guigó, R., & Bergeron, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87361-7_5 · Cited by 52 · see citing works
Alternative Splicing QTLs in European and African Populations
Ongen, H., & Dermitzakis, E. T. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.09.004 · Cited by 47 · see citing works
Discover hidden splicing variations by mapping personal transcriptomes to personal genomes
Stein, S., Lu, Z., Bahrami‐Samani, E., Park, J. W., & Xing, Y. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1099 · Cited by 12 · see citing works
Survey of the Heritability and Sparse Architecture of Gene Expression Traits Across Human TissuesPreprint
Wheeler, H. E., Shah, K. P., Brenner, J., Garcia, T., Aquino-Michaels, K., Cox, N. J., Nicolae, D. L., & Im, H. K. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/043653 · Cited by 12 · see citing works
Self-citationImproving the value of public RNA-seq expression data by phenotype predictionPreprint
Ellis, S., Collado‐Torres, L., & Leek, J. T. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/145656 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [8]Bray N Pimentel H Melsted P Pachter L Near-optimal rna-seq quantification arxiv 1 1 2015
Cited by
Sorted by citations
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Self-citationPredicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853 · see citing works
Self-citationTranscriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., et al. (2018) · Science · doi:10.1126/science.aat8127 · Cited by 1,247 · see citing works
Gustavsson, E. K., Zhang, D., Reynolds, R. H., García-Ruiz, S., & Ryten, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac409 · Cited by 745 · see citing works
Haplotype-resolved diverse human genomes and integrated analysis of structural variation
Ebert, P., Audano, P. A., Zhu, Q., Rodriguez-Martin, B., Porubsky, D., Bonder, M. J., Sulovari, A., Ebler, J., Zhou, W., Serra Mari, R., et al. (2021) · Science · doi:10.1126/science.abf7117 · Cited by 702 · see citing works
Self-citationThe Polygenic and Monogenic Basis of Blood Traits and Diseases
Vuckovic, D., Bao, E. L., Akbari, P., Lareau, C. A., Mousas, A., Jiang, T., Chen, M.-H., Raffield, L. M., Tardaguila, M., Huffman, J. E., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.008 · Cited by 671 · see citing works
Self-citationCommon genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586 · see citing works
Characterizing the Major Structural Variant Alleles of the Human Genome
Audano, P. A., Sulovari, A., Graves-Lindsay, T. A., Cantsilieris, S., Sorensen, M., Welch, A. E., Dougherty, M. L., Nelson, B. J., Shah, A., Dutcher, S. K., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.019 · Cited by 492 · see citing works
Self-citationCano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 608 · see citing works
Genetic diagnosis of Mendelian disorders via RNA sequencing
Kremer, L. S., Bader, D. M., Mertes, C., Kopajtich, R., Pichler, G., Iuso, A., Haack, T. B., Graf, E., Schwarzmayr, T., Terrile, C., et al. (2017) · Nature Communications · doi:10.1038/ncomms15824 · Cited by 590 · see citing works
RNA splicing dysregulation and the hallmarks of cancer
Bradley, R. K., & Anczuków, O. (2023) · Nature Reviews Cancer · doi:10.1038/s41568-022-00541-7 · Cited by 579 · see citing works
TDP-43 represses cryptic exon inclusion in the FTD-ALS gene UNC13A
Ma, X. R., Prudencio, M., Koike, Y., Vatsavayai, S. C., Kim, G., Harbinski, F., Briner, A., Rodriguez, C. M., Guo, C., Akiyama, T., et al. (2022) · Nature · doi:10.1038/s41586-022-04424-7 · Cited by 574 · see citing works
Gusev, A., Mancuso, N., Won, H., Kousi, M., Finucane, H. K., Reshef, Y., Song, L., Safi, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0092-1 · Cited by 491 · see citing works
Cell type–specific genetic regulation of gene expression across human tissues
Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S. E., Hamel, A. R., Viñuela, A., Roberts, A. L., et al. (2020) · Science · doi:10.1126/science.aaz8528 · Cited by 530 · see citing works
Self-citationTDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A
Brown, A., Wilkins, O. G., Keuss, M. J., Kargbo-Hill, S. E., Zanovello, M., Lee, W. C., Bampton, A., Lee, F. C. Y., Masino, L., Qi, Y. A., et al. (2022) · Nature · doi:10.1038/s41586-022-04436-3 · Cited by 526 · see citing works
Self-citationA compendium of uniformly processed human gene expression and splicing quantitative trait loci
Kerimov, N., Hayhurst, J. D., Peikova, K., Manning, J. R., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00924-w · Cited by 440 · see citing works
Raj, T., Li, Y. I., Wong, G., Humphrey, J., Wang, M., Ramdhani, S., Wang, Y.-C., Ng, B., Gupta, I., Haroutunian, V., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0238-1 · Cited by 430 · see citing works
Self-citationThe GTEx Consortium atlas of genetic regulatory effects across human tissuesPreprint
Aguet, F., Barbeira, A., Bonazzola, R., Brown, A. A., Castel, S. E., Jo, B., Kasela, S., Kim-Hellmuth, S., Liang, Y., Oliva, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/787903 · Cited by 379 · see citing works
Self-citationSingle-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells
Gupta, I., Collier, P. G., Haase, B., Mahfouz, A., Joglekar, A., Floyd, T., Koopmans, F., Barres, B., Smit, A. B., Sloan, S. A., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4259 · Cited by 343 · see citing works
A multi-tissue atlas of regulatory variants in cattle
Liu, S., Gao, Y., Canela‐Xandri, O., Wang, S., Yu, Y., Cai, W., Li, B., Xiang, R., Chamberlain, A. J., Pairo‐Castineira, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01153-5 · Cited by 311 · see citing works
Transcriptome variation in human tissues revealed by long-read sequencing
Glinos, D. A., Garborcauskas, G., Hoffman, P. J., Ehsan, N., Jiang, L., Gokden, A., Dai, X.-G., Aguet, F., Brown, K. L., Garimella, K., et al. (2021) · Nature · doi:10.1038/s41586-022-05035-y · Cited by 278 · see citing works
Beyond mass spectrometry, the next step in proteomics
Timp, W., & Timp, G. (2020) · Science Advances · doi:10.1126/sciadv.aax8978 · Cited by 254 · see citing works
Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification
Dapas, M., & Dunaif, A. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnac001 · Cited by 300 · see citing works
Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms
Walker, R. L., Ramaswami, G., Hartl, C., Mancuso, N., Gandal, M. J., de la Torre-Ubieta, L., Paşaniuc, B., Stein, J. L., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.09.021 · Cited by 259 · see citing works
Lopes, K. D. P., Snijders, G. J. L., Humphrey, J., Allan, A., Sneeboer, M. A. M., Navarro, E., Schilder, B. M., Vialle, R. A., Parks, M., Missall, R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00976-y · Cited by 273 · see citing works
Self-citationZhou, S., Butler‐Laporte, G., Nakanishi, T., Morrison, D., Afilalo, J., Afilalo, M., Laurent, L., Pietzner, M., Kerrison, N. D., Zhao, K., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01281-1 · Cited by 273 · see citing works
Wang, Y., Xie, Z., Kutschera, E., Adams, J. I., Kadash-Edmondson, K. E., & Xing, Y. (2024) · Nature Protocols · doi:10.1038/s41596-023-00944-2 · Cited by 277 · see citing works
A compendium of genetic regulatory effects across pig tissues
Teng, J., Gao, Y., Yin, H. H., Bai, Z., Liu, S., Zeng, H., Bai, L., Cai, Z., Zhao, B., Li, X., et al. (2024) · Nature Genetics · doi:10.1038/s41588-023-01585-7 · Cited by 276 · see citing works
Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia
Prudencio, M., Humphrey, J., Pickles, S., Brown, A.-L., Hill, S. E., Kachergus, J. M., Shi, J., Heckman, M. G., Spiegel, M. R., Cook, C., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci139741 · Cited by 231 · see citing works
Self-citationGenetic control of RNA splicing and its distinct role in complex trait variation
Qi, T., Wu, Y., Fang, H., Zhang, F., Liu, S., Zeng, J., & Yang, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01154-4 · Cited by 237 · see citing works
Alternative mRNA splicing in cancer immunotherapy
Frankiw, L., Baltimore, D., & Li, G. (2019) · Nature reviews. Immunology · doi:10.1038/s41577-019-0195-7 · Cited by 248 · see citing works
Multiomics study of nonalcoholic fatty liver disease
Sveinbjörnsson, G., Ulfarsson, M. O., Þórólfsdóttir, R. B., Jónsson, B. A., Einarsson, E., Gunnlaugsson, G., Rögnvaldsson, S., Arnar, D. O., Baldvinsson, M., Bjarnason, R. G., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01199-5 · Cited by 242 · see citing works
Qiu, C., Huang, S., Park, J., Park, Y., Ko, Y.-A., Seasock, M. J., Bryer, J. S., Xu, X., Song, W., Palmer, M., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0194-4 · Cited by 230 · see citing works
Molecular and functional variation in iPSC-derived sensory neurons
HIPSCI Consortium, Schwartzentruber, J., Foskolou, S., Kilpinen, H., Rodrigues, J., Alasoo, K., Knights, A. J., Patel, M., Goncalves, A., Ferreira, R., et al. (2018) · Nature Genetics · doi:10.1038/s41588-017-0005-8 · Cited by 223 · see citing works
The role of ABCA7 in Alzheimer’s disease: evidence from genomics, transcriptomics and methylomics
De Roeck, A., Van Broeckhoven, C., & Sleegers, K. (2019) · Acta Neuropathologica · doi:10.1007/s00401-019-01994-1 · Cited by 212 · see citing works
Clinical implementation of RNA sequencing for Mendelian disease diagnostics
Yépez, V. A., Gušić, M., Kopajtich, R., Mertes, C., Smith, N. H., Alston, C. L., Ban, R., Beblo, S., Berutti, R., Blessing, H., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01019-9 · Cited by 210 · see citing works
DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics
Nowicka, M., & Robinson, M. D. (2016) · F1000Research · doi:10.12688/f1000research.8900.2 · Cited by 207 · see citing works
Ageing-associated changes in transcriptional elongation influence longevity
Debès, C., Papadakis, A., Grönke, S., Karalay, Ö., Tain, L. S., Mizi, A., Nakamura, S., Hahn, O., Weigelt, C. M., Josipović, N., et al. (2023) · Nature · doi:10.1038/s41586-023-05922-y · Cited by 209 · see citing works
Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease
de Goede, O. M., Nachun, D. C., Ferraro, N. M., Gloudemans, M. J., Rao, A. S., Smail, C., Eulalio, T. Y., Aguet, F., Ng, B., Xu, J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.050 · Cited by 194 · see citing works
Self-citationZhang, Z. S., Luo, K., Zou, Z., Qiu, M., Tian, J., Sieh, L., Shi, H., Zou, Y., Wang, G., Morrison, J., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0644-z · Cited by 187 · see citing works
Detection of aberrant splicing events in RNA-seq data using FRASER
Mertes, C., Scheller, I. F., Yépez, V. A., Çelik, M. H., Liang, Y., Kremer, L. S., Gušić, M., Prokisch, H., & Gagneur, J. (2021) · Nature Communications · doi:10.1038/s41467-020-20573-7 · Cited by 180 · see citing works
Bowles, K. R., Silva, M. C., Whitney, K., Bertucci, T., Berlind, J. E., Lai, J. D., Garza, J. C., Boles, N. C., Mahali, S., Strang, K. H., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.003 · Cited by 175 · see citing works
Recurrent noncoding U1 snRNA mutations drive cryptic splicing in SHH medulloblastoma
Suzuki, H., Kumar, S. A., Shuai, S., Díaz‐Navarro, A., Gutiérrez‐Fernández, A., Antonellis, P. D., Cavalli, F. M. G., Juraschka, K., Farooq, H., Shibahara, I., et al. (2019) · Nature · doi:10.1038/s41586-019-1650-0 · Cited by 171 · see citing works
The U1 spliceosomal RNA is recurrently mutated in multiple cancers
Shuai, S., Suzuki, H., Díaz‐Navarro, A., Nadeu, F., Kumar, S. A., Gutiérrez‐Fernández, A., Delgado, J., Pinyol, M., López-Otı́n, C., Puente, X. S., et al. (2019) · Nature · doi:10.1038/s41586-019-1651-z · Cited by 169 · see citing works
Transcriptomic signatures across human tissues identify functional rare genetic variation
Ferraro, N. M., Strober, B. J., Einson, J. E., Abell, N. S., Aguet, F., Barbeira, A., Brandt, M. K., Bućan, M., Castel, S. E., Davis, J. R., et al. (2020) · Science · doi:10.1126/science.aaz5900 · Cited by 155 · see citing works
Self-citationXiang, R., van den Berg, I., MacLeod, I. M., Hayes, B. J., Prowse-Wilkins, C., Wang, M., Bolormaa, S., Liu, Z., Rochfort, S. J., Reich, C. M., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1904159116 · Cited by 171 · see citing works
Systematic evaluation of differential splicing tools for RNA-seq studies
Mehmood, A., Laiho, A., Venäläinen, M. S., McGlinchey, A. J., Wang, N., & Elo, L. L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz126 · Cited by 168 · see citing works
Detection of aberrant gene expression events in RNA sequencing data
Yépez, V. A., Mertes, C., Müller, M., Klaproth-Andrade, D., Wachutka, L., Frésard, L., Gušić, M., Scheller, I. F., Goldberg, P. F., Prokisch, H., et al. (2021) · Nature Protocols · doi:10.1038/s41596-020-00462-5 · Cited by 167 · see citing works
Doyle, S. R., Tracey, A., Laing, R., Holroyd, N. E., Bartley, D. J., Bazant, W., Beasley, H. L., Beech, R. N., Britton, C., Brooks, K. L., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-01377-3 · Cited by 162 · see citing works
Garrido-Martín, D., Borsari, B., Calvo, M., Reverter, F., & Guigó, R. (2021) · Nature Communications · doi:10.1038/s41467-020-20578-2 · Cited by 140 · see citing works
m6A mRNA methylation is essential for oligodendrocyte maturation and CNS myelination
Xu, H., Dzhashiashvili, Y., Shah, A., Kunjamma, R. B., Weng, Y.-L., Elbaz, B., Fei, Q., Jones, J., Li, Y. I., Zhuang, X., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.12.013 · Cited by 142 · see citing works
Self-citationRNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
den Berge, K. V., Hembach, K., Soneson, C., Tiberi, S., Clement, L., Love, M. I., Patro, R., & Robinson, M. D. (2019) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021255 · Cited by 144 · see citing works
Prioritizing Parkinson’s disease genes using population-scale transcriptomic data
Li, Y. I., Wong, G., Humphrey, J., & Raj, T. (2019) · Nature Communications · doi:10.1038/s41467-019-08912-9 · Cited by 153 · see citing works
Self-citationRNA-seq data science: From raw data to effective interpretation
Deshpande, D., Chhugani, K., Chang, Y., Karlsberg, A., Loeffler, C., Zhang, J., Muszyńska, A., Munteanu, V., Yang, H. T., Rotman, J., et al. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.997383 · Cited by 147 · see citing works
Panousis, N. I., Bertsias, G. K., Ongen, H., Gergianaki, I., Tektonidou, M. G., Trachana, M., Romano, L., Bielser, D., Howald, C., Pamfil, C. A. M., et al. (2019) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2018-214379 · Cited by 141 · see citing works
Choi, J., Zhang, T., Vu, A. H., Ablain, J., Makowski, M. M., Colli, L. M., Xu, M., Hennessey, R. C., Yin, J., Rothschild, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16590-1 · Cited by 139 · see citing works
Humphrey, J., Venkatesh, S., Hasan, R., Herb, J. T., de Paiva Lopes, K., Küçükali, F., Byrska-Bishop, M., Evani, U. S., Narzisi, G., Fagegaltier, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01205-3 · Cited by 138 · see citing works
Self-citationCotto, K. C., Feng, Y.-Y., Ramu, A., Richters, M. M., Freshour, S. L., Skidmore, Z. L., Xia, H., McMichael, J. F., Kunisaki, J., Campbell, K. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37266-6 · Cited by 131 · see citing works
Love, M. I., Soneson, C., & Patro, R. (2018) · F1000Research · doi:10.12688/f1000research.15398.1 · Cited by 123 · see citing works
Truncated FGFR2 is a clinically actionable oncogene in multiple cancers
Zingg, D., Bhin, J., Yemelyanenko, J., Kas, S. M., Rolfs, F., Lutz, C., Lee, J., Klarenbeek, S., Silverman, I. M., Annunziato, S., et al. (2022) · Nature · doi:10.1038/s41586-022-05066-5 · Cited by 123 · see citing works
RNA splicing analysis using heterogeneous and large RNA-seq datasets
Vaquero-Garcia, J., Aicher, J. K., Jewell, S., Gazzara, M. R., Radens, C. M., Jha, A., Green, C. J., Norton, S., Lahens, N. F., Grant, G. R., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36585-y · Cited by 124 · see citing works
A mouse-specific retrotransposon drives a conserved Cdk2ap1 isoform essential for development
Modzelewski, A. J., Shao, W., Chen, J., Lee, A., Qi, X., Noon, M., Tjokro, K., Sales, G., Biton, A., Anand, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.09.021 · Cited by 115 · see citing works
Quantitative analysis of cryptic splicing associated with TDP-43 depletion
Humphrey, J., Emmett, W., Fratta, P., Isaacs, A. M., & Plagnol, V. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0274-1 · Cited by 113 · see citing works
Self-citationKnowles, D. A., Burrows, C. K., Blischak, J., Patterson, K. M., Serie, D., Norton, N., Ober, C., Pritchard, J. K., & Gilad, Y. (2018) · eLife · doi:10.7554/elife.33480 · Cited by 116 · see citing works
Self-citationRNA-Seq Perspectives to Improve Clinical Diagnosis
Marco-Puche, G., Lois, S., Benitez, J. J., & Triviño, J. C. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01152 · Cited by 115 · see citing works
Love, M. I., Soneson, C., & Patro, R. (2018) · F1000Research · doi:10.12688/f1000research.15398.3 · Cited by 112 · see citing works
Wang, Z., Yang, X. P., Liu, C., Li, X., Zhang, B., Wang, B., Zhang, Y., Song, C., Zhang, T., Liu, M., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.04.009 · Cited by 111 · see citing works
SMG5-SMG7 authorize nonsense-mediated mRNA decay by enabling SMG6 endonucleolytic activity
Boehm, V., Kueckelmann, S., Gerbracht, J. V., Kallabis, S., Britto‐Borges, T., Altmüller, J., Krüger, M., Dieterich, C., & Gehring, N. H. (2021) · Nature Communications · doi:10.1038/s41467-021-24046-3 · Cited by 104 · see citing works
Cell type specific genetic regulation of gene expression across human tissuesPreprint
Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Wucher, V., Kasela, S., Castel, S. E., Hamel, A. R., Viñuela, A., Roberts, A. L., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/806117 · Cited by 98 · see citing works
Self-citationJacobs, A., & Elmer, K. R. (2021) · Molecular Ecology · doi:10.1111/mec.15817 · Cited by 96 · see citing works
Molecular quantitative trait loci
Aguet, F., Alasoo, K., Li, Y. I., Battle, A., Im, H. K., Montgomery, S. B., & Lappalainen, T. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00188-6 · Cited by 98 · see citing works
Self-citationDRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics
Nowicka, M., & Robinson, M. D. (2016) · F1000Research · doi:10.12688/f1000research.8900.1 · Cited by 95 · see citing works
Genetic Regulatory Mechanisms of Smooth Muscle Cells Map to Coronary Artery Disease Risk Loci
Liu, B., Pjanic, M., Wang, T., Nguyen, T., Gloudemans, M., Rao, A., Castano, V. G., Nurnberg, S., Rader, D. J., Elwyn, S., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.001 · Cited by 94 · see citing works
ASCOT identifies key regulators of neuronal subtype-specific splicing
Ling, J. P., Wilks, C., Charles, R., Leavey, P. J., Ghosh, D., Jiang, L., Santiago, C. P., Pang, B., Venkataraman, A., Clark, B. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14020-5 · Cited by 107 · see citing works
Hamel, A. R., Yan, W., Rouhana, J. M., Monavarfeshani, A., Jiang, X., Mehta, P. A., Advani, J., Luo, Y., Liang, Q., Rajasundaram, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44380-y · Cited by 97 · see citing works
The transcriptional landscape of Shh medulloblastoma
Skowron, P., Farooq, H., Cavalli, F. M. G., Morrissy, A. S., Ly, M., Hendrikse, L. D., Wang, E. Y., Djambazian, H. H. V., Zhu, H., Mungall, K. L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21883-0 · Cited by 89 · see citing works
Sugino, K., Clark, E., Schulmann, A., Shima, Y., Wang, L., Hunt, D. L., Hooks, B. M., Tränkner, D., Chandrashekar, J., Picard, S., et al. (2019) · eLife · doi:10.7554/elife.38619 · Cited by 84 · see citing works
Global impact of unproductive splicing on human gene expression
Fair, B. J., Najar, C. F. B. A., Zhao, J., Lozano, S., Reilly, A., Mossian, G., Staley, J. P., Wang, J., & Li, Y. (2024) · Nature Genetics · doi:10.1038/s41588-024-01872-x · Cited by 91 · see citing works
Self-citationGenome-wide mapping of somatic mutation rates uncovers drivers of cancer
Sherman, M. A., Yaari, A. U., Priebe, O., Dietlein, F., Loh, P.-R., & Berger, B. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01353-8 · Cited by 80 · see citing works
Splicing in the Diagnosis of Rare Disease: Advances and Challenges
Lord, J., & Baralle, D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.689892 · Cited by 89 · see citing works
Genetic regulation of gene expression across multiple tissues in chickens
Guan, D., Bai, Z., Zhu, X., Zhong, C., Hou, Y., Zhu, D., Li, H., Lan, F., Diao, S., Yao, Y., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02155-9 · Cited by 93 · see citing works
Alternative splicing, RNA-seq and drug discovery
Zhao, S. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.03.030 · Cited by 88 · see citing works
Decoding disease: from genomes to networks to phenotypes
Wong, A. K., Sealfon, R. S. G., Theesfeld, C. L., & Troyanskaya, O. G. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00389-x · Cited by 87 · see citing works
Cherry, S. R., & Lynch, K. W. (2020) · Genes & Development · doi:10.1101/gad.338962.120 · Cited by 87 · see citing works
Conlon, E. G., Fagegaltier, D., Agius, P., Davis‐Porada, J., Gregory, J. A., Hubbard, I., Kang, K., Kim, D., Phatnani, H. P., Kwan, J. Y., et al. (2018) · eLife · doi:10.7554/elife.37754 · Cited by 85 · see citing works
Long non-coding RNA Neat1 regulates adaptive behavioural response to stress in mice
Kukharsky, M. S., Ninkina, N. N., An, H., Telezhkin, V., Wei, W., de Meritens, C. R., Cooper‐Knock, J., Nakagawa, S., Hirose, T., Buchman, V. L., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0854-2 · Cited by 84 · see citing works
Zandi, P. P., Jaffe, A. E., Goes, F. S., Burke, E. E., Collado‐Torres, L., Huuki-Myers, L. A., Seyedian, A., Lin, Y., Seifuddin, F., Pirooznia, M., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01024-6 · Cited by 81 · see citing works
Genetic regulation of gene expression and splicing during a 10-year period of human aging
Balliu, B., Durrant, M. G., de Goede, O. M., Abell, N. S., Li, X., Liu, B., Gloudemans, M. J., Cook, N. L., Smith, K. S., Knowles, D. A., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1840-y · Cited by 83 · see citing works
Self-citationSources of gene expression variation in a globally diverse human cohort
Taylor, D. J., Chhetri, S. B., Tassia, M. G., Biddanda, A., Yan, S. M., Wojcik, G. L., Battle, A. J., & McCoy, R. C. (2024) · Nature · doi:10.1038/s41586-024-07708-2 · Cited by 80 · see citing works
Genome-wide association and multi-omic analyses reveal ACTN2 as a gene linked to heart failure
Arvanitis, M., Tampakakis, E., Zhang, Y., Wang, W., Auton, A., Agee, M., Aslibekyan, S. W., Bell, R. K., Bryc, K., Clark, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14843-7 · Cited by 75 · see citing works
Defining the genetic and evolutionary architecture of alternative splicing in response to infection
Rotival, M., Quach, H., & Quintana‐Murci, L. (2019) · Nature Communications · doi:10.1038/s41467-019-09689-7 · Cited by 75 · see citing works
Bonder, M. J., Smail, C., Gloudemans, M. J., Frésard, L., Jakubosky, D. A., D’Antonio, M., Li, X., Ferraro, N. M., Carcamo‐Orive, I., Mirauta, B. A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00800-7 · Cited by 68 · see citing works
ASpli: integrative analysis of splicing landscapes through RNA-Seq assays
Mancini, E., Rabinovich, A., Iserte, J., Yanovsky, M. J., & Chernomoretz, A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab141 · Cited by 76 · see citing works
Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy
Li, G., Mahajan, S., Ma, S., Jeffery, E. D., Zhang, X., Bhattacharjee, A., Venkatasubramanian, M., Weirauch, M. T., Miraldi, E. R., Grimes, H. L., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.ade2886 · Cited by 82 · see citing works
Chen, K., Zhou, Y., Ding, M., Wang, Y., Ren, Z., & Yang, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae163 · Cited by 77 · see citing works
Wei, G., Almeida, M., Pintacuda, G., Coker, H. A., Bowness, J. S., Ule, J., & Brockdorff, N. (2021) · Genome Research · doi:10.1101/gr.271635.120 · Cited by 72 · see citing works
Alternative polyadenylation mediates genetic regulation of gene expression
Mittleman, B. E., Pott, S., Warland, S., Zeng, T., Mu, Z., Kaur, M., Gilad, Y., & Li, Y. (2020) · eLife · doi:10.7554/elife.57492 · Cited by 69 · see citing works
Self-citationGenetic basis of mitochondrial diseases
Gušić, M., & Prokisch, H. (2021) · FEBS Letters · doi:10.1002/1873-3468.14068 · Cited by 76 · see citing works
ICE1 promotes the link between splicing and nonsense-mediated mRNA decay
Baird, T. D., Cheng, K. C.-C., Chen, Y., Buehler, E. C., Martin, S. E., Inglese, J., & Hogg, J. R. (2018) · eLife · doi:10.7554/elife.33178 · Cited by 72 · see citing works
Holmes, G., Gonzalez-Reiche, A. S., Lü, N., Zhou, X., Rivera, J., Kriti, D., Sebra, R., Williams, A. A., Donovan, M. J., Potter, S. S., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107871 · Cited by 69 · see citing works
- 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 · 2016: 5
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
