{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-splicing-from-primary-sequence","title":"Predicting Splicing from Primary Sequence with Deep Learning","arxiv_id":null,"date":"2019-01-17","proceeding":"A Cell Press journal 2019 1","authors":["Kishore Jaganathan","Sofia Kyriazopoulou Panagiotopoulou","Jeremy F. McRae","Siavash Fazel Darbandi","David Knowles","Yang I. Li","Jack A. Kosmicki","Juan Arbelaez","Wenwu Cui","Grace B. Schwartz","Eric D. Chow","Efstathios Kanterakis","Hong Gao","Amirali Kia","Serafim Batzoglou","Stephan J. Sanders","Kyle Kai-How Farh"],"abstract":"The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%–11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.","url_abs":"https://www.cell.com/action/showPdf?pii=S0092-8674%2818%2931629-5","url_pdf":"https://www.cell.com/action/showPdf?pii=S0092-8674%2818%2931629-5","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-splicing-from-primary-sequence","repo_url":"https://github.com/Illumina/SpliceAI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"predicting-splicing-from-primary-sequence","repo_url":"https://github.com/mainguyenanhvu/SpliceAI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"predicting-splicing-from-primary-sequence","repo_url":"https://github.com/mainguyenanhvu/spliceai-reforged","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"predicting-splicing-from-primary-sequence","repo_url":"https://github.com/skoblov-lab/spliceai-reforged","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}