{"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/biisq-bayesian-nonparametric-discovery-of","title":"BIISQ: Bayesian nonparametric discovery of Isoforms and Individual Specific Quantification","arxiv_id":"1703.08260","date":"2017-03-24","proceeding":null,"authors":[],"abstract":"Most human protein-coding genes can be transcribed into multiple possible\ndistinct mRNA isoforms. These alternative splicing patterns encourage molecular\ndiversity and dysregulation of isoform expression plays an important role in\ndisease etiology. However, isoforms are difficult to characterize from\nshort-read RNA-seq data because they share identical subsequences and exist in\ntissue- and sample-specific frequencies. Here, we develop BIISQ, a Bayesian\nnonparametric model to discover Isoforms and Individual Specific Quantification\nfrom RNA-seq data. BIISQ does not require known isoform reference sequences but\ninstead estimates isoform composition directly with an isoform catalog shared\nacross samples. We develop a stochastic variational inference approach for\nefficient and robust posterior inference and demonstrate superior precision and\nrecall for short read RNA-seq simulations and simulated short read data from\nPacBio long read sequencing when compared to state-of-the-art isoform\nreconstruction methods. BIISQ achieves the most significant gains for longer\n(in terms of exons) isoforms and isoforms that are lowly expressed (over 500%\nmore transcripts correctly inferred at low coverage in simulations). Finally,\nwe estimate isoforms in the GEUVADIS RNA-seq data, identify genetic variants\nthat regulate transcript ratios, and demonstrate variant enrichment in\nfunctional elements related to mRNA splicing regulation.","url_abs":"http://arxiv.org/abs/1703.08260v1","url_pdf":"http://arxiv.org/pdf/1703.08260v1.pdf","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":"biisq-bayesian-nonparametric-discovery-of","repo_url":"https://github.com/bee-hive/BIISQ","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}