- Title Information
- Title
- Genome-wide annotation of functional branchpoints in the human genome
- Type of Resource (primo)
- dissertations
- Name:
Personal
- Name Part
- Zhong, Yu
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Singh, Ritambhara
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Bailey, Jeff
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Fairbrother, William
- Role
- Role Term:
Text
- Advisor
- Name:
Corporate
- Name Part
- Brown University. Center for Computational Molecular Biology
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2026
- Physical Description
- Extent
- 1, 35 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (A. M.)--Brown University, 2026
- Genre (aat)
- theses
- Abstract
- Branchpoints (BPs) are pivotal control elements in RNA splicing, yet they remain the most elusive of the core splicing signals. Comprehensive annotation of the human branchpoint landscape has been hindered by the technical biases of enzymatic mapping and the scarcity of training data for the minor (U12-type) spliceosome. Here, we present DeepEnsemble, an ensemble-based deep learning framework that integrates sequence and genomic features to resolve the "splicing code" of both major and minor introns. By employing a transfer learning strategy, DeepEnsemble overcomes data sparsity to achieve unprecedented accuracy in predicting U12-type BPs, revealing strict evolutionary constraints distinct from the degenerate motifs of the major spliceosome. We rigorously validated our predictions using Massively Parallel Reporter Assays (MPRAs), systematically testing thousands of introns and variants. This experimental audit confirmed the existence of functional BPs in over 60% of introns lacking prior annotation and demonstrated a 70% concordance between our model and in vivo lariat formation. Furthermore, we developed DeepEnsemble-LR, an interpretable extension that integrates in silico mutagenesis with evolutionary conservation to prioritize pathogenic splicing variants, outperforming existing state-of-the-art methods. Our study provides a unified, experimentally validated atlas of human branchpoints and a robust tool for decoding the clinical impact of non-coding variation.
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01086257")
- Topic
- RNA splicing
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/02032663")
- Topic
- Deep learning (Machine learning)
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20260427