Differential exon usage in single cell RNA-seq

单细胞 RNA-seq 中的差异外显子使用

• 批准号: 10629529
• 负责人: Mira Han
• 金额: $ 14.59万
• 依托单位: UNIVERSITY OF NEVADA LAS VEGAS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629529
• 关键词: Algorithms; Alternative Splicing; Atlases; Benchmarking; Binomial Distribution; Cell Differentiation process; Cell Separation; Cells; Data; Data Set; Detection; Development; Disease; Dropout; Event; Exons; Foundations; Genes; Human; Human Development; Individual; Knowledge; Length; Literature; Maps; Methods; Modeling; Molecular; Negative Binomial Distributions; Noise; Peripheral Blood Mononuclear Cell; Probability; Protein Isoforms; Protocols documentation; RNA Splicing; Regulation; Resolution; Sensitivity and Specificity; Time; Tissues; Transcript; Variant; Work; cell type; human disease; improved; interest; neglect; new technology; novel; novel strategies; organ growth; simulation; single molecule; single-cell RNA sequencing; stem; transcriptome; transcriptome sequencing

Project Summary Alternative splicing generates transcripts that vary between tissues and cell types, and contributes to cell differentiation and organ development. Despite its importance in development and cell identity, alternative splicing is usually neglected in single cell RNA sequencing (scRNA-seq) analysis. This is due to the challenge in identifying confident alternative splicing events from scRNA- seq data, which are limited in read-depth, and comes with large amount of noise due to variation in molecule capture efficiency, amplification bias, and excessive zero-counts or dropouts. We propose a novel approach to infer differential splicing based on short-read full-length scRNA-seq data, utilizing read counts mapping to adjacent exons. In addition, we plan to systematically evaluate existing approaches to answer questions on the strategy regarding the variable, pooling and dispersion estimates. Finally, we will assess the accuracy of short read based methods by comparing against scRNA-seq generated with new types of protocols. The work proposed will increase our understanding on the utility and limitations of short read scRNA-seq data, and provide a better pipeline for alternative splicing analysis in scRNA-seq.

项目概要 选择性剪接产生的转录本在组织和细胞类型之间有所不同，并且 有助于细胞分化和器官发育。尽管它在发展和 细胞身份，选择性剪接在单细胞 RNA 测序 (scRNA-seq) 中通常被忽略 分析。这是由于从 scRNA 中识别可靠的选择性剪接事件是一项挑战。 seq数据，其读取深度有限，并且由于变化而带来大量噪声 分子捕获效率、扩增偏差以及过多的零计数或丢失。我们建议 一种基于短读长全长 scRNA-seq 数据推断差异剪接的新方法， 利用映射到相邻外显子的读取计数。此外，我们计划系统地评估 回答有关变量、汇集和策略问题的现有方法 离散度估计。最后，我们将通过比较来评估基于短读的方法的准确性 针对使用新型协议生成的 scRNA-seq。拟议的工作将增加我们的 了解短读 scRNA-seq 数据的实用性和局限性，并提供更好的 scRNA-seq 中选择性剪接分析的流程。