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Computational tools and resources to study alternative splicing and mRNA isoform variation

研究选择性剪接和 mRNA 亚型变异的计算工具和资源

基本信息

批准号：
10669330
负责人：
Yi Xing
金额：
$ 56.88万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-13 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10669330
关键词：
Address Adopted Adoption Algorithmic Software Algorithms Alternative Splicing Atlases Big Data Bioinformatics Biology Cell Differentiation process Cells Communities Computer software Data Data Set Development Disease Ensure Event Exons Gene Expression Gene Expression Regulation Genes Genetic Genomics Genotype-Tissue Expression Project Heterogeneity Human Human Development Immune Introns Length Mammalian Cell Measurement Medicine Messenger RNA Molecular Molecular Analysis Mus Pattern Performance Phenotype Population Protein Isoforms Protocols documentation RNA RNA Splicing Regulation Research Research Personnel Resolution Scientist Techniques Technology Testing Tissues Transcriptional Regulation Variant Work base brain tissue computerized tools computing resources data resource deep learning deep learning model epigenomics genetic variant human disease improved interest mRNA Precursor mammalian genome nanopore neuron development novel open source predictive tools programs public repository single cell analysis single-cell RNA sequencing software development tool trait transcriptome transcriptome sequencing transcriptomics user-friendly

项目摘要

PROJECT SUMMARY The central objective of this project is to develop broadly applicable computational tools and resources for studying pre-mRNA alternative splicing (AS) and mRNA isoform variation. AS is a prevalent mechanism for generating regulatory and functional diversity. Almost all multi-exon genes in mammalian genomes undergo AS, resulting in mRNAs that vary in their exon composition. Widespread changes in AS occur in development, cell differentiation, and disease. However, many questions remain about the patterns, regulation, and functions of AS. Rapid accumulation of large-scale RNA-seq data in public repositories, emergence of new long-read sequencing strategies for transcriptome analysis, and recent advances in deep learning techniques create exciting opportunities for omics and big data approaches to uncover new regulatory and functional features of AS. To make full use of these powerful datasets and technologies, new computational tools are urgently needed. Our lab has an outstanding track record in developing and disseminating widely adopted algorithms and computational tools for characterizing AS and mRNA isoform variation. In this R01 project, building on that expertise, we will develop new computational tools and resources to study intron retention – a still poorly understood mechanism of gene regulation in human cells – and characterize genetically regulated intron retention events in the human population (Aim 1), and to delineate AS patterns and mRNA isoform variation using long-read RNA-seq data of bulk tissues (Aim 2) and single cells (Aim 3). The resulting user-friendly, scalable tools as well as associated data resources will substantially enhance the ability of biomedical researchers to study AS and transcriptome complexity using in-house and public datasets in diverse normal and diseased contexts. All software developed in this project will be made open source and freely available to the research community. We will leverage our extensive network of expert collaborators and broad user base to enhance the development, testing, and dissemination of these computational tools. Our efforts will be integrated with our long-standing interest in the biology of AS and mRNA isoform regulation, ensuring that our work addresses important biomedical questions and serves unmet technological needs. Importantly, in each aim the development of computational tools and resources will synergize with their applications to questions about the variation, regulation, and functions of AS. Overall, we expect that our proposed R01 project will provide powerful and widely used computational tools and resources for studying AS in mammalian transcriptomes.

项目总结