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High-throughput detection of transcriptomic and epitranscriptomic variation and kinetics using MarathonRT

使用 MarathonRT 高通量检测转录组和表观转录组变异和动力学

基本信息

批准号：
10470888
负责人：
Brenton R. Graveley
金额：
$ 96.63万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-17 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10470888
关键词：
Algorithms Alternative Splicing Catalytic RNA Cell Cycle Kinetics Cell physiology Cells Cellular Stress Chemicals Chromatin Complementary DNA Complex Complex Mixtures DNA DNA Damage Data Analyses Dependence Detection Development Digestion Disease Doxorubicin Drosophila genus Event Evolution Gene Expression Gene Expression Regulation Genes Guanosine Health Heat-Shock Response Human Individual Ions Kinetics Label Length Life Link Mass Spectrum Analysis Medicine Messenger RNA Metabolic Metals Methodology Methods Modeling Modification Monitor Mutation Nucleotides Organism Paralysed Pathway interactions Play Population Population Heterogeneity Positioning Attribute Post-Transcriptional RNA Processing Primer Extension Principal Investigator Process Protein Isoforms Protocols documentation Pseudouridine RNA RNA chemical synthesis RNA-Directed DNA Polymerase RNase P Repetitive Sequence Reporting Role Sampling Site Sodium Channel Specificity Structure System Technology Testing Thiouridine Time Tissues Training Transcript Transfer RNA Translations Validation Variant cDNA Library cofactor computational suite epitranscriptomics implementation facilitation indexing interest machine learning algorithm methyl group nuclease performance tests preservation programs reference genome sequencing platform single-cell RNA sequencing transcriptome transcriptome sequencing transcriptomics viral RNA voltage

项目摘要

Project Summary The discovery and characterization of an efficient, ultraprocessive reverse transcriptase (MarathonRT) now makes it possible to develop high-throughput methods for accurate end-to- end sequencing of long RNA transcripts, thereby preserving information content on alternative splicing, editing and modification isoforms while conserving positional linkage information, thereby enabling one to distinguish RNA isoforms in complex mixtures without mapping to a reference genome. This type of technology is essential for deciphering the role of post- transcriptional RNA processing events during control of developmental stage, cell and tissue specificity and regulation of gene expression in higher organisms. It must be sufficiently efficient and accurate to power the long-read sequencing approaches that are used in single- cell RNAseq, particularly when transcript diversification is monitored as a function of time. The first two aims of the proposal are focused on high-throughput detection of RNA modifications (such as 2-O-methyl groups and N7-methyl guanosines). In the first aim, a unique MarathonRT primer extension protocol will be combined with a trained mutational profiling algorithm to recognize the positions and chemical identities of specific RNA modifications, reporting a modification signature that can be recognized at high throughput during long-read sequencing (MRT-ModSeq). In the second aim, MRT-ModSeq will be tested on unknown RNAs, where it will be used to predict sites of modifications on challenging long transcripts and robustness of the predictions will be directly evaluated using mass spectrometry. The second half of the proposal is focused on identification of linked alternative splicing and editing sites on long transcripts within complex cellular mixtures. In aim 3, MarathonRT will be incorporated into a workflow for accurately profiling the relative abundance and processing diversity of the highly complex paralytic (para) gene, which encodes more than 1 million possible processing variants, a subset of which are essential for the voltage-gating of a sodium channel. This sets the stage for Aim 4, in which sensitivity of the workflow must be further optimized and merged with data analysis strategies suitable for time-resolved single cell applications. The resulting method will be tested by monitoring full-length transcriptomic signatures induced by cell stress.

项目总结