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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data.

Sean K Simmons1,2,3, Xian Adiconis1,2,3, Nathan Haywood1,2,3

  • 1Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA.

Biorxiv : the Preprint Server for Biology
|August 26, 2024
PubMed
Summary
This summary is machine-generated.

Single-nucleus RNA sequencing (snRNA-seq) enhances allele-specific expression (ASE) analysis by leveraging intronic reads. This approach offers greater power than eQTL analysis for identifying disease-associated genetic variants.

Keywords:
Parkinson’s diseaseRNA-seqSingle-cellallele-specific expressionvariant to function

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding gene function.
  • Allele-specific expression (ASE) analysis at the single-cell level reveals how genomic variation impacts RNA expression.
  • Intronic regions in single-nucleus RNA-Seq (snRNA-seq) are enriched for genetic variants, suggesting potential for improved ASE analysis.

Purpose of the Study:

  • To optimize experimental and computational strategies for single-cell allele-specific expression (ASE) analysis.
  • To investigate the utility of snRNA-seq for detecting ASE, particularly in intronic regions.
  • To develop and apply novel computational tools for ASE analysis in scRNA-seq and snRNA-seq data.

Main Methods:

  • Exploration of experimental factors (RNA source, read length, sequencing depth, genotyping) influencing ASE analysis power.
  • Development of a computational toolkit for processing and analyzing scRNA-seq and snRNA-seq data for ASE.
  • Utilizing intronic reads and optimizing read length for enhanced ASE detection.
  • Application of hybrid selection to improve detection of allelic imbalance.
  • Analysis of allele-specific isoform expression from long- and short-read snRNA-seq data.

Main Results:

  • Intronic reads from snRNA-seq provide more ASE information than exonic reads.
  • Optimizing read length and employing hybrid selection significantly increased the power to detect allelic imbalance.
  • The developed computational tools effectively processed and analyzed ASE from scRNA-seq and snRNA-seq.
  • ASE analysis demonstrated higher power than eQTL analysis in identifying significant SNP/gene pairs in a Parkinson's disease cohort.

Conclusions:

  • snRNA-seq, particularly utilizing intronic regions, is a powerful method for single-cell ASE analysis.
  • The developed end-to-end experimental and computational approach enhances the study of genetic variation's impact on gene expression at the single-cell level.
  • This methodology offers improved power for identifying disease-associated genetic variants compared to traditional eQTL analysis.