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Related Concept Videos

RNA-seq03:21

RNA-seq

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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. 
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Towards mouse genetic-specific RNA-sequencing read mapping.

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  • 1Centre for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.

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|September 26, 2022
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Summary
This summary is machine-generated.

Ignoring genetic variations in RNA-sequencing (RNA-seq) skews results. Using custom references for mouse genetic populations improves transcriptome and expression quantitative trait locus (eQTL) analyses, highlighting the need for better genome references.

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Area of Science:

  • Genomics and Transcriptomics
  • Bioinformatics and Computational Biology
  • Systems Biology

Background:

  • Genetic variations influence complex traits and diseases, but their impact on molecular phenotypes like the transcriptome is not fully understood.
  • Standard RNA-sequencing (RNA-seq) analysis often overlooks sample-specific genetic variations, potentially leading to biased transcriptome estimates.
  • Biased transcriptome data can negatively affect downstream analyses, such as expression quantitative trait locus (eQTL) detection.

Purpose of the Study:

  • To assess the impact of reference-based analysis on transcriptome and eQTL detection in the BXD mouse genetic panel.
  • To identify and address reference bias in RNA-seq data analysis for model organisms.
  • To propose and evaluate practical solutions for improving transcriptome and eQTL analyses by incorporating genetic variants.

Main Methods:

  • Comparative analysis of RNA-seq data using standard genome references versus custom references tailored to the BXD mouse panel.
  • Integration of genetic variants, genotypes, and genome reference sequences to create improved analytical pipelines.
  • Evaluation of downstream eQTL detection accuracy using both standard and custom reference approaches.

Main Results:

  • Significant reference bias was identified in standard RNA-seq analysis pipelines for the BXD mouse panel.
  • The use of custom BXD line-specific references demonstrably improved transcriptome estimates and downstream eQTL analysis.
  • Incorporating genetic variation information into reference sequences enhances the accuracy of transcriptomic analyses.

Conclusions:

  • Standard genome references introduce bias in RNA-seq analysis, impacting the study of genetic variation and complex traits.
  • Customized genome references that account for specific genetic variations in model populations are crucial for accurate transcriptomic studies.
  • Reassessing and improving genome references is essential for advancing genetic research, particularly in studies combining transcriptomics and genetics.