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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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A pipeline for RNA-seq based eQTL analysis with automated quality control procedures.

Tao Wang1,2, Yongzhuang Liu2, Junpeng Ruan1

  • 1School of Computer Science, Northwestern Polytechnical University, 1 Dongxiang Road, Chang'an District, Xi'an, China.

BMC Bioinformatics
|August 26, 2021
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Summary

This study introduces eQTLQC, a user-friendly pipeline for expression quantitative trait loci (eQTL) analysis. It automates data preprocessing and quality control for genetic variant and gene expression data, simplifying complex eQTL studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Expression quantitative trait loci (eQTL) studies link genetic variants to gene expression, aiding disease mechanism research.
  • Challenges exist in managing and analyzing heterogeneous eQTL data, especially for researchers with limited computational expertise.
  • A need for automated, user-friendly tools for eQTL data processing and analysis is evident.

Purpose of the Study:

  • To develop an automated and user-friendly computational pipeline for eQTL analysis.
  • To address the challenges of quality control and normalization for multi-source eQTL raw data.
  • To facilitate eQTL mapping by simplifying data preprocessing.

Main Methods:

  • Developed the eQTLQC pipeline with automated preprocessing for genotype and gene expression data.
  • Integrated quality control and normalization methods within the pipeline.
  • Employed automated techniques to minimize manual intervention in the analysis workflow.

Main Results:

  • The eQTLQC pipeline automates data preprocessing, quality control, and normalization for eQTL analysis.
  • Demonstrated the pipeline's utility and robustness through case studies using real-world RNA-seq and whole genome sequencing (WGS) genotype data.
  • Successfully performed eQTL mapping on diverse datasets.

Conclusions:

  • eQTLQC offers a reliable computational workflow for comprehensive eQTL analysis.
  • The pipeline standardizes quality control, normalization, and eQTL mapping for various raw data formats.
  • Source code, demo data, and instructions are publicly available for accessibility and reproducibility.