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RNA-seq

RNA-seq

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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Updated: Jun 9, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.

Mei Ge¹, Chenyu Li¹, Zhiyan Zhang¹

¹National Key Laboratory for Swine Genetic Improvement and Germplasm Innovation Technology, Jiangxi Agricultural University, Nanchang 330045, China.

Animals : an Open Access Journal From MDPI

|October 26, 2024

Summary

This summary is machine-generated.

RNA-sequencing enables expression quantitative trait locus (eQTL) mapping using single nucleotide polymorphisms (SNPs) and kmer-based methods. This study identified 87 eQTLs, validating 23/30 associations between SNP and kmer approaches.

Keywords:

RNA-seq SNP eQTL haplotype block kmer

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Last Updated: Jun 9, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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Published on: November 11, 2014

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Published on: March 5, 2022

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Traditional expression quantitative trait locus (eQTL) mapping relies on single nucleotide polymorphisms (SNPs) from whole-genome sequencing (WGS), which is costly.
RNA-sequencing (RNA-seq) offers a cost-effective alternative, providing both gene expression and SNP data simultaneously.
eQTL analysis using RNA-seq data presents significant potential for genetic association studies.

Purpose of the Study:

To evaluate and compare two eQTL mapping strategies using RNA-seq data: SNP-based analysis and kmer-based analysis.
To identify novel eQTLs and associated genes using these RNA-seq based methods.
To assess the concordance and efficacy of SNP-based versus kmer-based eQTL identification.

Main Methods:

SNP-based eQTL analysis: Associated gene expression levels with variant sites identified directly from RNA-seq data.
Kmer-based eQTL analysis: Utilized kmer genotypes derived from RNA-seq reads to represent variant sites and associated them with gene expression.
Haplotype block analysis: Employed to validate overlapping association signals between the two eQTL methods.

Main Results:

Identified 87 significant association signals for eQTLs using the SNP-based approach.
Notable eGenes identified include DYNLT1, NMNAT1, and MRLC2, implicated in neurological functions, cellular metabolism, and muscle contraction.
Demonstrated high concordance between SNP-based and kmer-based eQTL findings, with 23 out of 30 overlapping signals validated by haplotype analysis.

Conclusions:

RNA-seq data can be effectively utilized for eQTL mapping through both SNP and kmer-based approaches.
The kmer-based method offers a complementary strategy for eQTL discovery, showing substantial agreement with traditional SNP-based methods.
This study provides valuable insights into the strengths and limitations of RNA-seq based eQTL analysis, enhancing genetic association study methodologies.