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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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Related Experiment Video

Updated: Apr 23, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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RNA-Seq alignment to individualized genomes improves transcript abundance estimates in multiparent populations.

Steven C Munger1, Narayanan Raghupathy1, Kwangbom Choi1

  • 1The Jackson Laboratory, Bar Harbor, Maine 04609.

Genetics
|September 20, 2014
PubMed
Summary

Genetic variants can cause errors in RNA sequencing analysis. Seqnature software creates individualized genomes, improving read mapping accuracy and transcript abundance estimation for genetically diverse populations.

Keywords:
Diversity Outbred (DO)Diversity Outbred miceMPPMultiparent Advanced Generation Inter-Cross (MAGIC)Multiparental populationsQTL mappingRNA-seqexpression QTLhaplotype reconstructionhigh-density genotypingmixed models

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Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • RNA sequencing (RNA-seq) is crucial for understanding transcriptional regulation.
  • Accurate alignment of sequencing reads to a reference genome is a critical first step.
  • Genetic variations can lead to misaligned reads and biased transcript abundance estimates.

Purpose of the Study:

  • To develop a novel software tool and analysis pipeline for accurate RNA-seq data analysis in genetically diverse populations.
  • To address the limitations of reference-based alignment in the presence of genetic variants.
  • To improve the accuracy of transcript abundance estimation and allele-specific expression analysis.

Main Methods:

  • Developed Seqnature software to construct individualized diploid genomes and transcriptomes.
  • Implemented a comprehensive analysis pipeline integrating existing bioinformatics tools.
  • Validated the approach using simulated and real RNA-seq datasets from multiparent populations.

Main Results:

  • Alignment to individualized transcriptomes significantly increased read mapping accuracy.
  • Improved estimation of transcript abundance and enabled direct assessment of allele-specific expression.
  • Corrected false-positive linkage signals and revealed previously hidden associations in expression QTL mapping.

Conclusions:

  • Individualized diploid genome alignment is superior to reference-based alignment for RNA-seq in genetically diverse populations.
  • Seqnature software and the proposed pipeline enhance the reliability of RNA-seq data analysis.
  • This approach is recommended for all high-throughput sequencing applications in populations with genetic variation.