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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: Mar 15, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown.

Mihaela Pertea1,2, Daehwan Kim1, Geo M Pertea1

  • 1Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.

Nature Protocols
|August 26, 2016
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Summary

This study introduces HISAT, StringTie, and Ballgown, free software tools for analyzing RNA sequencing (RNA-seq) data. These tools efficiently process complex RNA-seq datasets to identify gene expression levels and differential gene expression.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing of mRNA (RNA-seq) is a standard for gene expression analysis.
  • RNA-seq generates large, complex datasets requiring efficient software solutions.
  • Existing methods may lack speed, accuracy, or flexibility for comprehensive analysis.

Purpose of the Study:

  • To present a comprehensive protocol for RNA-seq data analysis using free, open-source software.
  • To enable accurate alignment, transcript assembly, and differential gene expression analysis.
  • To provide scientists with tools for efficient processing of large RNA-seq datasets.

Main Methods:

  • Utilized HISAT (hierarchical indexing for spliced alignment of transcripts) for read alignment to the genome.
  • Employed StringTie for transcript assembly, including novel splice variants.
  • Used Ballgown for computing transcript abundance and identifying differentially expressed genes.

Main Results:

  • Developed a protocol integrating HISAT, StringTie, and Ballgown for end-to-end RNA-seq analysis.
  • Demonstrated the ability to align reads, assemble transcripts, quantify abundance, and detect differential expression.
  • Achieved processing of large RNA-seq datasets in under 45 minutes of computer time.

Conclusions:

  • HISAT, StringTie, and Ballgown offer a fast, accurate, and flexible solution for comprehensive RNA-seq analysis.
  • The integrated protocol facilitates the identification of gene transcripts, expression levels, and differentially expressed genes.
  • These open-source tools empower scientists to efficiently analyze complex genomic data.