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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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Related Experiment Video

Updated: May 5, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

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AutoRNAseq: Automated Bulk RNA-seq Analysis Pipeline.

Josh Loecker1, Brandt Bessell1, Bhanwar Lal Puniya1

  • 1Department of Biochemistry, University of Nebraska-Lincoln.

Biorxiv : the Preprint Server for Biology
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

AutoRNAseq provides a reproducible, end-to-end workflow for bulk RNA sequencing (RNA-seq) analysis. This automated pipeline simplifies data processing, ensuring consistent gene quantification across experiments with minimal user input.

Keywords:
RNA-seqSnakemakepipelinereproducibilityworkflow

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput RNA sequencing (RNA-seq) generates vast amounts of data annually.
  • The increasing volume of RNA-seq data necessitates reproducible and consistent analysis pipelines.
  • Existing workflows often require complex user coordination and pre-configuration of reference data.

Purpose of the Study:

  • To develop an automated, end-to-end workflow for bulk RNA sequencing data analysis.
  • To address the need for reproducible RNA-seq data processing across diverse experiments.
  • To simplify the analysis pipeline, reducing user intervention and pre-configuration requirements.

Main Methods:

  • Implementation of a Snakemake-based workflow named AutoRNAseq.
  • Automation of data retrieval, quality control, alignment, and gene quantification.
  • Integration of automated reference data preparation.

Main Results:

  • AutoRNAseq offers a single, unified workflow for comprehensive RNA-seq analysis.
  • The workflow automates critical steps from data acquisition to gene quantification.
  • Minimal user intervention is required, enhancing reproducibility and efficiency.

Conclusions:

  • AutoRNAseq provides a robust solution for consistent bulk RNA-seq data processing.
  • The workflow is applicable to various research domains, including bioinformatics and drug-response studies.
  • This tool enhances the accessibility and reliability of RNA-seq data analysis.