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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: Nov 24, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
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ARPIR: automatic RNA-Seq pipelines with interactive report.

Giulio Spinozzi1, Valentina Tini2, Alessia Adorni2

  • 1Department of Medicine, Section of Hematology, University of Perugia, Perugia, Italy. giulio.spinozzi@unipg.it.

BMC Bioinformatics
|December 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces ARPIR, an automated pipeline for RNA-Seq analysis, simplifying complex bioinformatics workflows. ARPIR optimizes RNA sequencing data analysis, offering efficiency and ease of use for researchers without extensive computational expertise.

Keywords:
BioinformaticsDEAGene ontologyGenomicsPathwaysPipelinesRNA-seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequencing (RNA-Seq) is a crucial method for analyzing both coding and non-coding RNA expression.
  • Existing RNA-Seq analysis tools are numerous, employ diverse algorithms, and require significant computational expertise, making multi-step analyses time-consuming.
  • The need for an automated, user-friendly pipeline for comprehensive RNA-Seq analysis, from primary to tertiary steps, is evident.

Purpose of the Study:

  • To develop an automated pipeline, ARPIR, for streamlined RNA-Seq data analysis.
  • To evaluate and compare eight different RNA-Seq analysis pipelines based on RAM usage, processing time, sensitivity, and specificity.
  • To provide a flexible tool that allows users to select preferred analysis components.

Main Methods:

  • Comparison of eight RNA-Seq analysis pipelines, combining popular tools for alignment, quantification, and differential analysis.
  • Development of ARPIR, an automated pipeline that defaults to the highest-performing combination (HISAT2, featureCounts, edgeR) but allows user customization.
  • Implementation of a user-friendly interface (graphical or script-based) and automated installation via a configuration script.

Main Results:

  • The optimal pipeline identified for speed, low RAM usage, and high sensitivity uses HISAT2 for alignment, featureCounts for quantification, and edgeR for differential analysis.
  • ARPIR was developed, incorporating this optimal pipeline by default while offering flexibility to choose alternative tools.
  • The pipeline enables multiple comparisons for different treatment groups in a single run and supports both paired-end and single-end RNA-Seq data.

Conclusions:

  • ARPIR provides an efficient and user-friendly solution for RNA-Seq analysis, covering quality control through tertiary analyses like Gene Ontology and Pathway analysis.
  • The pipeline offers flexibility in tool selection and can be launched via a graphical interface or script, accommodating users with varying computational skills.
  • Results are presented through an interactive Shiny App and can be exported into comprehensive reports (PDF, Word, HTML).