Challenges for the development of automated RNA-seq analyses pipelines

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Abstract: Background: Transcriptional changes are hallmarks of development and disease. RNA sequencing (RNA-seq) allows qualitative and quantitative RNA expression analysis. Raw RNA-seq data passes through a multi-step computational pipeline to derive meaning from such measurements. Often ad hoc scripts are used for such analyses. However, the use of workflow management systems (WFMS) should be encouraged in order to enhance result reproducibility, to establish best data analysis practices, and to share such data analysis workflows. In this work, we created RNA-seq data analysis workflows in three WFMS, namely Galaxy (free, open-source), KNIME (free, commercial, and partially open source), and CLC (commercial, closed source). Methods: These tools were compared using a variety of criteria ranging from installation to workflow execution and sharing. Four different workflows (WFs) performing RNA-seq data analysis were successfully constructed in all three WFMS. In summary, Galaxy currently ...
Abstract: Hintergrund: Transkriptionelle Veränderungen sind Kennzeichen von Entwicklung und Krankheit. RNA-Sequenzierung (RNA-seq) ermöglicht die qualitative und quantitative Analyse der RNA-Expression. Rohdaten von RNA-seq durchlaufen typischerweise eine mehrstufige, computergestützte Pipeline, um aus solchen Messungen eine Bedeutung abzuleiten. Oft werden dafür Ad-hoc-Skripte verwendet. Allerdings sollte die Verwendung von Workflow-Management-Systemen (WFMS) gefördert werden, um die Reproduzierbarkeit von Ergebnissen zu verbessern, bewährte Datenanalyseverfahren zu etablieren und solche Workflows zur Datenanalyse zu teilen. In dieser Arbeit haben wir RNA-seq Datenanalyse-Workflows in drei WFMS erstellt, namentlich: Galaxy (kostenlos, Open Source), KNIME (kostenlos, kommerziell und teilweise Open Source) und CLC (kommerziell, Closed Source).Methoden: Diese Werkzeuge wurden anhand einer Vielzahl von Kriterien verglichen, von der Installation bis zur Ausführung und Freigabe von Workflows. ...

Alternative title: Herausforderungen bei der Entwicklung automatisierter RNA-seq Analyse-Pipelines

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Challenges for the development of automated RNA-seq analyses pipelines ; volume:19 ; day:04 ; month:07 ; year:2023
GMS Medizinische Informatik, Biometrie und Epidemiologie ; 19 (04.07.2023)

Classification: Medizin, Gesundheit

Creator: Beukers, Matthieu
Allmer, Jens

DOI: 10.3205/mibe000245

URN: urn:nbn:de:0183-mibe0002457

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:59 AM CEST

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Beukers, Matthieu
Allmer, Jens

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Challenges for the development of automated RNA-seq analyses pipelines

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Other Objects (12)

A benchmark for RNA-seq quantification pipelines

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads

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Automated Isoform Diversity Detector (AIDD): a pipeline for investigating transcriptome diversity of RNA-seq data

DEgenes Hunter - A Flexible R Pipeline for Automated RNA-seq Studies in Organisms without Reference Genome

SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses

A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery

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Automated identification of reference genes based on RNA-seq data

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Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads

ARPIR: automatic RNA-Seq pipelines with interactive report

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ARPIR: automatic RNA-Seq pipelines with interactive report

A pipeline for RNA-seq based eQTL analysis with automated quality control procedures

Erratum to: A benchmark for RNA-seq quantification pipelines

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