![]() KNIME hinkt in diesen beiden Aspekten hinterher, glänzt jedoch auf anderen Ebenen, wie z.B. Zusammenfassend bietet Galaxy derzeit die größte Anzahl an Analysetools für RNA-seq, während CLC die intuitivste Visualisierung bietet. Vier verschiedene Workflows zur RNA-seq Datenanalyse wurden in allen drei WFMS erfolgreich erstellt. Methoden: Diese Werkzeuge wurden anhand einer Vielzahl von Kriterien verglichen, von der Installation bis zur Ausführung und Freigabe von Workflows. 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). 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. Oft werden dafür Ad-hoc-Skripte verwendet. Rohdaten von RNA-seq durchlaufen typischerweise eine mehrstufige, computergestützte Pipeline, um aus solchen Messungen eine Bedeutung abzuleiten. RNA-Sequenzierung (RNA-seq) ermöglicht die qualitative und quantitative Analyse der RNA-Expression. Hintergrund: Transkriptionelle Veränderungen sind Kennzeichen von Entwicklung und Krankheit. RNA sequencing, RNA-seq, data analysis workflow, workflow management system Finally, we share the WFs in the hope of reducing the use of ad hoc scripts and that sharing them will lead to the development of best practices for RNA-seq data analysis. The level of expertise with these WFMS should be taken into account during the WFMS selection. In short, RNA-seq is currently best performed using Galaxy, followed by CLC, and KNIME. We further performed an in-depth analysis of challenges using the three WFMS and provide decision support for which WFMS to use in RNA-seq analysis. These differences entailed disparate results, which were further sensitive to processing settings leading to different biological interpretations in the worst case. While it was possible to construct RNA-seq analysis WFs with all three WFMS tools, the constructed WFs are different. Results: Since we already decided on the three WMFS, many of the criteria we suggest for WFMS evaluation do not apply to our situation and we focus on the WF creation here. KNIME lags behind in these two aspects but excels at other levels, such as machine learning. In summary, Galaxy currently provides the most significant number of analysis tools for RNA-seq, while CLC offers the most intuitive visualization. Four different workflows (WFs) performing RNA-seq data analysis were successfully constructed in all three WFMS. Methods: These tools were compared using a variety of criteria ranging from installation to workflow execution and sharing. 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). 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. Often ad hoc scripts are used for such analyses. Raw RNA-seq data passes through a multi-step computational pipeline to derive meaning from such measurements. RNA sequencing (RNA-seq) allows qualitative and quantitative RNA expression analysis. Background: Transcriptional changes are hallmarks of development and disease. ![]()
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