TRAPLINE: A standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation

Wolfien, Markus, Rimmbach, Christian, Schmitz, Ulf, Jung, Julia Jeannine, Krebs, Stefan, Steinhoff, Gustav, David, Robert, and Wolkenhauer, Olaf (2016) TRAPLINE: A standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation. BMC Bioinformatics, 17. 21.

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Technical advances in Next Generation Sequencing (NGS) provide a means to acquire deeper insights into cellular functions. The lack of standardized and automated methodologies poses a challenge for the analysis and interpretation of RNA sequencing data. We critically compare and evaluate state-of-the-art bioinformatics approaches and present a workflow that integrates the best performing data analysis, data evaluation and annotation methods in a Transparent, Reproducible and Automated PipeLINE (TRAPLINE) for RNA sequencing data processing (suitable for Illumina, SOLiD and Solexa).


Comparative transcriptomics analyses with TRAPLINE result in a set of differentially expressed genes, their corresponding protein-protein interactions, splice variants, promoter activity, predicted miRNA-target interactions and files for single nucleotide polymorphism (SNP) calling. The obtained results are combined into a single file for downstream analysis such as network construction. We demonstrate the value of the proposed pipeline by characterizing the transcriptome of our recently described stem cell derived antibiotic selected cardiac bodies ('aCaBs').


TRAPLINE supports NGS-based research by providing a workflow that requires no bioinformatics skills, decreases the processing time of the analysis and works in the cloud. The pipeline is implemented in the biomedical research platform Galaxy and is freely accessible via or the specific Galaxy manual page (

Item ID: 68992
Item Type: Article (Research - C1)
ISSN: 1471-2105
Copyright Information: © 2016 Wolfien et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
Date Deposited: 08 Jun 2022 02:59
FoR Codes: 31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310201 Bioinformatic methods development @ 100%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 100%
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