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J Biotechnol. 2017 Nov 10;261:85-96. doi: 10.1016/j.jbiotec.2017.06.1203. Epub 2017 Jul 1.

Customized workflow development and data modularization concepts for RNA-Sequencing and metatranscriptome experiments.

Author information

1
Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany.
2
Department of Systems Biology & Bioinformatics, University of Rostock, Ulmenstr. 69, 18057 Rostock, Germany.
3
Transcriptome Bioinformatics Group, LIFE Research Complex, University Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany.
4
Department of Systems Biology & Bioinformatics, University of Rostock, Ulmenstr. 69, 18057 Rostock, Germany; Stellenbosch Institute of Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, 7602 Stellenbosch, South Africa.
5
Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany. Electronic address: wolfgang.hess@biologie.uni-freiburg.de.

Abstract

RNA-Sequencing (RNA-Seq) has become a widely used approach to study quantitative and qualitative aspects of transcriptome data. The variety of RNA-Seq protocols, experimental study designs and the characteristic properties of the organisms under investigation greatly affect downstream and comparative analyses. In this review, we aim to explain the impact of structured pre-selection, classification and integration of best-performing tools within modularized data analysis workflows and ready-to-use computing infrastructures towards experimental data analyses. We highlight examples for workflows and use cases that are presented for pro-, eukaryotic and mixed dual RNA-Seq (meta-transcriptomics) experiments. In addition, we are summarizing the expertise of the laboratories participating in the project consortium "Structured Analysis and Integration of RNA-Seq experiments" (de.STAIR) and its integration with the Galaxy-workbench of the RNA Bioinformatics Center (RBC).

KEYWORDS:

Metatranscriptomics; Regulatory RNA; Transcriptomics; Workflow development

PMID:
28676233
DOI:
10.1016/j.jbiotec.2017.06.1203
[Indexed for MEDLINE]
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