A comprehensive RNA-Seq pipeline includes meta-analysis, interactivity and automatic reporting
Author and article information
Abstract
There are many methods available for each phase of the RNA-Seq analysis and each of them uses different algorithms. It is therefore useful to identify a pipeline that combines the best tools in terms of time and results. For this purpose, we compared five different pipelines, obtained by combining the most used tools in RNA-Seq analysis. Using RNA-Seq data on samples of different Acute Myeloid Leukemia (AML) cell lines, we compared five pipelines from the alignment to the differential expression analysis (DEA). For each one we evaluated the peak of RAM and time and then compared the differentially expressed genes identified by each pipeline. It emerged that the pipeline with shorter times, lower consumption of RAM and more reliable results, is that which involves the use ofHISAT2for alignment, featureCountsfor quantification and edgeRfor differential analysis. Finally, we developed an automated pipeline that recurs by default to the cited pipeline, but it also allows to choose between different tools. In addition, the pipeline makes a final meta-analysis that includes a Gene Ontology and Pathway analysis. The results can be viewed in an interactive Shiny Appand exported in a report (pdf, word or html formats).
Cite this as
2018. A comprehensive RNA-Seq pipeline includes meta-analysis, interactivity and automatic reporting. PeerJ Preprints 6:e27317v2 https://doi.org/10.7287/peerj.preprints.27317v2Author comment
This is an abstract which has been accepted for the BBCC2018 Conference.
In v2 we removed only the Type of Presentation for the conference.
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Competing Interests
There are no competing interests
Author Contributions
Giulio Spinozzi conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, performed the computation work, authored or reviewed drafts of the paper, approved the final draft.
Valentina Tini conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, performed the computation work, authored or reviewed drafts of the paper, approved the final draft.
Laura Mincarelli contributed reagents/materials/analysis tools, approved the final draft, rNA-seq experiments.
Brunangelo Falini contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.
Maria Paola Martelli conceived and designed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
Funding
The project is funded by the European Research Council (ERC) to Maria Paola Martelli (ContraNPM1AML) and by CINECA (HP10CZ1LLU) to Giulio Spinozzi. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
