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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).
This is an abstract which has been accepted for the BBCC2018 Conference.
In v2 we removed only the Type of Presentation for the conference.