Abstract
BackgroundRNA-Seq is an increasing used methodology to study either coding and non-coding RNA expression. There are many software tools available for each phase of the RNA-Seq analysis and each of them uses different algorithms. Furthermore, the analysis consists of several steps regarding alignment (primary-analysis), quantification, differential analysis (secondary-analysis) and any tertiary-analysis and can therefore be time-consuming to deal with each step separately, in addition to requiring a computer knowledge. For this reason, the development of an automated pipeline that allows the entire analysis to be managed through a single initial command and that is easy to use even for those without computer skills can be useful. Faced with the vast availability of RNA-Seq analysis tools, it is first of all necessary to select a limited number of pipelines to include. For this purpose, we compared eight pipelines obtained by combining the most used tools and for each one we evaluated peak of RAM, time, sensitivity and specificity.ResultsThe pipeline with shorter times, lower consumption of RAM and higher sensitivity is the one consisting in HISAT2 for alignment, featureCounts for quantification and edgeR for differential analysis. Here, we developed ARPIR, an automated pipeline that recurs by default to the cited pipeline, but it also allows to choose, between different tools, those of the pipelines having the best performances.ConclusionsARPIR allows the analysis of RNA-Seq data from groups undergoing different treatment allowing multiple comparisons in a single launch and can be used either for paired-end or single-end analysis. All the required prerequisites can be installed via a configuration script and the analysis can be launched via a graphical interface or by a template script. In addition, ARPIR makes a final tertiary-analysis that includes a Gene Ontology and Pathway analysis. The results can be viewed in an interactive Shiny App and exported in a report (pdf, word or html formats). ARPIR is an efficient and easy-to-use tool for RNA-Seq analysis from quality control to Pathway analysis that allows you to choose between different pipelines.
Highlights
RiboNucleic acid (RNA)-Seq is an increasing used methodology to study either coding and non-coding RNA expression
Developed since the 2000s, it quickly became one of the methods of choice in the study of differential expression in various fields. One of these is the study of tumors and among them the leukemias, including acute myeloid leukemias (AML), where the RNA-Seq is used with increasing frequency either to characterize the disease or for diagnostic and risk assessment prognosis [1]
Evaluating the 29 pipelines cited on Wikipedia [2] we noticed that none met all main requirements that could be of interest for an RNA-Seq analysis
Summary
RNA-Seq is an increasing used methodology to study either coding and non-coding RNA expression. Developed since the 2000s, it quickly became one of the methods of choice in the study of differential expression in various fields One of these is the study of tumors and among them the leukemias, including acute myeloid leukemias (AML), where the RNA-Seq is used with increasing frequency either to characterize the disease or for diagnostic and risk assessment prognosis [1]. For this reason, it becomes important to use with efficiency and simplicity the tools that allow to operate standard analysis, from differential analysis to Pathway and Gene Ontology analyses. We decided to develop our own pipeline that was as complete as possible in dealing with each step
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