Neurofibromatosis type I, a genetic disorder due to mutations in the NF1 gene, is characterized by a high mutation rate (about 50% of the cases are de novo) but, with the exception of whole gene deletions associated with a more severe phenotype, no specific hotspots and few solid genotype/phenotype correlations. After retrospectively re-evaluating all NF1 gene variants found in the diagnostic activity, we studied 108 patients affected by neurofibromatosis type I who harbored mutations that had not been previously reported in the international databases, with the aim of analyzing their type and distribution along the gene and of correlating them with the phenotypic features of the affected patients. Out of the 108 previously unreported variants, 14 were inherited by one of the affected parents and 94 were de novo. Twenty-nine (26.9%) mutations were of uncertain significance, whereas 79 (73.2%) were predicted as pathogenic or probably pathogenic. No differential distribution in the exons or in the protein domains was observed and no statistically significant genotype/phenotype correlation was found, confirming previous evidences.
Background
RNA-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.
Results
The 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.
Conclusions
ARPIR 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.
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