Abstract
BackgroundRNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity. The automatic detection of RNA-editing from RNA-seq data is computational intensive and limited to small data sets, thus preventing a reliable genome-wide characterisation of such process.ResultsIn this work we introduce HPC-REDItools, an upgraded tool for accurate RNA-editing events discovery from large dataset repositories. Availability: https://github.com/BioinfoUNIBA/REDItools2.ConclusionsHPC-REDItools is dramatically faster than the previous version, REDItools, enabling big-data analysis by means of a MPI-based implementation and scaling almost linearly with the number of available cores.
Highlights
RNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity
Data loading optimization To experimentally test the speed improvement between High Performance Computing (HPC)-REDItools and REDItools, we created a dataset consisting of 10 RNA-seq samples randomly selected from the Genotype-Tissue Expression project (GTEx) project
HPC-REDItools are on average 8 times faster than REDItools. This finding is quite interesting because enables the use of HPC-REDItools to users with no access to HPC infrastructures and greatly speeds up the genome wide RNA editing detection
Summary
RNA editing is a co-/post-transcriptional phenomenon occurring in many organisms including animals and plants and has relevant biological implications. It can be detected employing RNA-seq data generated by high throughput sequencing technologies. As data volume increases, more powerful tools are required to analyse large number of samples in a time affordable way. In the present work we described HPC-REDItools, a HPC-aware tool for efficiently detect high-quality RNA-editing events from big data repositories on a HPC cluster. HPC-REDItools introduce at least three main algorithmic improvements over the previous version: i) high parallelism to employ the computational power available at High Performance Computing infrastructures; ii) optimised data loading that dramatically reduces computing time per genomic interval; iii) Dynamic Interval Analysis approach to improve workload balance across parallel processes.
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