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bioRxiv · 10.1101/2020.11.30.400010

NGScloud2: optimized bioinformatic analysis using Amazon Web Services

Mora-Marquez, F.·Vazquez-Poletti, J. L.·Lopez de Heredia, U.
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Abstract

NGScloud was a bioinformatic system developed to perform de novo RNAseq analysis of non-model species by exploiting the cloud computing capabilities of Amazon Web Services. The rapid changes undergone in the way this cloud computing service operates, along with the continuous release of novel bioinformatic applications to analyze next generation sequencing data, have made the software obsolete. NGScloud2 is an enhanced and expanded version of NGScloud that permits the access to ad hoc cloud computing infrastructure, scaled according to the complexity of each experiment. NGScloud2 presents major technical improvements, such as the possibility of running spot instances and the most updated AWS instances types, that can lead to significant cost savings. As compared to its initial implementation, this improved version updates and includes common applications for de novo RNAseq analysis, and incorporates tools to operate workflows of bioinformatic analysis of reference-based RNAseq, RADseq and functional annotation. NGScloud2 optimizes the access to Amazons large computing infrastructures to easily run popular bioinformatic software applications, otherwise inaccessible to non-specialized users lacking suitable hardware infrastructures. The correct performance of the pipelines for de novo RNAseq, reference-based RNAseq, RADseq and functional annotation was tested with real experimental data. NGScloud2 code, instructions for software installation and use are available at https://github.com/GGFHF/NGScloud2. NGScloud2 includes a companion package, NGShelper that contains python utilities to post-process the output of the pipelines for downstream analysis at https://github.com/GGFHF/NGShelper.

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Mora-Marquez, F., Vazquez-Poletti, J. L., Lopez de Heredia, U.. 2020-12-01. NGScloud2: optimized bioinformatic analysis using Amazon Web Services. https://doi.org/10.1101/2020.11.30.400010

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