DNAscan: a fast, computationally and memory efficient bioinformatics pipeline for the analysis of DNA next-generation-sequencing data [article]

Alfredo Iacoangeli, Ahmad Al Khleifat, William Sproviero, Aleksey Shatunov, Ashley Jones, Richard Dobson, Stephen J Newhouse, Ammar Al-Chalabi
2018 bioRxiv   pre-print
The generation of DNA Next Generation Sequencing (NGS) data is a commonly applied approach for studying the genetic basis of biological processes, including diseases, and underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. A huge number of bioinformatics tools exist and it is therefore challenging to design an analysis pipeline; NGS analysis is computationally intensive, requiring expensive infrastructure which can be
more » ... h can be problematic given that many medical and research centres do not have adequate high performance computing facilities and the use of cloud computing facilities is not always possible due to privacy and ownership issues. We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. We achieved this by exploiting state-of-the-art bioinformatics tools. DNAscan can analyse raw, 40x whole genome NGS data in 8 hours, using as little as 8 threads and 16 Gbs of RAM, while guaranteeing a high performance. DNAscan can look for SNVs, small indels, SVs, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases including ClinVar, Exac and dbSNP, and a local deployment of the gene.iobio platform is available for an on-the-fly result visualisation.
doi:10.1101/267195 fatcat:jeawn3cfbnfobcxe2ikxq4uqpa