Abstract
Life science has entered the so-called 'big data era' where biologists, clinicians and bioinformaticians are overwhelmed with high-throughput sequencing data. While they offer new insights to decipher the genome structure they also raise major challenges to use them for daily clinical practice care and diagnosis purposes as they are bigger and bigger. Therefore, we implemented a software to reduce the time to delivery for the alignment and the sorting of high-throughput sequencing data. Our solution is implemented using Message Passing Interface and is intended for high-performance computing architecture. The software scales linearly with respect to the size of the data and ensures a total reproducibility with the traditional tools. For example, a 300X whole genome can be aligned and sorted within less than 9 hours with 128 cores. The software offers significant speed-up using multi-cores and multi-nodes parallelization.
Highlights
Life science has entered the so-called 'big data era' where biologists, clinicians and bioinformaticians are overwhelmed with highthroughput sequencing data
As we have entered the era of genomic medicine, delivering the results to the clinicians within a short delay to guide the therapeutic decision is a challenge of the utmost importance in daily clinical practice
A typical bioinformatics workflow to analyze high-throughput sequencing (HTS) data consists of a set of systematic steps of pre-processing to i) align the sequencing reads on a reference genome and ii) to sort the alignments according to their coordinates on the genome
Summary
The most recent generation of sequencers can produce terabytes of data each day and we expect this exponential growth of the sequencing to continue This data tsunami raises many challenges, from data management to data analysis, requiring an efficient high-performance computing architecture (Lightbody et al, 2019). These steps are very time consuming (up to several days for whole genome analysis) as they suffer from bottlenecks at the CPU, IO and memory levels Removing these bottlenecks would make it possible to reduce the time-to-delivery of the results such that they could be available within a reasonable delay when very large data are produced by the sequencers. This allows an efficient distribution of the workload over the available resources of the supercomputers providing the expected scalability
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