Background: Nanopore sequencing enables portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these outcomes requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. However, comparing raw nanopore signals to a biological reference sequence is a computationally complex task. The dynamic programming algorithm called Adaptive Banded Event Alignment (ABEA) is a crucial step in polishing sequencing data and identifying non-standard nucleotides, such as measuring DNA methylation. Here, we parallelise and optimise an implementation of the ABEA algorithm (termed f5c) to efficiently run on heterogeneous CPU-GPU architectures. Results: By optimising memory, computations and load balancing between CPU and GPU, we demonstrate how f5c can perform ∼3-5× faster than an optimised version of the original CPU-only implementation of ABEA in the Nanopolish software package. We also show that f5c enables DNA methylation detection on-the-fly using an embedded System on Chip (SoC) equipped with GPUs. Conclusions: Our work not only demonstrates that complex genomics analyses can be performed on lightweight computing systems, but also benefits High-Performance Computing (HPC). The associated source code for f5c along with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c.
Nanopore sequencing has the potential to revolutionise genomics by realising portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these applications requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. For instance, comparing raw nanopore signals to a biological reference sequence is a computationally complex task despite leveraging a dynamic programming algorithm for Adaptive Banded Event Alignment (ABEA)-a commonly used approach to polish sequencing data and identify non-standard nucleotides, such as measuring DNA methylation. Here, we parallelise and optimise an implementation of the ABEA algorithm (termed f5c) to efficiently run on heterogeneous CPU-GPU architectures. By optimising memory, compute and load balancing between CPU and GPU, we demonstrate how f5c can perform~3-5× faster than the original implementation of ABEA in the Nanopolish software package. We also show that f5c enables DNA methylation detection on-the-fly using an embedded System on Chip (SoC) equipped with GPUs. Our work not only demonstrates that complex genomics analyses can be performed on lightweight computing systems, but also benefits High-Performance Computing (HPC). The associated source code for f5c along with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c.
Radio frequency identification (RFID) has generated vast amounts of interest in the supply chain, logistics, and the manufacturing area. RFID can be used to significantly improve the efficiency of business processes by providing automatic data identification and capture. Enormous data would be collected as items leave a trail of data while moving through different locations. Some important challenges such as false read, data overload, real-time acquisition of data, data security, and privacy must be dealt with. Good quality data is needed because business decisions depend on these data. Other important issues are that business processes must change drastically as a result of implementing RFID, and data must be shared between suppliers and retailers. The main objective of this article is focused on data management challenges of RFID, and it provides potential solutions for each identified risk.
Radio frequency identification (RFID) has generated vast amounts of interest in the supply chain, logistics, and the manufacturing area. RFID can be used to significantly improve the efficiency of business processes by providing automatic data identification and capture. Enormous data would be collected as items leave a trail of data while moving through different locations. Some important challenges such as false read, data overload, real-time acquisition of data, data security, and privacy must be dealt with. Good quality data is needed because business decisions depend on these data. Other important issues are that business processes must change drastically as a result of implementing RFID, and data must be shared between suppliers and retailers. The main objective of this article is focused on data management challenges of RFID, and it provides potential solutions for each identified risk.
Radio frequency identification (RFID) has generated vast amounts of interest in the supply chain, logistics, and the manufacturing area. RFID can be used to significantly improve the efficiency of business processes by providing automatic data identification and capture. Enormous data would be collected as items leave a trail of data while moving through different locations. Some important challenges such as false read, data overload, real-time acquisition of data, data security, and privacy must be dealt with. Good quality data is needed because business decisions depend on these data. Other important issues are that business processes must change drastically as a result of implementing RFID, and data must be shared between suppliers and retailers. The main objective of this article is focused on data management challenges of RFID, and it provides potential solutions for each identified risk.
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