RESUMEN
Genomics has the potential to transform medicine from reactive to a personalized, predictive, preventive, and participatory (P4) form. Being a Big Data application with continuously increasing rate of data production, the computational costs of genomics have become a daunting challenge. Most modern computing systems are heterogeneous consisting of various combinations of computing resources, such as CPUs, GPUs, and FPGAs. They require platform-specific software and languages to program making their simultaneous operation challenging. Existing read mappers and analysis tools in the whole genome sequencing (WGS) pipeline do not scale for such heterogeneity. Additionally, the computational cost of mapping reads is high due to expensive dynamic programming based verification, where optimized implementations are already available. Thus, improvement in filtration techniques is needed to reduce verification overhead. To address the aforementioned limitations with regards to the mapping element of the WGS pipeline, we propose a Cross-platfOrm Read mApper using opencL (CORAL). CORAL is capable of executing on heterogeneous devices/platforms, simultaneously. It can reduce computational time by suitably distributing the workload without any additional programming effort. We showcase this on a quadcore Intel CPU along with two Nvidia GTX 590 GPUs, distributing the workload judiciously to achieve up to 2× speedup compared to when, only, the CPUs are used. To reduce the verification overhead, CORAL dynamically adapts k-mer length during filtration. We demonstrate competitive timings in comparison with other mappers using real and simulated reads. CORAL is available at: https://github.com/nclaes/CORAL.
Asunto(s)
Mapeo Cromosómico/métodos , Genómica/métodos , Secuenciación Completa del Genoma/métodos , Algoritmos , Humanos , Alineación de SecuenciaRESUMEN
Research for new technologies and methods in computational bioinformatics has resulted in many folds biological data generation. To cope with the ever increasing growth of biological data, there is a need for accelerated solutions in various domains of computational bioinformatics. In these domains, string matching is a most versatile operation performed at various stages of the computational pipeline. For search patterns that are updated with time, there is a need for accelerated and reconfigurable string matching to perform faster searching in the ever-growing biological databases. In this paper, we have proposed an accelerated and real-time reconfigurable methodology for string matching using hardware-software codesign. Using state of the art field programmable gate arrays, we have proposed a complete system-on-chip solution for applications that require accelerated as well as real-time reconfigurable string matching. The proposed methodology is the first of its kind novel approach for high-speed string matching that also supports quick reconfiguration by patterns changing with time. It is verified at the string matching stage of protein identification. Experimental results show that the architectures designed using our proposed methodology are 4X faster than state-of-the-art software implementation running on a workstation and 1.5X-4X faster than hardware accelerators available in the literature.
Asunto(s)
Algoritmos , Biología Computacional/métodos , Programas Informáticos , Computadores , Bases de Datos Genéticas , Proteínas/química , Proteínas/genéticaRESUMEN
In this paper we propose an on-the-fly reconfigurable hardware-software codesign based reconfigurable solution for real-time protein identification. Reconfigurable string matching is performed in the disciplines of protein identification and biomarkers discovery. With the generation of plethora of sequenced data and number of biomarkers for several diseases, it is becoming necessary to have an accelerated processing and on-the-fly reconfigurable system design methodology to bring flexibility to its usage in the medical science community without the need of changing the entire hardware every time with the advent of new biomarker or protein. The proteome database of human at UniProtKB (Proteome ID up000005640) comprising of 42132 canonical and isoform proteins with variable database-size are used for testing the proposed design and the performance of the proposed system has been found to compare favorably with the state-of-the-art approaches with the additional advantage of real-time reconfigurability.