A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model

Abstract

Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.

OptCAM: An ultra‐fast all‐optical architecture for DNA variant discovery

“…For instance, performing local DNA sequence alignment for a 128‐base‐pair DNA subsequences, adopting the well‐known BLAST algorithm, takes estimated computational time of 12.28 seconds per comparison in average on a typical computer (ie, an Intel Core i7‐4500U CPU @2.40 GHz with 8.00 GB of RAM) . This process on a high‐performance computing (HPC) system (ie, Cisco UCS Blade Server B200M2 × 2 Units with CPU 2 × 6 cores [2.4 GHz, 12 cores in total] and 96 GB of RAM) takes an estimated 0.53 seconds per comparison in average . The HPC system used in this study was a small model; however, a larger HPC might be able to complete the same sequence alignment in less time.…”

Reference: An algorithm for recognizing the main melody of orchestral music based on artificial intelligence of music melody contour

“…With the development and popularity of computer networks, a large number of music resources can be obtained through network retrieval, and the problem of identifying and locating music has begun to become a difficult problem []- [5]. In this situation, an intelligent recognition system based on music content was born, which is an integrated system to study how to identify and classify music information using digital signal processing techniques []- [10].…”