High-performance computing service for bioinformatics and data science

Abstract

Despite having an ideal setup in their labs for wet work, researchers often lack the computational infrastructure to analyze the magnitude of data that result from “-omics” experiments. In this innovative project, the library supports analysis of high-throughput data from global molecular profiling experiments by offering a high-performance computer with open source software along with expert bioinformationist support. The audience for this new service is faculty, staff, and students for whom using the university’s large scale, CORE computational resources is not warranted because these resources exceed the needs of smaller projects. In the library’s approach, users are empowered to analyze high-throughput data that they otherwise would not be able to on their own computers. To develop the project, the library’s bioinformationist identified the ideal computing hardware and a group of open source bioinformatics software to provide analysis options for experimental data such as scientific images, sequence reads, and flow cytometry files. To close the loop between learning and practice, the bioinformationist developed self-guided learning materials and workshops or consultations on topics such as the National Center for Biotechnology Information’s BLAST, Bioinformatics on the Cloud, and ImageJ. Researchers apply the data analysis techniques that they learned in the classroom in the library’s ideal computing environment.

A simple guide to de novo transcriptome assembly and annotation

“…Broadly speaking, there are two ways in which such resources can be requisitioned. Depending on the usage frequency, a workstation/server with the necessary capacity may be rented or purchased outright for institutional/departmental use [ 244 ]. Very often, research and educational institutions will have their own centralized computational infrastructure (e.g.…”

“…Very often, research and educational institutions will have their own centralized computational infrastructure (e.g. high performance compute clusters) from which such resources can be requested [ 244 ]. Computational resources may also be acquired from national-scale compute infrastructure projects [ 245 , 246 ], non-profit foundations that offer bioinformatic-as-a-service (e.g.…”

Data science and its relationship to library and information science: a content analysis

“…They proposed meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process. Courneya and Mayo (2018) described the project where the library supports analysis of high-throughput data from global molecular profiling experiments by offering a high-performance computer with open-source software along with expert bio-informationist support. The library's bio-informationist identified the ideal computing hardware and a group of open-source bioinformatics software to provide analysis options for experimental data such as scientific images, sequence reads and flow cytometry files.…”

Parallel Implementation of Smith-Waterman Algorithm on FPGA

“…Bioinformatics analysis algorithms are critically dependent on the computational infrastructure to cover high-throughput, ultra-low-latency, and low-power requirements. It can be said that there are three generations of infrastructure used, which are: High-Performance Computing (HPC) [15], Graphics Processing Units (GPUs) [16,17], and Custom Hardware Architectures (CHA) [18][19][20].…”

“…However, these software-only approaches cannot keep up with the growing computational demands of genomic analysis, given the barriers to reducing latency in large volumes using only CPUs and GPUs. In addition, as the number of nodes grows to handle increasing amounts of data, performance is not scaled linearly [15,[21][22][23]. The third (CHA) generation of infrastructure has been presenting itself as an exciting alternative to satisfy high-throughput, ultra-low-latency, and low-power requirements [24][25][26][27][28][29].…”