Bioinformatics services for analyzing massive genomic datasets

Abstract

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www. bioexpress.re.kr/.

CATE: A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data

“…To tackle this predicament, researchers need solutions conducting genome wide calculations on expansive repositories in a fast, resource-efficient, and scalable nature (10,17,19). However, popular computational tools, though capable of being executed on HPC (High Performance Computing) clusters, have been designed with the goal of processing data that spans a few hundred individuals across thousands of polymorphic sites (20)(21)(22). They use single thread algorithms that do not exploit the prevalent parallel processing technologies made available through multi core CPUs (Control Processing Unit), and GPUs (Graphical Processing Unit) (10,21,23,24).…”

CATE: A fast and scalable CUDA implementation to conduct highly parallelized evolutionary tests on large scale genomic data

“…To tackle this predicament, researchers need solutions that are of a fast, resource‐efficient, and scalable nature (Pfeifer et al, 2014; Rozas et al, 2017; Szpiech & Hernandez, 2014). However, popular computational tools, though capable of being executed on HPC (high‐performance computing) clusters, have been designed with the goal of processing data that spans a few hundred individuals across thousands of polymorphic sites (Cook & Andersen, 2017; Ko et al, 2020; Siepel, 2019). They use single‐thread algorithms that do not exploit the prevalent parallel processing technologies made available through multi‐core CPUs (central processing unit), and GPUs (graphical processing unit) (Cook & Andersen, 2017; Ghorpade, 2012; Pfeifer et al, 2014; Tendler et al, 2002).…”