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/.

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/.

Lespedeza cuneata (LC) is a perennial plant used in herbal medicine to treat numerous diseases, including prostatic hyperplasia, diabetes, early atherosclerosis, and hematuria. Reference collections of bioactive compounds of LC are crucial for the determination of their pharmacological properties. However, little is known regarding its anti-oxidative and anti-inflammatory effects in alveolar macrophage (MH-S) cells. This study examined whether LC can inhibit reactive oxygen species and Coal fly ash (CFA) induced inflammation in MH-S cells. The anti-oxidative effects of LC were evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays, anti-inflammatory effects were examined using nitric oxide (NO) assay, and cytotoxicity was analyzed using methyl thiazolyl tetrazolium assay. The expression of inflammatory cytokine genes was assessed through a reverse-transcription polymerase chain reaction. Our results revealed that LC exhibited high radical scavenging activity and a dose-dependent (7.8–1,000 μg/mL) inhibition of oxidation as compared to ascorbic acid and Trolox. It also inhibited CFA-induced NO production in MH-S cells. Moreover, it suppressed the CFA exposure-mediated expression of pro-inflammatory mediators and cytokines, including inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), interleukin (IL)-1β, IL-6, and tumor necrosis factor-α. These results suggest that LC is a potent antioxidant and anti-inflammatory agent that can be useful as a nutraceutical product.