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Yang, J. O.

Publications and source records attributed to Yang, J. O..

2 recordsLinked to original sources

KoNA: Korean Nucleotide Archive as a New Data Repository for Nucleotide Sequence Data

During the last decade, generation and accumulation of petabase-scale high-throughput sequencing data have resulted in ethical and technical challenges, including access to human data, and transfer, storage, and sharing of enormous amount of data. To promote data-driven research in biology, the Korean government announced that all the biological data generated from government-funded research projects should be deposited in the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. We introduce the Korean Nucleotide Archive (KoNA), a repository for nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next generation sequencing data from several national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure (SOP) was adopted, which is similar to the International Nucleotide Sequence Database Collaboration. The SOP includes quality control processes for submitted data and metadata using an automated pipeline followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed in a cloud-computing service for genomic data analysis called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances data experience including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea, but also provides datasets to researchers globally and contribute to advances in genomics. KoNA is available at https://www.kobic.re.kr/kona/.

genomics↗

Metastatic prognostic ability of lung cancer stromal cells from a single-cell RNA-seq perspective

Single-cell RNA sequencing (scRNA-seq) has been widely studied and analyzed to understand cancer heterogeneity. Metastasis and invasion through communication with immune cells have been widely studied; stromal cells are known to change during cancer progression and cause metastasis, but little is known about their inherent metastasis prognostic abilities. This study investigated the abilities of stromal cells by analyzing the scRNA-seq data of adjacent, tumor, and metastasized tissues, biopsied from 15 patients. We considered fibroblast and smooth muscle cells as cell subtypes of stromal cells. We detected decorin (DCN) and insulin-like growth factor-binding protein 7 (IGFBP7) as sub-cell type markers conserved in tumor and metastasis. We found an organic relationship that affects metastasis by assessing the interaction between the expression and related pathways among the assigned stromal cell subtypes. In addition to the interactions of sub-cell-types within stromal cells, we also studied communication between stromal cells and the five assigned lung cancer cell types, and observed its relation with migration and metastasis; the role of DCN as a mediating factor was also studied. Results of our study indicated that DCN and IGFBP7 are factors that can be monitored in the follow-up of prognostic metastasis factors in patients with lung cancer. Therefore, DCN and IGFBP7, which are the assigned sub-cell types marker in lung cancer stromal cells, can be used as potential biomarkers for follow-up in lung cancer metastasis. Our study assigned stromal cell subtypes of lung cancer and detected markers that can be used as monitoring factors during metastasis of lung cancer. This suggests that DCN and IGFBP7 are potential biomarkers to evaluate metastatic ability and follow-up as metastatic prognostic factors in lung cancer patients. Author SummaryLung cancer is known to be a disease with high heterogeneity. In particular, it has been studied that stromal cells of lung cancer are involved in metastasis in cancer. Finding specific markers of stromal cell subtypes can lead to more accurate cancer markers. In this study, we assigned stromal cell subtypes as FB and SMC through scRNA-seq data analysis. DCN and IGFBP7, which are markers specifically expressed in stromal cell sub-cell types of NSCLC and metastatic NSCLC, were found. Whether DCN and IGFBP7 can be used as prognostic factors for metastasis was verified through co-expression analysis of FB and SMC and cell-cell communication. Specific expression as a marker was verified by confirming the role in the hub-gene network and the target genes of communication. Our findings suggest that DCN and IGFBP7 are potential biomarkers for evaluating the metastasis prognostic ability of stromal cells and can be monitored in the follow-up of metastasis prognostic factors in lung cancer patients.

bioinformatics↗