Background: The Korean Red Cross has conducted serologic tests for C, c, E, e antigens and found 18 D-- donors.In this study, we performed RHCE genotyping to identify the molecular characteristics of the serologic D-- blood type in Korean blood donors. Methods: We performed RHCE-specific PCR-based electrophoresis to check the amplification pattern of each exon.Sanger sequencing was conducted to find the variants in the nucleotide sequence. We determined the RHCE genotype based on the electrophoresis and Sanger sequencing results. Results: Total eight out of 18 D-- donors were participated in this research. In the PCR-based electrophoresis tests, RHCE exons 3, 4, and 6 were not amplified in samples #4, #6, and #8. Also, sample #2 showed an abnormal band pattern of RHCE exon 9. The Sanger sequencing results showed that the nucleotide sequences of the RHCE exons 5, 7, and 8 in samples #4, #6 and #8 corresponded to the nucleotide sequences of RHD exons 5, 7, and 8, respectively, suggesting the possibility of a RHCE-RHD(3-8)-RHCE hybrid allele. The nucleotide sequences of RHCE exons 7 and 8 in sample #2 were the same as the nucleotide sequences of RHD exons 7 and 8, respectively.In samples #1, #3, #5, and #7, no specific variants known to cause D-- phenotype were found. Conclusion RHCE genes partially replaced by the RHD genes were found in four out of eight participants and three of them were identified as ?RHCE*02N.07, which is known as the RHCE null allele. A further study with complete RHCE sequencing could be helpful for an understanding of the molecular mechanisms of samples in which no significant variants were identified.

Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-Seq) is a powerful technology to profile the location of proteins of interest on a whole-genome scale. To identify the enrichment location of proteins, many programs and algorithms have been proposed. However, none of the commonly used peak calling programs could accurately explain the binding features of target proteins detected by ChIP-Seq. Here, publicly available data on 12 histone modifications, including H3K4ac/me1/me2/me3, H3K9ac/me3, H3K27ac/me3, H3K36me3, H3K56ac, and H3K79me1/me2, generated from a human embryonic stem cell line (H1), were profiled with five peak callers (CisGenome, MACS1, MACS2, PeakSeq, and SISSRs). The performance of the peak calling programs was compared in terms of reproducibility between replicates, examination of enriched regions to variable sequencing depths, the specificity-to-noise signal, and sensitivity of peak prediction. There were no major differences among peak callers when analyzing point source histone modifications. The peak calling results from histone modifications with low fidelity, such as H3K4ac, H3K56ac, and H3K79me1/me2, showed low performance in all parameters, which indicates that their peak positions might not be located accurately. Our comparative results could provide a helpful guide to choose a suitable peak calling program for specific histone modifications.

Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-Seq) is a powerful technology to profile the location of proteins of interest on a whole-genome scale. To identify the enrichment location of proteins, many programs and algorithms have been proposed. However, none of the commonly used peak calling programs could accurately explain the binding features of target proteins detected by ChIP-Seq. Here, publicly available data on 12 histone modifications, including H3K4ac/me1/me2/me3, H3K9ac/me3, H3K27ac/me3, H3K36me3, H3K56ac, and H3K79me1/me2, generated from a human embryonic stem cell line (H1), were profiled with five peak callers (CisGenome, MACS1, MACS2, PeakSeq, and SISSRs). The performance of the peak calling programs was compared in terms of reproducibility between replicates, examination of enriched regions to variable sequencing depths, the specificity-to-noise signal, and sensitivity of peak prediction. There were no major differences among peak callers when analyzing point source histone modifications. The peak calling results from histone modifications with low fidelity, such as H3K4ac, H3K56ac, and H3K79me1/me2, showed low performance in all parameters, which indicates that their peak positions might not be located accurately. Our comparative results could provide a helpful guide to choose a suitable peak calling program for specific histone modifications.

Background: An activating mutation (c.617A>C/p.Lys206Arg, L206R) in protein kinase cAMP-activated catalytic subunit alpha (PRKACA) has been reported in 35% to 65% of cases of cortisol-producing adenomas (CPAs). We aimed to compare the clinical characteristics and transcriptome analysis between PRKACA L206R mutants and wild-type CPAs in Korea. Methods: We included 57 subjects with CPAs who underwent adrenalectomy at Seoul National University Hospital. Sanger sequencing for PRKACA was conducted in 57 CPA tumor tissues. RNA sequencing was performed in 13 fresh-frozen tumor tissues. Results: The prevalence of the PRKACA L206R mutation was 51% (29/57). The mean age of the study subjects was 42±12 years, and 87.7% (50/57) of the patients were female. Subjects with PRKACA L206R mutant CPAs showed smaller adenoma size (3.3±0.7 cm vs. 3.8±1.2 cm, P=0.059) and lower dehydroepiandrosterone sulfate levels (218±180 ng/mL vs. 1,511±3,307 ng/mL, P=0.001) than those with PRKACA wild-type CPAs. Transcriptome profiling identified 244 differentially expressed genes (DEGs) between PRKACA L206R mutant (n=8) and wild-type CPAs (n=5), including five upregulated and 239 downregulated genes in PRKACA L206R mutant CPAs (|fold change| ≥2, P<0.05). Among the upstream regulators of DEGs, CTNNB1 was the most significant transcription regulator. In several pathway analyses, the Wnt signaling pathway was downregulated and the steroid biosynthesis pathway was upregulated in PRKACA mutants. Protein-protein interaction analysis also showed that PRKACA downregulates Wnt signaling and upregulates steroid biosynthesis. Conclusion The PRKACA L206R mutation in CPAs causes high hormonal activity with a limited proliferative capacity, as supported by transcriptome profiling.

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/.