• Genomics & Informatics
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• v.18 no.4
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• pp.42.1-42.9
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• 2020

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.

• Biomedical Science Letters
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• v.28 no.2
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• pp.109-119
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• 2022

Histone proteins can be modified by the addition of acetyl group or methyl group to specific amino acids. The modifications have different distribution patterns in chromatin. Recently, histone modifications are studied based on ChIP-seq data, which requires reasonable analysis of sequencing data depending on their distribution patterns. Here we have analyzed histone H3K27ac and H3K27me3 ChIP-seq data and it showed that the H3K27ac is enriched at narrow regions while H3K27me3 distributes broadly. To properly analyze the ChIP-seq data, we called peaks for H3K27ac and H3K27me3 using MACS2 (narrow option and broad option) and SICER methods, and compared propriety of the peaks using signal-to-background ratio. As results, H3K27ac-enriched regions were well identified by both methods while H3K27me3 peaks were properly identified by SICER, which indicates that peak calling method is more critical for histone modifications distributed broadly. When ChIP-seq data were compared in different sequencing depth (15, 30, 60, 120 M), high sequencing depth caused high false-positive rate in H3K27ac peak calling, but it reflected more properly the broad distribution pattern of H3K27me3. These results suggest that sequencing depth affects peak calling from ChIP-seq data and high sequencing depth is required for H3K27me3. Taken together, peak calling tool and sequencing depth should be chosen depending on the distribution pattern of histone modification in ChIP-seq analysis.

• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.342-350
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• 2018

The purpose of this study was to identify the environmental factors that affect the beginning and end of calling by Hyalessa fuscata and Cryptotympana atrata, which are dominant cicada species in the central urban areas of Korea. The study area was Banpo Apartments in Seoul. The research period included two months, being from the end of July to the end of August 2015. We analyzed the start and end time of cicada calling, and on average H. fuscata started calling at 5:21 am and C. atrata started at 7:40 am. The average end time of calling was 6:31 pm for H. fuscata and 7:51 pm for C. atrata. From the scatter plot and box plot results, H. fuscata started calling at 05:00 am, whereas C. atrata consistently stopped calling at 20:00 pm compared to H. fuscata. Multiple regression analysis of the start and end time of cicada calling showed that sunrise time was a factor affecting the start of H. fuscata calling. The end time of H. fuscata calling was affected by sunset time and total cloud cover. The starting time of C. atrata calling was mostly affected by temperature and sunrise time. The effect of temperature was greater than that of sunrise time. The end time of C. atrata calling was strongly affected by sunset time, whereas peak temperature was also shown to affect the end time. From the above results, sunrise and sunset are thought to be the critical factor affecting the start and end time of H. fuscata calling. Therefore, H. fuscata started calling with sunrise, and the end time was also affected by sunset. Temperature was the factor most affecting the start of C. atrata calling and sunset was identified as the factor affecting the end time. Therefore, the start time of C. atrata calling shows variation with daily temperature changes, and C. atrata stop calling simultaneously with sunset.

• Genomics & Informatics
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• v.15 no.1
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• pp.11-18
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• 2017

Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPseeker. 'dada2' performs trimming of the high-throughput sequencing data. 'QuasR' and 'mosaics' perform quality control and mapping of the input reads to the reference genome and peak calling, respectively. Finally, 'ChIPseeker' performs annotation and visualization of the called peaks. This workflow runs well independently of operating systems (e.g., Windows, Mac, or Linux) and processes the input fastq files into various results in one run. R code is available at github: https://github.com/ddhb/Workflow_of_Chipseq.git.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.6
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• pp.791-797
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• 2018

Objective: Trimethylation of histone 3 (H3) at 4th lysine N-termini (H3K4me3) in gene promoter region was the universal marker of active genes specific to cell lineage. On the contrary, coexistence of trimethylation at 27th lysine (H3K27me3) in the same loci-the bivalent H3K4m3/H3K27me3 was known to suspend the gene transcription in germ cells, and could also be inherited to the developed stem cell. In galline species, throughout example of H3K4m3 and H3K27me3 ChIP-seq analysis was still not provided. We therefore designed and demonstrated such procedures using ChIP-seq and mRNA-seq data of chicken follicular mesenchymal cells and male germ cells. Methods: Analytical workflow was designed and provided in this study. ChIP-seq and RNA-seq datasets of follicular mesenchymal cells and male germ cells were acquired and properly preprocessed. Peak calling by Model-based analysis of ChIP-seq 2 was performed to identify H3K4m3 or H3K27me3 enriched regions ($Fold-change{\geq}2$, $FDR{\leq}0.01$) in gene promoter regions. Integrative genomics viewer was utilized for cellular retinoic acid binding protein 1 (CRABP1), growth differentiation factor 10 (GDF10), and gremlin 1 (GREM1) gene explorations. Results: The acquired results indicated that follicular mesenchymal cells and germ cells shared several unique gene promoter regions enriched with H3K4me3 (5,704 peaks) and also unique regions of bivalent H3K4m3/H3K27me3 shared between all cell types and germ cells (1,909 peaks). Subsequent observation of follicular mesenchyme-specific genes-CRABP1, GDF10, and GREM1 correctly revealed vigorous transcriptions of these genes in follicular mesenchymal cells. As expected, bivalent H3K4m3/H3K27me3 pattern was manifested in gene promoter regions of germ cells, and thus suspended their transcriptions. Conclusion: According the results, an example of chicken H3K4m3/H3K27me3 ChIP-seq data analysis was successfully demonstrated in this study. Hopefully, the provided methodology should hereby be useful for galline ChIP-seq data analysis in the future.

• Journal of Korea Port Economic Association
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• v.34 no.2
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• pp.69-82
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• 2018

The number of ships serviced at the container terminals in Busan is increasing by 2.9% per year. In spite of the increase in calling ships, there are no official records of waiting rate by the port authority. This study attempts to compare the theoretical ship waiting ratio and actual ship waiting ratio. The actual ship waiting ratio of container terminals is acquired from the 2014 to 2016 data of PORT-MIS and Terminal Operating System (TOS). Furthermore, methods and procedures to measure the actual ship's waiting rate of container terminal are proposed for ongoing measurement. In drawing the theoretical ship waiting ratio, the queuing theory is applied after deploying the ship arrival probability distribution and ship service probability distribution by the Chi Square method. As a result, the total number of ships waiting in a terminal for three years was 587, the average monthly service time and the average waiting time was 13.8 hours and 17.1 hours, respectively, and the monthly number of waiting ships was 16.3. Meanwhile, according to the queuing theory with multi servers, the ship waiting ratio is 31.1% on a 70% berth occupancy ratio. The reason behind the huge gap is the congested sailing in the peak days of the week, such as Sunday, Tuesday, and Wednesday. In addition, the number of waiting ships recorded on Sundays was twice as much as the average number of waiting ships.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.356-367
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• 2018

A series of tests dedicated to station blackout (SBO) accident scenarios have been recently performed at the $Prim{\ddot{a}}rkreislauf-Versuchsanlage$ (primary coolant loop test facility; PKL) facility in the framework of the OECD/NEA PKL-3 project. These investigations address current safety issues related to beyond design basis accident transients with significant core heat up. This work presents a detailed analysis using the best estimate thermal-hydraulic code TRACE (v5.0 Patch4) of different SBO scenarios conducted at the PKL facility; failures of high- and low-pressure safety injection systems together with steam generator (SG) feedwater supply are considered, thus calling for adequate accident management actions and timely implementation of alternative emergency cooling procedures to prevent core meltdown. The presented analysis evaluates the capability of the applied TRACE model of the PKL facility to correctly capture the sequences of events in the different SBO scenarios, namely the SBO tests H2.1, H2.2 run 1 and H2.2 run 2, including symmetric or asymmetric secondary side depressurization, primary side depressurization, accumulator (ACC) injection in the cold legs and secondary side feeding with mobile pump and/or primary side emergency core coolant injection from the fuel pool cooling pump. This study is focused specifically on the prediction of the core exit temperature, which drives the execution of the most relevant accident management actions. This work presents, in particular, the key improvements made to the TRACE model that helped to improve the code predictions, including the modeling of dynamical heat losses, the nodalization of SGs' heat exchanger tubes and the ACCs. Another relevant aspect of this work is to evaluate how well the model simulations of the three different scenarios qualitatively and quantitatively capture the trends and results exhibited by the actual experiments. For instance, how the number of SGs considered for secondary side depressurization affects the heat transfer from primary side; how the discharge capacity of the pressurizer relief valve affects the dynamics of the transient; how ACC initial pressure and nitrogen release affect the grace time between ACC injection and subsequent core heat up; and how well the alternative feeding modes of the secondary and/or primary side with mobile injection pumps affect core quenching and ensure stable long-term core cooling under controlled boiling conditions.

• Journal of Life Science
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• v.31 no.4
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• pp.410-417
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• 2021

Next-generation sequencing (NGS) is a high-throughput technique for sequencing large numbers of DNA fragments that are prepared from a genome. This sequencing technique has been used to elucidate whole genome sequences of living organisms and to analyze complementary DNA (cDNA) or chromatin immunoprecipitated DNA (ChIPed DNA) at the genome level. After NGS, the use of proper tools is important for processing and analyzing data with reasonable parameters. However, handling large-scale sequencing data and programing for data analysis can be difficult. The Galaxy platform, a public web service system, provides many different tools for NGS data analysis, and it allows researchers to analyze their data on a web browser with no deep knowledge about bioinformatics and/or programing. In this study, we explain the procedure for preparing chromatin immunoprecipitation-sequencing (ChIP-seq) libraries and steps for analyzing ChIP-seq data using the Galaxy platform. The data analysis steps include the NGS data upload to Galaxy, quality check of the NGS data, premapping processes, read mapping, the post-mapping process, peak-calling and visualization by window view, heatmaps, average profile, and correlation analysis. Analysis of our histone H3K4me1 ChIP-seq data in K562 cells shows that it correlates with public data. Thus, NGS data analysis using the Galaxy platform can provide an easy approach to bioinformatics.

• Korean Journal of Environment and Ecology
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• v.33 no.6
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• pp.636-644
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• 2019

The purpose of this study was to investigate the callings of nocturnal birds using bioacoustic recording technology to identify species and to analyze the ecological characteristics of each species. Three sites - Seoraksan National Park, National Institute of Ecology, and Mudeungsan National Park - were investigated. The investigation period was from the middle of April 2018 to early March 2019 for Seoraksan national park, from late February of 2018 to the middle of February 2019 for the National Institute of Ecology, and from the middle of February 2018 to the end of August 2018 for Mudeungsan National Park. The main research results are as follows. Firstly, nocturnal bird species identified by the survey included Caprimulgus indicus, Otus sunia, Zoothera aurea, Bubo bubo, and Strix uralensis, 5 species in total. Secondly, the breeding call period of each species was from early May to early August for C. indicus, from early April to the end of September for O. sunia, from early March to early October for Z. aurea, from late September to early February for B. bubo, and from mid-January to early March for S. uralensis. Thirdly, the mating call rhythm was between 16:00 and 10:00 on the following day for all the observed species in the three regions, and the peak time zone was from 20:00 to 06:00 on the following day. Fourthly, there was no correlation between the cumulative call frequency and the precipitation for each species. Fifthly, the mean temperature during the period when the specific calls of nocturnal birds were detected was -4.00 ℃ for S. uralensis, 2.58 ℃ for B. bubo, 13.66 ℃ for Z. aurea, 19.50 ℃ for O. sunia, and 20.77 ℃ for C. indicus. The ANOVA results showed that there was a significant difference in mean temperature for the calling by species and that the mean temperature was S. uralensis, B. bubo, Z. aurea, and O. sunia-C. indicus, in the ascending order, for 4 groups in total. The period of the specific mating calls confirmed by the study is a period in which the frequency of calls was the highest among the periods when the specific calls were detected. Since it is associated with the known mating period of each species, the period of the high frequency of calls confirmed by the bioacoustic monitoring can be regarded as the mating season. This study is meaningful in that it is the early research that has used the bioacoustic recording technology to identify species and ecological characteristics of species of nocturnal birds in Korea.