• 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.

• 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.

• 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.

• 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.

• Genomics & Informatics
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• v.19 no.2
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• pp.17.1-17.13
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• 2021

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)-specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3'-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

• Genomics & Informatics
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• v.12 no.4
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• pp.145-150
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• 2014

Recent technical advances, such as chromatin immunoprecipitation combined with DNA microarrays (ChIp-chip) and chromatin immunoprecipitation-sequencing (ChIP-seq), have generated large quantities of high-throughput data. Considering that epigenomic datasets are arranged over chromosomes, their analysis must account for spatial or temporal characteristics. In that sense, simple clustering or classification methodologies are inadequate for the analysis of multi-track ChIP-chip or ChIP-seq data. Approaches that are based on hidden Markov models (HMMs) can integrate dependencies between directly adjacent measurements in the genome. Here, we review three HMM-based studies that have contributed to epigenetic research, from a computational perspective. We also give a brief tutorial on HMM modelling-targeted at bioinformaticians who are new to the field.

• 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.

• Genomics & Informatics
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• v.10 no.3
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• pp.145-152
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• 2012

Chromatin structure and dynamics that are influenced by epigenetic marks, such as histone modification and DNA methylation, play a crucial role in modulating gene transcription. To understand the relationship between histone modifications and regulatory elements in breast cancer cells, we compared our chromatin immunoprecipitation sequencing (ChIP-Seq) histone modification patterns for histone H3K4me1, H3K4me3, H3K9/16ac, and H3K27me3 in MCF-7 cells with publicly available formaldehyde-assisted isolation of regulatory elements (FAIRE)-chip signals in human chromosomes 8, 11, and 12, identified by a method called FAIRE. Active regulatory elements defined by FAIRE were highly associated with active histone modifications, like H3K4me3 and H3K9/16ac, especially near transcription start sites. The H3K9/16ac-enriched genes that overlapped with FAIRE signals (FAIRE-H3K9/14ac) were moderately correlated with gene expression levels. We also identified functional sequence motifs at H3K4me1-enriched FAIRE sites upstream of putative promoters, suggesting that regulatory elements could be associated with H3K4me1 to be regarded as distal regulatory elements. Our results might provide an insight into epigenetic regulatory mechanisms explaining the association of histone modifications with open chromatin structure in breast cancer cells.

• 한국균학회소식:학술대회논문집
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• 2018.05a
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• pp.19-19
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• 2018

Fungal pathogens have huge impact on health and economic wellbeing of human by causing life-threatening mycoses in immune-compromised patients or by destroying crop plants. A key determinant of fungal pathogenesis is their ability to undergo developmental change in response to host or environmental factors. Genetic pathways that regulate such morphological transitions and adaptation are therefore extensively studied during the last few decades. Given that epigenetic as well as genetic components play pivotal roles in development of plants and mammals, contribution of microbial epigenetic counterparts to this morphogenetic process is intriguing yet nearly unappreciated question to date. To bridge this gap in our knowledge, we set out to investigate histone modifications among epigenetic mechanisms that possibly regulate fungal adaptation and processes involved in pathogenesis of a model plant pathogenic fungus, Magnaporthe oryzae. For functional and comparative analysis of histone modifications, a web-based database (dbHiMo) was constructed first to archive and analyze histone modifying enzymes from eukaryotic species whose genome sequences are available. Based on the database entries, we carried out functional analysis of genes encoding histone modifying enzymes. Here I provide examples of such analyses that show how histone acetylation and methylation is implicated in regulating important aspects of fungal pathogenesis. Current analysis of histone modifying enzymes is followed by ChIP-seq and RNA-seq experiments to pinpoint the genes that are controlled by particular histone modifications. We anticipate that our work will provide not only the significant advances in our understanding of epigenetic mechanisms operating in microbial eukaryotes but also basis to expand our perspective on regulation of development in fungal pathogens.

• Molecules and Cells
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• v.45 no.9
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• pp.622-630
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• 2022

Colorectal cancer (CRC) has a high mortality rate among cancers worldwide. To reduce this mortality rate, chemotherapy (5-fluorouracil, oxaliplatin, and irinotecan) or targeted therapy (bevacizumab, cetuximab, and panitumumab) has been used to treat CRC. However, due to various side effects and poor responses to CRC treatment, novel therapeutic targets for drug development are needed. In this study, we identified the overexpression of EHMT1 in CRC using RNA sequencing (RNA-seq) data derived from TCGA, and we observed that knocking down EHMT1 expression suppressed cell growth by inducing cell apoptosis in CRC cell lines. In Gene Ontology (GO) term analysis using RNA-seq data, apoptosis-related terms were enriched after EHMT1 knockdown. Moreover, we identified the CHOP gene as a direct target of EHMT1 using a ChIP (chromatin immunoprecipitation) assay with an anti-histone 3 lysine 9 dimethylation (H3K9me2) antibody. Finally, after cotransfection with siEHMT1 and siCHOP, we again confirmed that CHOP-mediated cell apoptosis was induced by EHMT1 knockdown. Our findings reveal that EHMT1 plays a key role in regulating CRC cell apoptosis, suggesting that EHMT1 may be a therapeutic target for the development of cancer inhibitors.