• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.363-373
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• 2022

Multi-omics data is difficult to interpret due to the heterogeneity of information by the volume of data, the complexity of characteristics of each data, and the diversity of omics platforms. There is not yet a system for interpreting to visualize research data on environmental diseases concerning environmental harmful substances. We provide MEE, a web-based visualization tool, to comprehensively explore the complexity of data due to the interconnected characteristics of high-dimensional data sets according to exposure to various environmental harmful substances. MEE visualizes omics data of correlation between omics data, subjects and samples by keyword searches of meta data, multi-omics data, and harmful substances. MEE has been demonstrated the versatility by two examples. We confirmed the correlation between smoking and asthma with RNA-seq and Methylation-Chip data, it was visualized that genes (P HACTR3, PXDN, QZMB, SOCS3 etc.) significantly related to autoimmune or inflammatory diseases. To visualize the correlation between atopic dermatitis and heavy metals, we selected 32 genes related immune response by integrated analysis of multi-omics data. However, it did not show a significant correlation between mercury in blood and atopic dermatitis. In the future, should continuously collect an appropriate level of multi-omics data in MEE system, will obtain data to analyze environmental substances and diseases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.768-770
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• 2015

We performed integration and standardization of the omics data related agriculture. To do this, we requires progressed computational methods and bioinformatics infrastructures for integration, standardization, mining, and analysis. It makes easier biological knowledge to find. we potentialize registration a row and processed data in NABIC (National Agricultural Biotechnology Information Center) and its processed analysis results were offered related researchers. And we also provided various analysis pipelines, NGS analysis (Reference assembly, RNA-seq), GWAS, Microbial community analysis. In addition, the our system was carried out based on the design and build the quality assurance in management omics information system and constructed the infrastructure for utilization of omics analyze system. We carried out major improvement quality of omics information system. First is Improvement quality of registration category for omics based information. Second is data processing and development platform for web UI about related omics data. Third is development of proprietary management information for omics registration database. Forth is management and development of the statistics module producers about omics data. Last is Improvement the standard upload/ download module for Large omics Registration information.

• Molecular & Cellular Toxicology
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• v.3 no.3
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• pp.208-213
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• 2007

With the help of a development and popularization of microarray technology that enable to us to simultaneously investigate the expression pattern of thousands of genes, the toxicogenomics experimenters can interpret the genome-scale interaction between genes exposed in toxicant or toxicant-related environment. The ultimate and primary goal of toxicogenomics identifies functional context among the group of genes that are differentially or similarly coexpressed under the specific toxic substance. On the other side, public reference databases with transcriptom, proteom, and biological pathway information are needed for the analysis of these complex omics data. However, due to the heterogeneous and independent nature of these databases, it is hard to individually analyze a large omics annotations and their pathway information. Fortunately, several web sites of the public database provide information linked to other. Nevertheless it involves not only approriate information but also unnecessary information to users. Therefore, the systematically integrated database that is suitable to a demand of experimenters is needed. For these reasons, we propose SOP (Search of Omics Pathway) database system which is constructed as the integrated biological database converting heterogeneous feature of public databases into combined feature. In addition, SOP offers user-friendly web interfaces which enable users to submit gene queries for biological interpretation of gene lists derived from omics experiments. Outputs of SOP web interface are supported as the omics annotation table and the visualized pathway maps of KEGG PATHWAY database. We believe that SOP will appear as a helpful tool to perform biological interpretation of genes or proteins traced to omics experiments, lead to new discoveries from their pathway analysis, and design new hypothesis for a next toxicogenomics experiments.

• Genomics & Informatics
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• v.16 no.4
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• pp.29.1-29.5
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• 2018

Hybrid capture-based targeted sequencing is being used increasingly for genomic variant profiling in tumor patients. Unique molecular index (UMI) technology has recently been developed and helps to increase the accuracy of variant calling by minimizing polymerase chain reaction biases and sequencing errors. However, UMI-adopted targeted sequencing data analysis is slightly different from the methods for other types of omics data, and its pipeline for variant calling is still being optimized in various study groups for their own purposes. Due to this provincial usage of tools, our group built an analysis pipeline for global application to many studies of targeted sequencing generated with different methods. First, we generated hybrid capture-based data using genomic DNA extracted from tumor tissues of colorectal cancer patients. Sequencing libraries were prepared and pooled together, and an 8-plexed capture library was processed to the enrichment step before 150-bp paired-end sequencing with Illumina HiSeq series. For the analysis, we evaluated several published tools. We focused mainly on the compatibility of the input and output of each tool. Finally, our laboratory built an analysis pipeline specialized for UMI-adopted data. Through this pipeline, we were able to estimate even on-target rates and filtered consensus reads for more accurate variant calling. These results suggest the potential of our analysis pipeline in the precise examination of the quality and efficiency of conducted experiments.

• Genomics & Informatics
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• v.18 no.4
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• pp.46.1-46.4
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• 2020

We developed the BaSDAS (Barcode-Seq Data Analysis System), a GUI-based pooled knockout screening data analysis system, to facilitate the analysis of pooled knockout screen data easily and effectively by researchers with limited bioinformatics skills. The BaSDAS supports the analysis of various pooled screening libraries, including yeast, human, and mouse libraries, and provides many useful statistical and visualization functions with a user-friendly web interface for convenience. We expect that BaSDAS will be a useful tool for the analysis of genome-wide screening data and will support the development of novel drugs based on functional genomics information.

• Animal Bioscience
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• v.34 no.8
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• pp.1271-1282
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• 2021

Genome-wide studies provide considerable insights into the genetic background of animals; however, the inheritance of several heritable factors cannot be elucidated. Epigenetics explains these heritabilities, including those of genes influenced by environmental factors. Knowledge of the mechanisms underlying epigenetics enables understanding the processes of gene regulation through interactions with the environment. Recently developed next-generation sequencing (NGS) technologies help understand the interactional changes in epigenetic mechanisms. There are large sets of NGS data available; however, the integrative data analysis approaches still have limitations with regard to reliably interpreting the epigenetic changes. This review focuses on the epigenetic mechanisms and profiling methods and multi-omics integration methods that can provide comprehensive biological insights in animal genetic studies.

• Genomics & Informatics
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• v.16 no.4
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• pp.17.1-17.6
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• 2018

Tumor heterogeneity, the cellular mosaic of multiple lineages arising from the process of clonal evolution, has continued to thwart multi-omics analyses using traditional bulk sequencing methods. The application of single-cell sequencing, in concert with existing genomics methods, has enabled high-resolution interrogation of the genome, transcriptome, epigenome, and proteome. Applied to cancers, these single-cell multi-omics methods bypass previous limitations on data resolution and have enabled a more nuanced understanding of the evolutionary dynamics of tumor progression, immune evasion, metastasis, and treatment resistance. This review details the growing number of novel single-cell multi-omics methods applied to tumors and further discusses recent discoveries emerging from these approaches, especially in regard to immunotherapy.

• IMMUNE NETWORK
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• v.14 no.2
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• pp.73-80
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• 2014

Because autoimmune diseases (AIDs) result from a complex combination of genetic and epigenetic factors, as well as an altered immune response to endogenous or exogenous antigens, systems biology approaches have been widely applied. The use of multi-omics approaches, including blood transcriptomics, genomics, epigenetics, proteomics, and metabolomics, not only allow for the discovery of a number of biomarkers but also will provide new directions for further translational AIDs applications. Systems biology approaches rely on high-throughput techniques with data analysis platforms that leverage the assessment of genes, proteins, metabolites, and network analysis of complex biologic or pathways implicated in specific AID conditions. To facilitate the discovery of validated and qualified biomarkers, better-coordinated multi-omics approaches and standardized translational research, in combination with the skills of biologists, clinicians, engineers, and bioinformaticians, are required.

• Biomedical Science Letters
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• v.21 no.4
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• pp.181-187
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• 2015

Osteoporosis is one of the diseases caused by accumulation of effects from complex interactions between genetic and environmental factors. Aging is the major cause for osteoporosis, which normally increases skeletal fragility and bone fracture especially among the elder. "Omics" refers to a specialized research field dealing with high-throughput biological data, such as genomics, transcriptomics, proteomics or metabolomics. Integration of data from multi-omics has been approved to be a powerful strategy to colligate biological phenomenon with multiple aspects. Actually, integrative analyses of "omics" datasets were used to present pathogenesis of specific diseases or casual biomarkers including susceptible genes. In this study, we evaluated the proposed relationship of novel susceptible genes (TREML2, HTR1E, and GLO1) with osteoporosis, which genes were obtained using multi-omics integration analyses. To this end, SNPs of the susceptible genes in the Korean female cohort were analyzed. As a result, one SNP of HTR1E and five SNPs of TREML2 were identified to associate with osteoporosis. The highest significant SNP was $rs6938076^*$ of TREML2 (OR=0.63, CI: 0.45~0.89, recessive P=0.009). Consequently, the susceptible genes identified through the multi-omics analyses were confirmed to have association with osteoporosis. Therefore, multi-omics analysis might be a powerful tool to find new genes associated with a disease. We further identified that TREML2 has more associated with osteoporosis in females than did HTR1E.

• Genomics & Informatics
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• v.14 no.2
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• pp.46-52
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• 2016

Diverse somatic mutations have been reported to serve as cancer drivers. Recently, it has also been reported that epigenetic regulation is closely related to cancer development. However, the effect of epigenetic changes on cancer is still elusive. In this study, we analyzed DNA methylation data on colon cancer taken from The Caner Genome Atlas. We found that several promoters were significantly hypermethylated in colon cancer patients. Through clustering analysis of differentially methylated DNA regions, we were able to define subgroups of patients and observed clinical features associated with each subgroup. In addition, we analyzed the functional ontology of aberrantly methylated genes and identified the G-protein-coupled receptor signaling pathway as one of the major pathways affected epigenetically. In conclusion, our analysis shows the possibility of characterizing the clinical features of colon cancer subgroups based on DNA methylation patterns and provides lists of important genes and pathways possibly involved in colon cancer development.