• Title/Summary/Keyword: Omics big data

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Application of Iipidomics in food science (식품분야에서 Iipidomics 분석 기술의 활용)

  • Kim, Hyun-Jin;Jang, Gwang-Ju;Lee, Hyeon-Jeong;Kim, Bo-Min;Oh, Juhong
    • Food Science and Industry
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    • v.50 no.1
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    • pp.16-25
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    • 2017
  • There is no doubt that accumulation of big data using multi-omics technologies will be useful to solve human's long-standing problems such as development of personalized diet and medicine, overcoming diseases, and longevity. However, in the food industry, big data based on omics is scarcely accumulated. In particular, comprehensive analysis of molecular lipid metabolites directly associated with food quality, such as taste, flavor, and texture has been very limited. Moreover, most of food lipidomics studies are applied to analyze lipid components and discriminate authenticity and freshness of limited foods including vegetable and fish oil. However, if lipid big data through food lipidomics research of various foods and materials can be accumulated, lipidomics can be used in the optimization of food processing, production, delivery system, food safety, and storage as well as functional food.

Modulation of senoinflammation by calorie restriction based on biochemical and Omics big data analysis

  • Bang, EunJin;Lee, Bonggi;Noh, Sang-Gyun;Kim, Dae Hyun;Jung, Hee Jin;Ha, Sugyeong;Yu, Byung Pal;Chung, Hae Young
    • BMB Reports
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    • v.52 no.1
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    • pp.56-63
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    • 2019
  • Aging is a complex and progressive process characterized by physiological and functional decline with time that increases susceptibility to diseases. Aged-related functional change is accompanied by a low-grade, unresolved chronic inflammation as a major underlying mechanism. In order to explain aging in the context of chronic inflammation, a new integrative concept on age-related chronic inflammation is necessary that encompasses much broader and wider characteristics of cells, tissues, organs, systems, and interactions between immune and non-immune cells, metabolic and non-metabolic organs. We have previously proposed a novel concept of senescent (seno)-inflammation and provided its frameworks. This review summarizes senoinflammation concept and additionally elaborates modulation of senoinflammation by calorie restriction (CR). Based on aging and CR studies and systems-biological analysis of Omics big data, we observed that senescence associated secretory phenotype (SASP) primarily composed of cytokines and chemokines was notably upregulated during aging whereas CR suppressed them. This result further strengthens the novel concept of senoinflammation in aging process. Collectively, such evidence of senoinflammation and modulatory role of CR provide insights into aging mechanism and potential interventions, thereby promoting healthy longevity.

Perspectives of Integrative Cancer Genomics in Next Generation Sequencing Era

  • Kwon, So-Mee;Cho, Hyun-Woo;Choi, Ji-Hye;Jee, Byul-A;Jo, Yun-A;Woo, Hyun-Goo
    • Genomics & Informatics
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    • v.10 no.2
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    • pp.69-73
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    • 2012
  • The explosive development of genomics technologies including microarrays and next generation sequencing (NGS) has provided comprehensive maps of cancer genomes, including the expression of mRNAs and microRNAs, DNA copy numbers, sequence variations, and epigenetic changes. These genome-wide profiles of the genetic aberrations could reveal the candidates for diagnostic and/or prognostic biomarkers as well as mechanistic insights into tumor development and progression. Recent efforts to establish the huge cancer genome compendium and integrative omics analyses, so-called "integromics", have extended our understanding on the cancer genome, showing its daunting complexity and heterogeneity. However, the challenges of the structured integration, sharing, and interpretation of the big omics data still remain to be resolved. Here, we review several issues raised in cancer omics data analysis, including NGS, focusing particularly on the study design and analysis strategies. This might be helpful to understand the current trends and strategies of the rapidly evolving cancer genomics research.

Prospects of omics-driven synthetic biology for sustainable agriculture

  • Soyoung Park;Sung-Dug Oh;Vimalraj Mani;Jin A Kim;Kihun Ha;Soo-Kwon Park;Kijong Lee
    • Korean Journal of Agricultural Science
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    • v.49 no.4
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    • pp.801-812
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    • 2022
  • Omics-driven synthetic biology is a multidisciplinary research field that creates new artificial life by employing genetic components, biological devices, and engineering technique based on genetic knowledge and technological expertise. It is also utilized to make valuable biomaterials with limited production via current organisms faster, more efficient, and in huge quantities. As the bioeconomic age begins, and the global synthetic biology market becomes more competitive, investment in research and development (R&D) and associated sectors has grown considerably. By overcoming the constraints of present biotechnologies through the merging of big data and artificial intelligence technologies, huge ripple effects are envisaged in the pharmaceutical, chemical, and energy industries. In agriculture, synthetic biology is being used to solve current agricultural problems and develop sustainable agricultural systems by increasing crop productivity, implementing low-carbon agriculture, and developing plant-based, high-value-added bio-materials such as vaccines for diagnosing and preventing livestock diseases. As international regulatory debates on synthetic biology are now underway, discussions should also take place in our country for the growth of bioindustries and the dissemination of research findings. Furthermore, the system must be improved to facilitate practical application and to enhance the risk evaluation technology and management system.

Bioinformatics services for analyzing massive genomic datasets

  • Ko, Gunhwan;Kim, Pan-Gyu;Cho, Youngbum;Jeong, Seongmun;Kim, Jae-Yoon;Kim, Kyoung Hyoun;Lee, Ho-Yeon;Han, Jiyeon;Yu, Namhee;Ham, Seokjin;Jang, Insoon;Kang, Byunghee;Shin, Sunguk;Kim, Lian;Lee, Seung-Won;Nam, Dougu;Kim, Jihyun F.;Kim, Namshin;Kim, Seon-Young;Lee, Sanghyuk;Roh, Tae-Young;Lee, Byungwook
    • Genomics & Informatics
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    • v.18 no.1
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    • pp.8.1-8.10
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    • 2020
  • 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/.