Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information. Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.
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Objectives This study presents the standardized protocols developed by the Clinical-Based Human Microbiome Research and Development Project (cHMP) in the Republic of Korea.
Methods It addresses clinical metadata collection, specimen handling, DNA extraction, sequencing methods, and quality control measures for microbiome research.
Results The cHMP involves collecting samples from healthy individuals and patients across various body sites, including the gastrointestinal tract, oral cavity, respiratory system, urogenital tract, and skin. These standardized procedures ensure consistent data quality through controlled specimen collection, storage, transportation, DNA extraction, and sequencing. Sequencing encompasses both amplicon and whole metagenome methods, followed by stringent quality checks. The protocols conform to international guidelines, ensuring that the data generated are both reliable and comparable across microbiome studies.
Conclusion The cHMP underscores the importance of methodological standardization in enhancing data integrity, reproducibility, and advancing microbiome-based research with potential applications for improving human health outcomes.
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<sec><title>Objectives</title><p>An atypical <italic>Shigella flexneri</italic> strain with a plural agglutination pattern [i.e., reacting not only with serum samples containing type antigen II but also with serum samples containing group antigens (3)4 and 7(8)] was selected for genome sequencing, with the aim of obtaining additional comparative information about such strains.</p></sec><sec><title>Methods</title><p>The genomic DNA of atypical <italic>S. flexneri</italic> strain NCCP 15744 was sequenced using an Ion Torrent PGM sequencing machine (Life Technologies, USA). The raw sequence data were preprocessed and reference-assembled in the CLC Assembly Cell software (version 4.0.6; CLC bio, USA).</p></sec><sec><title>Results</title><p>Ion Torrent sequencing produced 1,450,025 single reads with an average length of 144 bp, totaling ~209 Mbp. The NCCP 15744 genome is composed of one chromosome and four plasmids and contains a <italic>gtrX</italic> gene. Among the published genome sequences of <italic>S. flexneri</italic> strains, including 2457T, Sf301, and 2002017, strain NCCP 15744 showed high similarity with strain 2002017. The differences between NCCP 15744 and 2002017 are as follows: i) NCCP 15744 carries four plasmids whereas 2002017 carries five; ii) 19 genes (including <italic>CI</italic>, <italic>CII</italic>, and <italic>cro</italic>) were lost in the <italic>SHI-O</italic> genomic island of NCCP 15744 and six genes were gained as compared with strain 2002017.</p></sec><sec><title>Conclusion</title><p>Strain NCCP 15744 is genetically similar to 2002017, but these two strains have different multilocus sequence types and serotypes. The exact reason is unclear, but the 19 lost genes may be responsible for the atypical seroconversion of strain NCCP 15744.</p></sec>
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Genomic and proteomic characterization of two strains of Shigella flexneri 2 isolated from infants’ stool samples in Argentina Mónica F. Torrez Lamberti, Lucrecia C. Terán, Fabián E. Lopez, María de las Mercedes Pescaretti, Mónica A. Delgado BMC Genomics.2022;[Epub] CrossRef
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