• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Molecules and Cells
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• v.46 no.2
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• pp.74-85
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• 2023

Single-cell research has provided a breakthrough in biology to understand heterogeneous cell groups, such as tissues and organs, in development and disease. Molecular barcoding and subsequent sequencing technology insert a single-cell barcode into isolated single cells, allowing separation cell by cell. Given that multimodal information from a cell defines precise cellular states, recent technical advances in methods focus on simultaneously extracting multimodal data recorded in different biological materials (DNA, RNA, protein, etc.). This review summarizes recently developed single-cell multiomics approaches regarding genome, epigenome, and protein profiles with the transcriptome. In particular, we focus on how to anchor or tag molecules from a cell, improve throughputs with sample multiplexing, and record lineages, and we further discuss the future developments of the technology.

• Molecules and Cells
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• v.44 no.11
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• pp.843-850
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• 2021

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

• Journal of Ginseng Research
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• v.48 no.1
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• pp.103-111
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• 2024

Background: Ginseng (Panax ginseng Mayer) is an important natural medicine. However, a long culture period and challenging quality control requirements limit its further use. Although artificial cultivation can yield a sustainable medicinal supply, research on the association between the transplantation and chaining of metabolic networks, especially the regulation of ginsenoside biosynthetic pathways, is limited. Methods: Herein, we performed Liquid chromatography tandem mass spectrometry based metabolomic measurements to evaluate ginsenoside accumulation and categorise differentially abundant metabolites (DAMs). Transcriptome measurements using an Illumina Platform were then conducted to probe the landscape of genetic alterations in ginseng at various ages in transplantation mode. Using pathway data and crosstalk DAMs obtained by MapMan, we constructed a metabolic profile of transplantation Ginseng. Results: Accumulation of active ingredients was not obvious during the first 4 years (in the field), but following transplantation, the ginsenoside content increased significantly from 6-8 years (in the wild). Glycerolipid metabolism and Glycerophospholipid metabolism were the most significant metabolic pathways, as Lipids and lipid-like molecule affected the yield of ginsenosides. Starch and sucrose were the most active metabolic pathways during transplantation Ginseng growth. Conclusion: This study expands our understanding of metabolic network features and the accumulation of specific compounds during different growth stages of this perennial herbaceous plant when growing in transplantation mode. The findings provide a basis for selecting the optimal transplanting time.

• Animal Bioscience
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• v.37 no.6
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• pp.982-992
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• 2024

Objective: Jining Grey goat is a local Chinese goat breed that is well known for its high fertility and excellent meat quality but shows low meat production performance. Numerous studies have focused on revealing the genetic mechanism of its high fertility, but its highlighting meat quality and muscle growth mechanism still need to be studied. Methods: In this research, an integrative analysis of the genomics and transcriptomics of Jining Grey goats compared with Boer goats was performed to identify candidate genes and pathways related to the mechanisms of meat quality and muscle development. Results: Our results overlap among five genes (ABHD2, FN1, PGM2L1, PRKAG3, RAVER2) and detected a set of candidate genes associated with fatty acid metabolism (PRKAG3, HADHB, FASN, ACADM), amino acid metabolism (KMT2C, PLOD3, NSD2, SETDB1, STT3B, MAN1A2, BCKDHB, NAT8L, P4HA3) and muscle development (MSTN, PPARGC1A, ANKRD2). Several pathways have also been detected, such as the FoxO signaling pathway and Apelin signaling pathway that play roles in lipid metabolism, lysine degradation, N-glycan biosynthesis, valine, leucine and isoleucine degradation that involving with amino acid metabolism. Conclusion: The comparative genomic and transcriptomic analysis of Jining Grey goat and Boer goat revealed the mechanisms underlying the meat quality and meat productive performance of goats. These results provide valuable information for future breeding of goats.