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This galley proof is being listed electronically before publishing the final manuscript (It's not final version).

 
Multi-Omics Analysis Sandbox Toolkit for Swift Derivations of Clinically Relevant Genesets and Biomarkers
Jin-Young Lee 1,*,# (Research Worker), Won Park2,# (Research Worker), Hyunjoong Kim3 (Research Worker), Hong Seok Lee1 (Professor), Tae-Wook Kang 2 (CEO), Dong-Hun Shin 2 (Research Worker), Kyung su Kim3 (Research Worker), Yoon Kyeong Lee 3 (Research Worker), Seon-Young Kim 4 (Professor), Ji Hwan Park 4 (Research Worker), Young-Joon Kim 1 (Professor)
1Department of Biochemistry, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea,
2the Moagen, Inc, Daejeon, South Korea,
3CoreIT, Inc, Cheongju, South Korea,
4Korea Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
Abstract
The utilization of multi-omics research has gained popularity in clinical investigations; however, effectively managing and merging extensive and diverse datasets presents a challenge due to its intricacy. This research introduces the Multi-Omics Analysis Sandbox Toolkit, an online platform designed to facilitate the exploration, integration, and visualization of datasets ranging from single-omics to multi-omics. This platform establishes connections between clinical data and omics information, allowing for versatile analysis and storage of both single and multi-omics data. Additionally, users can repeatedly utilize and exchange their findings within the platform. The toolkit offers diverse alternatives for data selection and gene set analysis, along with presenting visualization outputs, potential candidates, and annotations. Furthermore, the platform empowers users to collaborate by sharing their datasets, analyses, and conclusions with others, thus enhancing its utility as a collaborative research tool. The Multi-Omics Analysis Sandbox Toolkit stands as a valuable asset in comprehensively grasping the influence of diverse factors in diseases and pinpointing potential biomarkers.
Abstract, Accepted Manuscript [Submitted on August 24, 2023, Accepted on February 5, 2024]
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