SkinDB is a comprehensive and user-friendly interactive online platform that is specifically designed to offer users a "one-click" operational mode for identifying novel targets through the integration of multi-omics data pertaining to skin diseases. SkinDB not only streamlines cumbersome and complex data analysis processes but also provides effective guidance for practical operations in laboratories, ultimately enhancing the efficiency and accuracy of research endeavors.
Haoxue Zhang123†, Jingyuan Ning4†, Ke Tang123†, Biao Song5
, Yuyao Liu6 and Shengxiu Liu123
1. Department of Dermatovenerology, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China.
2. Key Laboratory of Dermatology, Ministry of Education, Hefei, Anhui Province, China.
3. Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, Hefei, Anhui Province, China
4. Peking Union Medical College
5. Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
6. Bioinformatics R&D Department, Hefei GuangRe Biotechnology Co., Ltd
† These authors contributed equally to this work.
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Introduction
The skin, as the largest organ of the human body, not only bears the crucial responsibility of shielding the body from external environmental hazards but also plays an important role in various physiological functions such as temperature regulation and tactile sensation. Skin health is intimately linked to an individual's overall health and quality of life. Skin diseases can cause pain, itchiness, and daily disruptions, even threatening life in severe cases. Hence, prioritizing skin health and timely treatment is crucial.
Currently, the extensive use of high-throughput sequencing technology in skin disease research has generated immense data resources. However, these valuable data remain unintegrated and underutilized due to the notable absence of a systematic database or platform, therefore, researchers will encounter numerous challenges when searching for, screening, or processing relevant datasets. Specifically, diverse research teams use various sequencing platforms and techniques, coupled with differing experimental timelines, resulting in inconsistencies in data formats, naming, and quality control standards. These issues not only augment the workload of data preprocessing but also hinder the comparison of one search with another, and lower data sharing efficiency.
For example, when analyzing gene expression profiles, one gene may be recorded using different names due to diverse naming conventions across different databases or literature, posing additional hurdles to data integration. Furthermore, as time progresses, new sequencing technologies and analytical methods continue to emerge, necessitating the reprocessing of older data to align with modern standards, which further complicates the task of data unification.
To tackle the aforementioned problems, there is an immediate necessity for developing a standardized and expandable platform for sharing dermatology data. This platform should possess the following key features: Firstly, it should offer comprehensive and flexible data submission guidelines to guarantee consistency and comparability of the data. Secondly, it should equip users with efficient search engines and data processing tools to facilitate quick information retrieval and preliminary data cleansing. Lastly, it should promote widespread engagement from both academia and industry, ensuring optimal data utilization through collaborative efforts and driving forward research developments in dermatology.
In summary, SkinDB, a comprehensive data platform, has been established. It not only significantly reduces researchers' repetitive efforts and boosts research efficiency, but also accelerates discoveries, ultimately facilitating the identification of more promising molecular targets for treating skin diseases.
Psoriasis
Atopic Dermatitis
Dermatomyositis
Vitiligo
Systemic Lupus Erythematosus
Scleroderma
Our Team
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Shengxiu Liu, MD Anhui Medical University Hefei China Email: liushengxiu@ahmu.edu.cn 研究方向: 皮肤黑色素瘤 | 皮肤激光美容 主要贡献: 项目负责人 |
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Haoxue Zhang, MD Anhui Medical University Hefei China Email: 215672062@qq.com 研究方向: 皮肤黑色素瘤 | 生物信息学 主要贡献: 数据库论文写作 | 项目负责人 |
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Yuyao Liu, Postgraduate Bioinformatics R&D Department, Hefei GuangRe Biotechnology Co., Ltd Hefei China Email: ahmulyy@163.com 研究方向: 皮肤黑色素瘤 | R包开发 | R语言爬虫 | 数据库搭建 主要贡献: 数据库开发 | 数据库运营 | 项目负责人 |
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Ke Tang , Postgraduate Anhui Medical University Hefei China Email: bitter3470792458@163.com 研究方向: 皮肤黑色素瘤 | 数据库搭建 主要贡献: 项目参与者 | 数据库论文写作 |
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Jingyuan Ning, Ph.D Peking Union Medical College Beijing China Email: ningjingyuan@ibms.pumc.edu.cn 研究方向: 生物信息学 主要贡献: 项目参与者 |
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Biao Song, MD Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan China Email: songbiao_derma@tjh.tjmu.edu.cn 研究方向: Basic and clinical research of psoriasis, atopic dermatitis and drug eruption 主要贡献: 项目参与者 |
Help
SKinDB提供了哪些信息
高通量技术领域的最新进展导致多种皮肤病基因表达谱数据的快速积累。在这里，我们开发了SkinDB数据库(https://www.grswsci.top/SkinDB/)，旨在提供跨疾病的皮肤数据集资源，并从多个角度扩展遗传注释。
SKinDB包含哪些数据集
SKinDB数据库内容和构建
SKinDB的开发环境
SKinDB怎么使用分析功能
Acknowledgements
Thanks
Author contributions
Funding information
Competing Interests
We have declared that no competing interest exists.
References
Start preparation time 2024-11-01
Pilot run time 2024-11-20
Formal running time 2024-12-01