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.
Citation:
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.
This study is part of the Sparkle Platform and operates as a dedicated sub-project within its ecosystem