College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.
Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac253.
The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.
从 OMIC 数据中发现合适的分子特征对于确定生物状态、生理状况、疾病病因和治疗反应是必不可少的。然而,所鉴定的特征被报道高度不一致,并且不同生物数据集所鉴定的特征几乎没有重叠。这种不一致性引发了对报告特征可靠性的质疑,并严重阻碍了其在生物学和临床中的应用。在此,构建了一个在线工具 ConSIG,以实现从任何上传的转录组学/蛋白质组学数据中一致地发现基因/蛋白质特征。该工具具有以下独特优势:a)集成了一种新颖的策略,能够显著提高特征发现的一致性;b)通过集体评估确定最佳特征;c)通过丰富疾病/基因本体论来确认生物学相关性。随着对特征一致性和生物学相关性的日益关注,这个在线工具有望成为其他基于 OMIC 的特征发现工具的重要补充。ConSIG 可在无需登录的情况下免费供所有用户使用,网址为 https://idrblab.org/consig/。