ConSIG: consistent discovery of molecular signature from OMIC data. Issue 4 (27th June 2022)

• Record Type: Journal Article
• Authors: Li, Fengcheng; Yin, Jiayi; Lu, Mingkun; Yang, Qingxia; Zeng, Zhenyu; Zhang, Bing; Li, Zhaorong; Qiu, Yunqing; Dai, Haibin; Chen, Yuzong; Zhu, Feng
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 4(2022)
• Publication Date: 2022-06-27
• Subjects: Omics -- Signature discovery -- Feature selection -- Signature consistency -- Classification accuracy; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac253
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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/