作者
Qi Song, Jiyoung Lee, Shamima Akter, Matthew Rogers, Ruth Grene, Song Li
发表日期
2020/6/19
期刊
Nucleic Acids Research
卷号
48
期号
11
页码范围
e62-e62
出版商
Oxford University Press
简介
Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the …
学术搜索中的文章
Q Song, J Lee, S Akter, M Rogers, R Grene, S Li - Nucleic Acids Research, 2020