Recent advances in genomic technologies have generated large-scale protein-DNA interaction data and open chromatic regions for multiple plant species. To predict condition-specific gene regulatory networks using these data, we developed the Condition Specific Regulatory network inference engine (ConSReg), which combines heterogeneous genomic data using sparse linear model followed by feature selection and stability selection to select key regulatory genes. ConsReg was developed as a Python package. The process of integrating genomic data and inferring regulatory networks is summarized in the flowchart below.
Song Q, Lee J, Akter S, Grene R, Li S: Accurate prediction of condition-specific regulatory maps in Arabidopsis using integrated genomic data. Nucleic Acids Research, Volume 48, Issue 11, 19 June 2020, Page e62, https://doi.org/10.1093/nar/gkaa264
ConSReg Python package is available on our GitHub repository: https://github.com/LiLabAtVT/ConSReg