Zhana Duren1, Fengge Chang2, Fnu Naqing2, Jingxue Xin3, Qiao Liu3, Wing Hung Wong4. 1. Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, 29646, USA. zduren@clemson.edu. 2. Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, 29646, USA. 3. Department of Statistics, Department of Biomedical Data Science and Bio-X Program, Stanford University, Stanford, CA, 94305, USA. 4. Department of Statistics, Department of Biomedical Data Science and Bio-X Program, Stanford University, Stanford, CA, 94305, USA. whwong@stanford.edu.
Correction: Genome Biol 23, 114 (2022)https://doi.org/10.1186/s13059-022-02682-2Following publication of the original paper [1], the authors have reported an error in reference genome version of the HiC data used for validation of the RE-TG interactions. After using the correct version of the HiC anchor locations, the result in Fig. 4D, supplementary Figure S11, and S12 are all improved.
Fig. 4
D. Validation of RE-TG prediction by HiC data on Naïve CD4 T cell
D. Validation of RE-TG prediction by HiC data on Naïve CD4 T cellAdditional file 1: Supplementary Figure S11. Validation of RE-TG prediction by HiC data. Consistency ratio of predicted RE and promoter capture HiC data on different cell types of. We can see in all cell type, scREG predict the greatest number of same RE-TG pairs as previously found promoter capture HiC data. set select distribution distance same with scREG, does improve the performance. Supplementary Figure S12. AUROC and AUPR of RE-TG predictio by taking HiC data as ground truth.