Inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data.
Lisa (epigenetic Landscape In Silico deletion Analysis) predicts the transcription factors most likely to regulate a user-supplied gene set by integrating tens of thousands of public ChIP-seq and chromatin accessibility datasets (Qin et al., 2020). It models the chromatin landscape around each gene and uses in silico deletion of regulatory elements to score candidate regulators.
Lisa is widely used by experimental and computational groups to generate mechanistic hypotheses from differential expression results, screen hits, and disease-associated gene panels. It is part of the broader Cistrome ecosystem (Mei et al., 2017), sharing its curated data foundation. Source code and a Python package are available on GitHub.
References
2020
Genome Biol
Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data
@article{Qin2020Lisa,title={{Lisa}: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and {ChIP-seq} data},author={Qin, Qian and Fan, J and Zheng, R and Wan, C and Mei, S and Wu, Q and Sun, H and Brown, M and Zhang, J and Meyer, C A and Liu, X S},journal={Genome Biology},volume={21},number={1},pages={32},year={2020},}
2017
NAR
Cistrome Data Browser: a data portal for ChIP-seq and chromatin accessibility data in human and mouse
@article{Mei2017CistromeDataBrowser,title={{Cistrome Data Browser}: a data portal for {ChIP-seq} and chromatin accessibility data in human and mouse},author={Mei, S and Qin, Qian and Wu, Q and Sun, H and Zheng, R and Zang, C and Zhu, M and Wu, J and Shi, X and Taing, L and Liu, T and Brown, M and Meyer, C A and Liu, X S},journal={Nucleic Acids Research},volume={45},number={D1},pages={D658--D662},year={2017},}