Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data.
Qian QinJingyu FanRongbin ZhengChangxin WanShenglin MeiQiu WuHanfei SunMyles BrownJing ZhangClifford A MeyerX Shirley LiuPublished in: Genome biology (2020)
We developed Lisa (http://lisa.cistrome.org/) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes and outperformed alternative methods in identifying the perturbed TRs.
Keyphrases
- genome wide
- dna methylation
- transcription factor
- genome wide identification
- copy number
- gene expression
- high throughput
- circulating tumor cells
- dna damage
- healthcare
- single cell
- molecular docking
- cancer therapy
- oxidative stress
- emergency department
- single molecule
- machine learning
- electronic health record
- fluorescence imaging
- molecular dynamics simulations
- drug induced