Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impa...Continue Reading
High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites
A library of yeast transcription factor motifs reveals a widespread function for Rsc3 in targeting nucleosome exclusion at promoters
Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data
Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression
Factorbook.org: a Wiki-based database for transcription factor-binding data generated by the ENCODE consortium
Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay
JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles
Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification
Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape
A Small Indel Mutant Mouse Model of Epidermolytic Palmoplantar Keratoderma and Its Application to Mutant-specific shRNA Therapy
Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response
An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency
Enhancing the interpretability of transcription factor binding site prediction using attention mechanism
Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility
Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data
Integrative Genomic Analysis Predicts Causative Cis -Regulatory Mechanisms of the Breast Cancer-Associated Genetic Variant rs4415084
DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.
DeepSRE: Identification of sterol responsive elements and nuclear transcription factors Y proximity in human DNA by Convolutional Neural Network analysis.
Convolutional Neural Networks Grouped by Transcription Factors for Predicting Protein-DNA Binding Site
CREs: Gene & Cell Therapy
Gene and cell therapy advances have shown promising outcomes for several diseases. The role of cis-regulatory elements (CREs) is crucial in the design of gene therapy vectors. Here is the latest research on CREs in gene and cell therapy.