Imputation for transcription factor binding predictions based on deep learning

By Qian Qin, Jianxing Feng. 2017

98 citations recorded by the source corpus.

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 impact of SNPs on TF binding.

Subfields: Molecular Biology

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Citations in the corpus

Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding (2017, background): “…et al. , 2012; Kumar and Bucher, 2016), (sparse) logistic regression (Natarajan et al. , 2012; Luo and Hartemink, 2012; K¨ah¨ar¨a and L¨ahdesm¨aki, 2015; Chen et al. , 2017), random forests (Liu et al. , 2017), or neural networks adapted by deep learning (Quang and Xie, 2017; Qin and Feng, 2017).”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, methodology): “bles the visualization of the combinatorial interactions among histone modifications via a novel optimization-based technique that generates feature pattern maps from the learned deep model. TFImpute [65] https://bitbucket.org/feeld ead/tfimpute Predicts cell-specific TF binding for TF-cell line com”
FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (2017, methodology): “Another recent method, TFImpute (Qin and Feng, 2017), shares 76 many similarities with FactorNet.”