Refine
Year of publication
Language
- English (43)
Has Fulltext
- yes (43)
Is part of the Bibliography
- no (43)
Keywords
- Chromatin accessibility (3)
- DNase1-seq (3)
- ENCODE-DREAM in vivo Transcription Factor binding site prediction challenge (2)
- Ensemble learning (2)
- Epigenetics (2)
- Gene regulation (2)
- Indirect-binding (2)
- TF-complexes (2)
- Transcription Factors (2)
- Actinomycin D (1)
- Anandamide (1)
- Atrial fibrillation classification (1)
- Automated sRNA analysis (1)
- BCL2 (1)
- Bidirectional genes (1)
- CCL2 (1)
- Chemosensitivity (1)
- Chemotherapy (1)
- Chromatin conformation (1)
- Comparative analysis (1)
- Computational sRNA analysis (1)
- Convolutional Neural Networks (1)
- Data mining (1)
- Data processing (1)
- EGFR inhibitor (1)
- Endocrinology (1)
- Energy-efficiency (1)
- Eukaryotic sRNA (1)
- GWAS (1)
- Gene expression (1)
- Gene expression prediction (1)
- HDAC4 (1)
- HepG2 (1)
- HiC (1)
- HiChIP (1)
- Huh7 (1)
- Inflammation (1)
- JAK2V617F (1)
- Liver cancer (1)
- MALAT1 (1)
- MYC (1)
- Machine Learning (1)
- Machine learning (1)
- Multi-Omics (1)
- Multimodal Modeling (1)
- NASH (1)
- NCoR1 (1)
- NEAT1 (1)
- NEAT2 (1)
- Personalized Medicine (1)
- Predictive Modeling (1)
- Sequencing (1)
- Single-cell RNA-seq (1)
- Small RNA analysis (1)
- TSC22D3 (1)
- Therapy response (1)
- VEGFA (1)
- WoMo score (1)
- acneiform skin toxicity (1)
- aligners (1)
- antiepileptic drugs (1)
- antiseizure (1)
- behavioral adverse events (1)
- benchmarking (1)
- bleeding (1)
- cancer (1)
- cancer-associated fibroblasts (1)
- cellular immunology (1)
- chemoresistance (1)
- chronic kidney disease (1)
- cytokines (1)
- drug‐resistant epilepsy (1)
- essential thrombocythemia (ET) (1)
- flow cytometry (1)
- focal seizures (1)
- gene signature (1)
- genetic generalized epilepsy (1)
- inflammation (1)
- lamotrigine (1)
- levetiracetam (1)
- liver cancer (1)
- lncRNA (1)
- mRNA decay (1)
- mRNA stability (1)
- mammary carcinoma (1)
- mappers (1)
- mapping-algorithms (1)
- mononuclear cell (1)
- mouse models (1)
- myeloproliferative neoplasms (MPN) (1)
- noninterventional (1)
- pharmacoresistance (1)
- polycythemia vera (PV) (1)
- primary myelofibrosis (PMF) (1)
- reactive oxygen species (1)
- renal dysfunction (1)
- sRNA (1)
- sRNA tool (1)
- siRNA analysis (1)
- siRNA quantification (1)
- single-cell RNA sequencing (1)
- steroid control of aging (1)
- thromboembolism (1)
- thrombosis (1)
- transcriptional profiling (1)
- transcriptomics (1)
- valproic acid (1)
- women (1)
The transcription factor vitamin D receptor (VDR) is the high affinity nuclear target of the biologically active form of vitamin D3 (1,25(OH)2D3). In order to identify pure genomic transcriptional effects of 1,25(OH)2D3, we used VDR cistrome, transcriptome and open chromatin data, obtained from the human monocytic cell line THP-1, for a novel hierarchical analysis applying three bioinformatics approaches. We predicted 75.6% of all early 1,25(OH)2D3-responding (2.5 or 4 h) and 57.4% of the late differentially expressed genes (24 h) to be primary VDR target genes. VDR knockout led to a complete loss of 1,25(OH)2D3–induced genome-wide gene regulation. Thus, there was no indication of any VDR-independent non-genomic actions of 1,25(OH)2D3 modulating its transcriptional response. Among the predicted primary VDR target genes, 47 were coding for transcription factors and thus may mediate secondary 1,25(OH)2D3 responses. CEBPA and ETS1 ChIP-seq data and RNA-seq following CEBPA knockdown were used to validate the predicted regulation of secondary vitamin D target genes by both transcription factors. In conclusion, a directional network containing 47 partly novel primary VDR target transcription factors describes secondary responses in a highly complex vitamin D signaling cascade. The central transcription factor VDR is indispensable for all transcriptome-wide effects of the nuclear hormone.
Highlights
• Single nucleotide variants (SNVs) may affect transcription factor (TF) binding
• Fast statistical approach to assess significance of differential TF binding for SNVs
• Validate new approach on in vitro and in vivo TF binding assays
• Applications on GWAS SNVs and large eQTL studies illustrate utility
Summary
Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type-specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.
Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.