Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box motifs
Marri, D. K.; Filipovic, D.; Kana, O.; Tischkau, S.; Bhattacharya, S.
Show abstract
The mammalian circadian clock is based on a core intracellular gene regulatory network, coordinated by communication between the central nervous system and peripheral tissues like the liver. Transcriptional and translational feedback loops underlie the molecular mechanism of circadian oscillation and generate its 24 h periodicity. The Brain and muscle Arnt-like protein-1 (Bmal1) forms a heterodimer with Circadian Locomotor Output Cycles Kaput (Clock) that binds to E-box gene regulatory elements, activating transcription of clock genes. In this work we aimed to develop a predictive model of genome-wide CLOCK-BMAL1 binding to E-box motifs. We found over-representation of the canonical E-box motif CACGTG in BMAL1-bound regions in accessible chromatin of the mouse liver, heart and kidney. We developed three different tissue-specific machine learning models based on DNA sequence, DNA sequence plus DNA shape, and DNA sequence and shape plus histone modifications. Combining DNA sequence with DNA shape and histone modification features yielded improved transcription factor binding site prediction. Further, we identified the genomic and epigenomic features that best correlate to the binding of BMAL1 to DNA. The DNA shape features Electrostatic Potential, Minor Groove Width and Propeller Twist together with the histone modifications H3K27ac, H3K4me1, H3K36me3, and H3K4me3 were the features most highly predictive of DNA binding by BMAL1 across all three tissues.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- To mock or not: a comprehensive comparison of mock IP and DNA input for ChIP-seq 95%
- ANANSE: An enhancer network-based computational approach for predicting key transcription factors in cell fate determination 94%
- CTCF-dependent chromatin boundaries formed by asymmetric nucleosome arrays with decreased linker length 94%
Similar papers in this journal
- Integrative Ranking Of Enhancer Networks Facilitates The Discovery Of Epigenetic Markers In Cancer 94%
- RESIC: A tool for comprehensive adenosine to inosine RNA Editing Site Identification and Classification 92%
- PENGUINN: Precise Exploration of Nuclear G-quadruplexes Using Interpretable Neural Networks 92%
Similar papers in this journal
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data 94%
- Quantifying the Tissue-Specific Regulatory Information within Enhancer DNA Sequences 93%
- Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.