A5025411665- RNA Research and Splicing
- Genomics and Chromatin Dynamics
- RNA and protein synthesis mechanisms
- Plant Molecular Biology Research
- Chaos control and synchronization
- Advanced Chemical Sensor Technologies
- Chromosomal and Genetic Variations
- Congenital heart defects research
- Genomic variations and chromosomal abnormalities
- Complex Systems and Time Series Analysis
- Protein Structure and Dynamics
- Genomics and Phylogenetic Studies
- Cancer-related gene regulation
- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- Hippo pathway signaling and YAP/TAZ
Stowers Institute for Medical Research
2019-2025
Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it difficult differentiate between TFs that drive and those do not. Deep learning models learn complex sequence rules provide an unprecedented opportunity dissect this problem. Using zygotic genome activation in Drosophila as a model, we analyzed high-resolution TF binding chromatin data with interpretable deep performed genetic validation experiments. We...
Signaling pathway components are well studied, but how they mediate cell-type-specific transcription responses is an unresolved problem. Using the Hippo in mouse trophoblast stem cells as a model, we show that DNA binding of signaling effectors driven by sequence rules can be learned genome wide deep learning models. Through model interpretation and experimental validation, motifs for factor TFAP2C enhance TEAD4/YAP1 nucleosome-range distance-dependent manner, driving synergistic enhancer...
Sequence-to-function neural networks learn cis-regulatory rules of many types genomic data from DNA sequence. However, a key challenge is to interpret these models relate the sequence underlying biological processes. This task especially difficult for complex readouts such as MNase-seq, which maps nucleosome occupancy but confounded by experimental bias. To overcome limitations, we introduce pairwise influence attribution (PISA), an interpretation tool that combinatorially decodes bases are...
Summary The arrangement of transcription factor (TF) binding motifs (syntax) is an important part the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution ChIP-nexus profiles pluripotency TFs. develop interpretation tools learn predictive motif representations and identify soft syntax rules for cooperative TF interactions. Strikingly, Nanog preferentially binds with helical periodicity, TFs often cooperate in...
Abstract Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the determinants TF binding, both and context-specific, is essential understand regulation impact regulatory, non-coding genetic variation. Biophysical models trained on vitro binding assays can estimate affinity landscapes...
<h2>Summary</h2> Congenital heart disease often arises from perturbations of transcription factors (TFs) that guide cardiac development. ISLET1 (ISL1) is a TF influences early cell fate, as well differentiation other types including motor neuron progenitors (MNPs) and pancreatic islet cells. While lineage specificity ISL1 function likely achieved through combinatorial interactions, its essential interacting partners are unknown. By assaying genomic occupancy in human induced pluripotent stem...
Summary Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it difficult differentiate between TFs that drive and those do not. Deep learning models learn complex sequence rules provide an unprecedented opportunity dissect this problem. Using zygotic genome activation in Drosophila embryo as a model, we generated high-resolution TF binding chromatin data, analyzed data with interpretable deep learning, performed...
Summary The response to signaling pathways is highly context-specific, and identifying the transcription factors mechanisms that are responsible very challenging. Using Hippo pathway in mouse trophoblast stem cells as a model, we show here this information encoded cis -regulatory sequences can be learned from high-resolution binding data of factors. interpretable deep learning, levels TEAD4 YAP1 enhanced distance-dependent manner by cell type-specific factors, including TFAP2C. We also...