Advancing Transcription Factor Binding Site Prediction Using DNA Breathing Dynamics and Sequence Transformers via Cross Attention.

Understanding the impact of genomic variants on transcription factor binding and gene regulation remains a key area of research, with implications for unraveling the complex mechanisms underlying various functional effects. Our study delves into the role of DNA's biophysical properties, including thermodynamic stability, shape, and flexibility in transcription factor (TF) binding. We developed a multi-modal deep learning model integrating these properties with DNA sequence data. Trained on ChIP-Seq (chromatin immunoprecipitation sequencing) data in vivo involving 690 TF-DNA binding events in human genome, our model significantly improves prediction performance in over 660 binding events, with up to 9.6% increase in AUROC metric compared to the baseline model when using no DNA biophysical properties explicitly. Further, we expanded our analysis to in vitro high-throughput Systematic Evolution of Ligands by Exponential enrichment (SELEX) and Protein Binding Microarray (PBM) datasets, comparing our model with established frameworks. The inclusion of DNA breathing features consistently improved TF binding predictions across different cell lines in these datasets. Notably, for complex ChIP-Seq datasets, integrating DNABERT2 with a cross-attention mechanism provided greater predictive capabilities and insights into the mechanisms of disease-related non-coding variants found in genome-wide association studies. This work highlights the importance of DNA biophysical characteristics in TF binding and the effectiveness of multi-modal deep learning models in gene regulation studies.

Examining DNA breathing with pyDNA-EPBD.

MOTIVATION: The two strands of the DNA double helix locally and spontaneously separate and recombine in living cells due to the inherent thermal DNA motion. This dynamics results in transient openings in the double helix and is referred to as "DNA breathing" or "DNA bubbles." The propensity to form local transient openings is important in a wide range of biological processes, such as transcription, replication, and transcription factors binding. However, the modeling and computer simulation of these phenomena, have remained a challenge due to the complex interplay of numerous factors, such as, temperature, salt content, DNA sequence, hydrogen bonding, base stacking, and others. RESULTS: We present pyDNA-EPBD, a parallel software implementation of the Extended Peyrard-Bishop-Dauxois (EPBD) nonlinear DNA model that allows us to describe some features of DNA dynamics in detail. The pyDNA-EPBD generates genomic scale profiles of average base-pair openings, base flipping probability, DNA bubble probability, and calculations of the characteristically dynamic length indicating the number of base pairs statistically significantly affected by a single point mutation using the Markov Chain Monte Carlo algorithm. AVAILABILITY AND IMPLEMENTATION: pyDNA-EPBD is supported across most operating systems and is freely available at https://github.com/lanl/pyDNA_EPBD. Extensive documentation can be found at https://lanl.github.io/pyDNA_EPBD/.

Examining DNA Breathing with pyDNA-EPBD.

Motivation: The two strands of the DNA double helix locally and spontaneously separate and recombine in living cells due to the inherent thermal DNA motion.This dynamics results in transient openings in the double helix and is referred to as "DNA breathing" or "DNA bubbles." The propensity to form local transient openings is important in a wide range of biological processes, such as transcription, replication, and transcription factors binding. However, the modeling and computer simulation of these phenomena, have remained a challenge due to the complex interplay of numerous factors, such as, temperature, salt content, DNA sequence, hydrogen bonding, base stacking, and others. Results: We present pyDNA-EPBD, a parallel software implementation of the Extended Peyrard-Bishop- Dauxois (EPBD) nonlinear DNA model that allows us to describe some features of DNA dynamics in detail. The pyDNA-EPBD generates genomic scale profiles of average base-pair openings, base flipping probability, DNA bubble probability, and calculations of the characteristically dynamic length indicating the number of base pairs statistically significantly affected by a single point mutation using the Markov Chain Monte Carlo (MCMC) algorithm.

Deep residual learning for neuroimaging: An application to predict progression to Alzheimer's disease.

BACKGROUND: The unparalleled performance of deep learning approaches in generic image processing has motivated its extension to neuroimaging data. These approaches learn abstract neuroanatomical and functional brain alterations that could enable exceptional performance in classification of brain disorders, predicting disease progression, and localizing brain abnormalities. NEW METHOD: This work investigates the suitability of a modified form of deep residual neural networks (ResNet) for studying neuroimaging data in the specific application of predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Prediction was conducted first by training the deep models using MCI individuals only, followed by a domain transfer learning version that additionally trained on AD and controls. We also demonstrate a network occlusion based method to localize abnormalities. RESULTS: The implemented framework captured non-linear features that successfully predicted AD progression and also conformed to the spectrum of various clinical scores. In a repeated cross-validated setup, the learnt predictive models showed highly similar peak activations that corresponded to previous AD reports. COMPARISON WITH EXISTING METHODS: The implemented architecture achieved a significant performance improvement over the classical support vector machine and the stacked autoencoder frameworks (p <  0.005), numerically better than state-of-the-art performance using sMRI data alone (> 7% than the second-best performing method) and within 1% of the state-of-the-art performance considering learning using multiple neuroimaging modalities as well. CONCLUSIONS: The explored frameworks reflected the high potential of deep learning architectures in learning subtle predictive features and utility in critical applications such as predicting and understanding disease progression.

CMOS approach to compressed-domain image acquisition.

A hardware implementation of a real-time compressed-domain image acquisition system is demonstrated. The system performs front-end computational imaging, whereby the inner product between an image and an arbitrarily-specified mask is implemented in silicon. The acquisition system is based on an intelligent readout integrated circuit (iROIC) that is capable of providing independent bias voltages to individual detectors, which enables implementation of spatial multiplication with any prescribed mask through a bias-controlled response-modulation mechanism. The modulated pixels are summed up in the image grabber to generate the compressed samples, namely aperture-coded coefficients, of an image. A rigorous bias-selection algorithm is presented to the readout circuit, which exploits the bias-dependent nature of the imager's responsivity. Proven functionality of the hardware in transform coding compressed image acquisition, silicon-level compressive sampling, in pixel nonuniformity correction and hardware-level implementation of region-based enhancement is demonstrated.

An fMRI study of working memory in first-degree unaffected relatives of schizophrenia patients.

Identifying intermediate phenotypes of genetically complex psychiatric illnesses such as schizophrenia is important. First-degree relatives of persons with schizophrenia have increased genetic risk for the disorder and tend to show deficits on working memory (WM) tasks. An open question is the relationship between such behavioral endophenotypes and the corresponding brain activation patterns revealed during functional imaging. We measured task performance during a Sternberg WM task and used functional magnetic resonance imaging (fMRI) to assess whether 23 non-affected first-degree relatives showed altered performance and functional activation compared to 43 matched healthy controls. We predicted that a significant proportion of unaffected first-degree relatives would show either aberrant task performance and/or abnormal related fMRI blood oxygen level dependent (BOLD) patterns. While task performance in the relatives was not different than that of controls they were significantly slower in responding to probes., Schizophrenia relatives displayed reduced activation, most markedly in bilateral dorsolateral/ventrolateral (DLPFC/VLPFC) prefrontal and posterior parietal cortex when encoding stimuli and in bilateral DLPFC and parietal areas during response selection. Additionally, fMRI differences in both conditions were modulated by load, with a parametric increase in between-group differences with load in several key regions during encoding and an opposite effect during response selection.