Biology subjects

Inan, T. T.

Publications and source records attributed to Inan, T. T..

2 recordsLinked to original sources

bioRxiv2024

Efficient High-Throughput DNA Breathing Features Generation Using Jax-EPBD

DNA breathing dynamics--transient base-pair opening and closing due to thermal fluctuations--are vital for processes like transcription, replication, and repair. Traditional models, such as the Extended Peyrard-Bishop-Dauxois (EPBD), provide insights into these dynamics but are computationally limited for long sequences. We present JAX-EPBD, a high-throughput Langevin molecular dynamics framework leveraging JAX for GPU-accelerated simulations, achieving up to 30x speedup and superior scalability compared to the original C-based EPBD implementation. JAX-EPBD efficiently captures time-dependent behaviors, including bubble lifetimes and base flipping kinetics, enabling genome-scale analyses. Applying it to transcription factor (TF) binding affinity prediction using SELEX datasets, we observed consistent improvements in R2 values when incorporating breathing features with sequence data. Validating on the 77-bp AAV P5 promoter, JAX-EPBD revealed sequence-specific differences in bubble dynamics correlating with transcriptional activity. These findings establish JAX-EPBD as a powerful and scalable tool for understanding DNA breathing dynamics and their role in gene regulation and transcription factor binding.

bioinformatics↗

bioRxiv2024

Scalable DNA Feature Generation and Transcription Factor Binding Prediction via Deep Surrogate Models

Simulating DNA breathing dynamics, for instance Extended Peyrard-Bishop-Dauxois (EPBD) model, across the entire human genome using traditional biophysical methods like pyDNA-EPBD is computationally prohibitive due to intensive techniques such as Markov Chain Monte Carlo (MCMC) and Langevin dynamics. To overcome this limitation, we propose a deep surrogate generative model utilizing a conditional Denoising Diffusion Probabilistic Model (DDPM) trained on DNA sequence-EPBD feature pairs. This surrogate model efficiently generates high-fidelity DNA breathing features conditioned on DNA sequences, reducing computational time from months to hours-a speedup of over 1000 times. By integrating these features into the EPBDxDNABERT-2 model, we enhance the accuracy of transcription factor (TF) binding site predictions. Experiments demonstrate that the surrogate-generated features perform comparably to those obtained from the original EPBD framework, validating the models efficacy and fidelity. This advancement enables real-time, genome-wide analyses, significantly accelerating genomic research and offering powerful tools for disease understanding and therapeutic development.

genomics↗