I’m a computational biologist with a PhD in Physics/Biophysics. I study 3D genome organization and how it controls gene regulation. My work involves developing pipelines to handle complex genomic data, implementing deep learning models to make predictions, and running physics-based simulations to test ideas.
- Modeling 3D Genome folding and its regulatory role
- Applications of machine learning models in biology
- Developing bioinformatic pipeline for next-generation sequencing (NGS) data
Developed OccuFold, a Nextflow Pipeline for Predicting 3D Genome Folding from Single-Molecule Footprinting
Deep learning model to infer 3D chromatin structure using single-molecule data (e.g., methylation footprints). Integrated sequence and occupancy data to predict genome folding and validate with Hi-C.
Reproducible pipeline for ChIP-seq analysis, including FRiP score computation using Bowtie2, samtools, and MACS2. Built for easy adoption and reproducibility.
Mechanistic model of CTCF/cohesin loop extrusion using dynamic barriers. Built in Python + OpenMM. Validated predictions with Hi-C and compartment scores.
Developing EnformerBindPredict,
reproducible Nextflow pipeline for predicting CTCF occupancy from multi-omic data using a hybrid CNN–Enformer model.
Python package to quantify structural features (e.g., TADs, loops) from contact maps. Optimized for simulated Hi-C data. (Repo: chromoscores)
Contributed to modeling functionality and maintenance of open-source codebase, particularly looplib, a package for 1D DNA loop extrusion.
Computational Biology & Bioinformatics: NGS data analysis, Genomics & Multi‑Omics data (Hi‑C, ATAC‑seq, ChIP‑seq, RNA‑seq), Tools: Cooltools, Biopython, Bioconductor
Modeling & Simulation: Molecular dynamics (OpenMM, GROMACS, AMBER), Finite element modeling: FEniCS
Machine Learning & Data Analysis: ML models: clustering, regression, neural networks, CNNs, NLP, Libraries: PyTorch, scikit‑learn, XGBoost
Programming & Scripting: Python (NumPy, SciPy, Pandas),R, Bash / Shell scripting
Workflow & Pipeline Development: Workflow management(Nextflow, Snakemake), Version control (Git, GitHub), Cloud & HPC computing(AWS, USC HPC), Containerization(Singularity)
📚 Selected Publications Google Scholar
- Rahmaninejad H, et al. Dynamic Barriers Modulate Cohesin Positioning and Genome Folding at Fixed Occupancy, Genome Research (2025)
- Rahmaninejad H, et al. Stimuli-Responsive Polymer Brushes in Nanofluidic Channels, ACS Appl. Mater. Interfaces (2023)
- Rahmaninejad H, et al. Crowding within synaptic junctions influences the degradation of nucleotides by CD39 and CD73 ectonucleotidases, Biophysical Journal (2022)
- Rahmaninejad H, et al. Co-localization and confinement of ecto-nucleotidases modulate extracellular adenosine nucleotide distributions, PLoS computational biology (2020)