Jennifer Hammelman

The paper maps gene expression and chromatin accessibility changes in mouse spinal motor neurons during maturation, revealing dynamic regulatory elements and shared transcription factor motifs.

seqgra is a deep learning pipeline that generates data based on hypothesized regulatory models and identifies neural networks that recover these rules, analyzing performance under varying conditions.

The paper introduces spatzie, an R package and website for detecting significant transcription factor motif co-enrichment in enhancer-promoter interactions using ChIA-PET data.

The paper introduces two deep learning methods to identify cell-type-specific DNA sequence patterns that influence chromatin accessibility and gene expression.

The paper evaluates nine computational methods for identifying and ranking reprogramming factors in directed differentiation, finding that chromatin accessibility-based methods like DeepAccess and AME perform best.

The paper introduces MIAA, a method to measure DNA accessibility changes caused by synthetic oligonucleotide motifs, and identifies key transcription factor motifs influencing chromatin accessibility in stem cells and endoderm.