Anusri Pampari

Flexible use of conserved motif vocabularies constrains genome access in cell type evolution

The paper shows that conserved motif vocabularies constrain genome access in cell type evolution across species.

Mutagenesis Sensitivity Mapping of HumanEnhancers In Vivo

The paper maps the mutagenic sensitivity of human enhancers in vivo by systematically mutating enhancer sequences and using predictive modeling to identify critical nucleotides.

Rewriting regulatory DNA to dissect and reprogram gene expression

The paper introduces Variant-FlowFISH to measure the effects of DNA sequence variants on gene expression, identifies regulatory mechanisms, and benchmarks predictive models.

Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage

The paper uses transfer learning to predict how TF dosage affects chromatin accessibility, finding that motif affinity and distribution determine sensitivity or buffering of TF concentration changes.

An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation

The paper expands the ENCODE Registry of candidate cis-regulatory elements (cCREs) to include 2.35 million human and 927 thousand mouse cCREs, revealing novel regulatory functions and identifying KLF1 as a causal gene for red blood cell traits.

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants

Learning and removing Tn5/DNase-I sequence bias lets a small base-resolution CNN (ChromBPNet) recover TF motifs and footprints from accessibility data and rival Enformer at predicting chromatin QTL effects.

A consensus variant-to-function score to functionally prioritize variants for disease

This paper introduces a consensus variant-to-function score to prioritize disease-causing variants, showing improved performance in heritability enrichment and fine-mapping.

Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogramming

The paper uses deep learning to analyze chromatin accessibility and gene expression during reprogramming, revealing how TF concentrations and motif syntax influence cis-regulatory networks.

Chromatin accessibility is a two-tier process regulated by transcription factor pioneering and enhancer activation

The paper uses deep learning to analyze chromatin accessibility in Drosophila embryos, revealing a two-phase process driven by pioneer TFs and enhancer-activating TFs.

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

The paper uses a single-cell multiome atlas of the human retina and deep learning to identify causal noncoding variants in eye diseases.

The dynseq genome browser track enables visualization of context-specific, dynamic DNA sequence features at single nucleotide resolution

The paper introduces a genome browser track for visualizing dynamic DNA sequence features at single nucleotide resolution using machine learning-derived scores.