Michael M. Hoffman

This study benchmarks CUT&RUN peak callers using motif enrichment, finding that MACS2 outperforms SEACR in most scenarios with optimal preprocessing.

The paper uses cfMeDIP-seq to analyze cell-free placental DNA methylation in maternal plasma, identifying enriched regions associated with preterm birth and IUGR.

The paper introduces effect size-based methods to rank transcription factor binding motifs, showing they outperform p-value rankings under heterogeneous sample sizes.

Segzoo simplifies genome annotation analysis by automating data retrieval and analysis, providing a single visualization of results.

This paper shows that LINE1 RNA is essential for maintaining hESCs by preventing the 8C state through nuclear compartmentalization and PRC2-mediated repression.

The paper develops methods to model transcription factor binding in modified DNA by expanding the DNA alphabet and adapting existing tools to handle custom alphabets, identifying modification-sensitive motifs and validating them with experimental data.

The paper develops a method to simulate cell-free chromatin for epigenetic biomarker discovery, showing it better reflects tissue chromatin features than plasma-derived samples.

The paper identifies an enhancer cluster that drives SOX2 overexpression in breast and lung cancer by altering chromatin accessibility and regulatory mechanisms.

The paper introduces peaKO, a method to optimize ChIP-seq motif analysis using knockout controls, showing improved results compared to existing methods.

This paper introduces synthetic spike-in controls for cfMeDIP-seq to enable accurate and reproducible quantification of DNA methylation, reducing batch effects and technical biases.

Virtual ChIP-seq predicts transcription factor binding in new cell types using neural networks and integrates ChIP-seq data with chromatin accessibility and gene expression.

The paper assesses BED format interoperability using Acidbio, finding most software packages perform poorly and introduces a badge system to improve reliability.

This paper introduces CLA, a method to identify cell-type-specific genes and alternative promoters by integrating Cell Ontology and random forest classification with stratified bootstrapping.

The paper identifies cis-interacting lncRNAs and their targets using RNA-chromatin and Hi-C data across 18 human cell types.

SegRNA identifies unannotated transcripts by analyzing multi-experiment transcriptomic data without relying on pre-existing annotations.

This paper introduces a semi-supervised method for genome annotation that improves accuracy by incorporating expert annotations and evaluates it using a new precision and recall metric.

The paper develops a fully automated method to annotate 164 human cell types using 1,615 genomics datasets, creating a unified encyclopedia of functional DNA elements with a new importance measure.

DNAmod is a database that catalogs DNA modifications, providing information on their chemical properties, sequencing methods, and nomenclature.

BEHST improves genomic set enrichment analysis by linking genomic regions to gene annotations via chromatin interactions, enhancing accuracy for non-coding regions.

The paper introduces Umap and Bismap tools to assess genome and methylome mappability, revealing significant non-uniquely mappable regions in both unmodified and bisulfite-converted genomes.

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.

Segway 2.0 improves genome annotation by using Gaussian mixture models and minibatch training for more accurate signal pattern discovery.

The paper describes using Segway software to annotate genomes with chromatin data from ChIP-seq experiments, providing a semi-automated workflow for interpretation.