Hoffman research group

Michael M. Hoffman

We develop machine learning techniques to better understand chromatin biology. These models and algorithms transform high-dimensional functional genomics data into interpretable patterns and lead to new biological insight.


  • Segway: semi-automated genomic annotation
  • Segtools: segmentation analysis and graphics
  • Genomedata: efficient storage and retrieval
  • Umap and Bismap: tools for quantifying mappability of the genome and methylome
  • BEHST: biological enrichment of hidden sequence targets
  • Virchip: predicting transcription factor binding by learning from the transcriptome
  • PeaKO: finding transcription factor binding motifs using knockout controls


  • DNAmod: the DNA modification database


Meet the team

  • Michael Hoffman
  • Eric Roberts
  • Coby Viner
  • Mehran Karimzadeh
  • Jai Sood
  • Mickael Mendez
  • Rachel Chan
  • Danielle Denisko
  • Francis Nguyen
  • Samantha Wilson
  • Matthew McNeil
  • Yi Nian (Jeffrey) Niu
  • Siddharth Reed
  • Yushan (Ida) Liu
  • Michael Wrana
  • Natalia Mukhina


  • 2020-02-13: Michael is the representative for Medical Biophysics on the program committee of the Collaborative Graduate Specialization Program in Genome Biology and Bioinformatics (GBB), University of Toronto.
  • 2020-02-13: Mehran and Michael are the co-authors of "Viral integration transforms chromatin to drive oncogenesis", a preprint on bioRxiv Bioinformatics.
  • 2020-02-07: Michael is a guest lecturer on the CBW Pathway Workshop where he will teach the Regulatory Network Analysis lecture on July 28th, 2020.
  • 2020-02-03: Rachel, Matthew, Eric, Mickael, and Michael are the co-authors of "Semi-supervised segmentation and genome annotation", a preprint on bioRxiv Bioinformatics.
  • 2020-01-29: Eric releases Segway 3.0
  • 2020-01-26: The 46 Questions for Scientists features an interview with Michael.
  • News archive


  • Twitter: @michaelhoffman
  • LinkedIn: michaelmhoffman