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
  • Mickael Mendez
  • Samantha Wilson
  • Linh Huynh
  • Annie Lu
  • Esther Yoo
  • Luomeng Tan
  • Veronica Alba
  • Natalia Mukhina


  • 2021-06-01: Mehran was awarded the CIHR Post-doctoral Fellowship.
  • 2021-05-26: Michael is a co-author on a paper titled "Viral integration transforms chromatin to drive oncogenesis" accepted for a talk at the Intelligent Systems for Molecular Biology/European Conference on Computational Biology 2021 Conference (ISMB/ECCB 2021) in July 2021.
  • 2021-05-11: Michael is a guest lecturer on the CBW Pathway Workshop where he delivers the Gene Regulation and Motif Analysis lecture on May 11h, 2021.
  • 2021-04-19: Sam is selected for an oral presentation at the International Federation of Placenta Associations annual meeting.
  • 2021-04-19: Sam and Michael are co-authors of "Spiky: standardizing cfMeDIP-seq data with spike-in controls" which will be presented by Lauren M. Harmon (Van Andel Institute) as a short talk at BioC 2021.
  • 2021-04-11: Sam and Michael are co-authors on "Sharing biological data: why, when, and how", an article in FEBS Letters.
  • News archive


  • Twitter: @michaelhoffman
  • LinkedIn: michaelmhoffman