Michael Hoffman

Picture of Dr. Michael Hoffman

Assistant Professor

Ph.D.
Princess Margaret Cancer Centre
Toronto Medical Discovery Tower
101 College Street, Room 11-311
Toronto, Ontario M5G 1L7

Julie Relatado, Administrative Assistant
Phone: +1 (416) 634-8789
Email Julie Relatado

Personal Website

http://hoffmanlab.org/

At A Glance:

Changes in gene regulation often underlie the mechanism of genetic disorders and cancer. These changes can arise from variation in genomic DNA sequence. They can also come from alterations in epigenomic properties, such as DNA methylation, chromatin packaging, histone modifications, or 3D chromosome conformation. New sequencing technology reveals a forest of genomic and epigenomic variation, but we are hindered by insufficient understanding of the variation's consequences. As a result, we can apply these data to diagnosis or personalized drug therapy only in limited cases.

Our research program addresses this gap in knowledge to understand interactions between genome, epigenome, and phenotype in human cancers. We apply a systematic framework to create and validate predictive models of (1) how genetic variants cause epigenomic changes, and (2) the effect of epigenomic changes on gene regulation and phenotype. First, we start with data from collaborators or public resources, using cancer cell lines and cancer patient primary tissue. Second, we develop machine learning models of how a genomic or epigenomic input leads to an epigenomic or phenotypic output. Third, we perturb input data and predict changes in output. Fourth, we validate predictions with targeted experiments.

Short Bio:

Michael Hoffman is a principal investigator at the Princess Margaret Cancer Centre and Assistant Professor in the Departments of Medical Biophysics and Computer Science, University of Toronto. He researches the application of machine learning techniques to epigenomic data. He previously led the National Institutes of Health ENCODE Project's large-scale integration task group while at the University of Washington. He has a PhD from the University of Cambridge, where he conducted computational genomics studies at the European Bioinformatics Institute. He also has a B.S. in Biochemistry and a B.A. in the Plan II Honors Program at The University of Texas at Austin. He was named a CIHR New Investigator and has received several awards for his academic work, including the NIH K99/R00 Pathway to Independence Award and the Ontario Early Researcher Award.

 

Major Contributions:

Advances in research. I transformed epigenomic analysis by creating the genome annotation method Segway. Segway analyzes multiple epigenomic datasets, integrates them, and categorizes each base in a genome (e.g. transcription start, enhancer, insulator, repressed). Segway enables simple interpretation and visualization of large multivariate genomic datasets. I led an effort to annotate the human genome using Segway—a linchpin of the ENCODE analysis, which shifted thinking about the biomedical importance of noncoding DNA.

Segway's global impact is demonstrated by the many scientists who run the software or use our annotations on human cell types. These annotations are displayed by both the Ensembl (50,000 unique users/week), and UCSC (38,000 unique/week) genome browsers. Segway annotations also form a building block for highly-used noncoding interpretation tools like CADD and the Ensembl Regulatory Build. In other work, I created Sunflower, a theoretical framework to predict effects of genetic variation on transcription factor (TF) binding, originating a widespread "motif-breaker" approach.

Training a new generation. My past students are in PhD programs at Princeton, University of Toronto, University of Washington, and University of Maryland. They have received the NSF Graduate Research Fellowship, Canadian Graduate Scholarship, and Ontario Graduate Scholarships.

 

List of Key Publications:

Link to Pubmed Publications
Link to Google Scholar

Lundberg SM, Tu WB, Raught B, Penn LZ, Hoffman MM, Lee SI. “ChromNet: learning the human chromatin network from all ENCODE ChIP-seq data.” Genome Biol. 2016; 17:82.

Viner C, Hoffman MM. “Determining the epigenome using DNA alone.” Nat Methods. 2015 Mar; 12:191–2.

Hoffman MM*, Ernst J*, Wilder SP, Kundaje A, Harris RS, Libbrecht M, Giardine B, Ellenbogen PM, Bilmes JA, Birney E, Hardison RC, Dunham I, Kellis M, Noble WS. “Integrative annotation of chromatin elements from ENCODE data.” Nucleic Acids Res. 2013 Jan; 41:827–41.

ENCODE Project Consortium. “An integrated Encyclopedia of DNA Elements in the human genome.” Nature. 2012 Sep 6; 489:57–74.

Hoffman MM, Buske OJ, Wang J, Weng Z, Bilmes JA, Noble WS. “Unsupervised pattern discovery in human chromatin structure through genomic segmentation.” Nat Methods. 2012 Mar 18; 9:473–6.

Hoffman MM, Buske OJ, Noble WS. “The Genomedata format for storing large-scale functional genomics data.” Bioinformatics. 2010 Jun 1; 26:1458–9.

Hoffman MM, Birney E. “An effective model for natural selection in promoters.” Genome Res. 2010 May; 20:685–92.