• Researcher Profile

    John Quackenbush, PhD

    Professor of Computational Biology and Bioinformatics, Harvard T.H. Chan School of Public Health
    Professor of Cancer Biology, Dana-Farber Cancer Institute
    Professor of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
    Director of the Center for Cancer Computational Biology at Dana-Farber Cancer Institute

    Office phone: 617-582-8163
    Fax: 617-582-7760
    Email: johnq@jimmy.harvard.edu
    Website: Functional Genomics and Computational Biology

    Preferred contact method: email

    Research Department

    Biostatistics and Computational Biology

    Area of Research

    Computational Biology and Functional Genomics

    Dana-Farber Cancer Institute
    450 Brookline Avenue
    Smith 822
    Boston, MA 02215


    John Quackenbush earned his PhD in theoretical particle physics from UCLA in 1990 and then completed a postdoctoral fellowship in experimental high energy physics. After receiving a fellowship from the National Center for Human Genome Research, he worked with Glen Evans on the physical mapping of human chromosome 11, and later with Richard Myers and David Cox on large-scale DNA sequencing of chromosomes 21 and 4. In 1998 he joined the faculty at The Institute for Genomic Research (TIGR) where his work focused on the use of genomic and computational methods for the study of human disease. He joined DFCI in 2005 where his work has increasingly focused on the analysis of women's cancers and pulmonary disease, although the methods he and his group develop can be broadly applied. In 2009 he launched the Center for Cancer Computational Biology (CCCB), a Dana-Farber Strategic Plan Center that provides computational support and access to genomic analysis more broadly to the DFCI research community.


    Computational Biology and Functional Genomics

    Every revolution in science has been driven by one thing: data. From the Copernican Revolution to the evolution of quantum mechanics to uncovering the structure and function of DNA, data has enabled us to expand our understanding of the universe and ourselves. The first human genome sequence—its ordered collection of chemical letters—was completed in 2000, allowing us to identify a parts list consisting of the more than 25,000 proteins that make up our cells. Today, new technologies are allowing us to sequence individual genomes for a few thousand dollars and in a matter of days, opening up unprecedented opportunities to discover the associations between the genes encoded in the genome and their manifestations in the physical phenotypes we observe, including their associations with disease. 

    My research focuses on integrating multiple sources of information—genomic data, clinical information, knowledge gleaned from the published scientific literature—to begin to map out the networks that drive processes within cells and to understand how the networks change as diseases like cancer develop and progress. Our hope is that by fully understanding those networks, we can develop strategies to better treat disease and to minimize the impact of therapy on normal, healthy cells.

    Our commitment is to build on what we learn to develop software and tools that will stimulate and facilitate research at DFCI which will be made widely and freely to the broader research community.

    Select Publications

    • Mar JC, Quackenbush J. Decomposition of gene expression state space trajectories. PLoS computational biology. 2009;5(12):e1000626. Epub 2009/12/31. doi: 10.1371/journal.pcbi.1000626. PubMed PMID: 20041215; PubMed Central PMCID: PMC2791157.
    • Mar JC, Wells CA, Quackenbush J. Defining an informativeness metric for clustering gene expression data. Bioinformatics. 2011;27(8):1094-100. Epub 2011/02/19. doi: 10.1093/bioinformatics/btr074. PubMed PMID: 21330289; PubMed Central PMCID: PMC3072547.
    • Mar JC, Matigian NA, Quackenbush J, Wells CA. attract: A method for identifying core pathways that define cellular phenotypes. PloS one. 2011;6(10):e25445. Epub 2011/10/25. doi: 10.1371/journal.pone.0025445. PubMed PMID: 22022396; PubMed Central PMCID: PMC3194807.
    • Mar JC, Matigian NA, Mackay-Sim A, Mellick GD, Sue CM, Silburn PA, McGrath JJ, Quackenbush J, Wells CA. Variance of gene expression identifies altered network constraints in neurological disease. PLoS genetics. 2011;7(8):e1002207. Epub 2011/08/20. doi: 10.1371/journal.pgen.1002207. PubMed PMID: 21852951; PubMed Central PMCID: PMC3154954.
    • Haibe-Kains B, Olsen C, Djebbari A, Bontempi G, Correll M, Bouton C, Quackenbush J. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks. Nucleic acids research. 2012;40(Database issue):D866-75. Epub 2011/11/19. doi: 10.1093/nar/gkr1050. PubMed PMID: 22096235; PubMed Central PMCID: PMC3245161.
    • Haibe-Kains B, Desmedt C, Loi S, Culhane AC, Bontempi G, Quackenbush J, Sotiriou C. A three-gene model to robustly identify breast cancer molecular subtypes. Journal of the National Cancer Institute. 2012;104(4):311-25. Epub 2012/01/21. doi: 10.1093/jnci/djr545. PubMed PMID: 22262870; PubMed Central PMCID: PMC3283537.
    • Bentink S, Haibe-Kains B, Risch T, Fan JB, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane A, Drapkin R, Quackenbush J, Matulonis UA. Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer. PloS one. 2012;7(2):e30269. Epub 2012/02/22. doi: 10.1371/journal.pone.0030269. PubMed PMID: 22348002; PubMed Central PMCID: PMC3278409.
    • Glass K, Huttenhower C, Quackenbush J, Yuan GC. Passing Messages between Biological Networks to Refine Predicted Interactions. PloS one. 2013;8(5):e64832. Epub 2013/06/07. doi: 10.1371/journal.pone.0064832. PubMed PMID: 23741402; PubMed Central PMCID: PMC3669401.
    • Schwede M, Spentzos D, Bentink S, Hofmann O, Haibe-Kains B, Harrington D, Quackenbush J, Culhane AC. Stem cell-like gene expression in ovarian cancer predicts type II subtype and prognosis. PloS one. 2013;8(3):e57799. Epub 2013/03/29. doi: 10.1371/journal.pone.0057799. PubMed PMID: 23536770; PubMed Central PMCID: PMC3594231.
    • Beck AH, Knoblauch NW, Hefti MM, Kaplan J, Schnitt SJ, Culhane AC, Schroeder MS, Risch T, Quackenbush J, Haibe-Kains B. Significance analysis of prognostic signatures. PLoS computational biology. 2013;9(1):e1002875. Epub 2013/02/01. doi: 10.1371/journal.pcbi.1002875. PubMed PMID: 23365551; PubMed Central PMCID: PMC3554539.


    • Culhane, Aedin, PhD


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    • Padi, Megha, PhD
    • Platig, John, PhD
    • Quiroz, Alejandro, PhD
    • Sathirapongsasuti, J. Fah, Ph.D. Student
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