John Quackenbush

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Genomics has transformed biological science not by producing genome sequences and gene catalogs for a range of species, but rather through the development of technologies that allow us to survey, on a global scale, organisms and their gene, protein, and metabolic patterns of expression. The challenge is no longer how to generate these vast bodies of genomic data, but rather in how to best collect, manage, and analyze the data. As a community, we have a long history of studying biological systems and our best strategy moving forward is to leverage that knowledge so as to best interpret genome scale datasets. Our research group focuses on methods spanning the laboratory to the laptop that are designed to use genomic and computational approaches to reveal the underlying biology. In particular, we have been looking at patterns of gene expression in cancer with the goal of elucidating the networks and pathways that are fundamental in the development and progression of the disease.



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. He joined DFCI in 2005.


Recent Publications

Larson JL, Huttenhower C, Quackenbush J, Yuan GC. A tiered hidden Markov modelcharacterizes multi-scale chromatin states. Genomics. 2013 Apr 6. doi:pii: S0888-7543(13)00062-1. 10.1016/j.ygeno.2013.03.009. [Epub ahead of print]

Kelly AD, Hill KE, Correll M, Hu L, Wang Y, Rubio R, Duan S, Quackenbush J, Spentzos D. Next-generation sequencing and microarray-based interrogation of microRNAs from formalin-fixed, paraffin-embedded tissue: Preliminary assessment of cross-platform concordance. Genomics. 2013 Apr 3.

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.

De Rienzo A, Richards WG, Yeap BY, Coleman MH, Sugarbaker PE, Chirieac LR, Wang YE, Quackenbush J, Jensen RV, Bueno R. Sequential Binary Gene Ratio Tests Define a Novel Molecular Diagnostic Strategy for Malignant Pleural Mesothelioma. Clin Cancer Res. 2013 May 1;19(9):2493-2502. Epub 2013 Mar 14.

Correll M, Johnson CK, Ferrari G, Brizzio M, Mak AW, Quackenbush J, Shaw RE, Zapolanski A, Grau JB. Mutational analysis clopidogrel resistance and platelet function in patients scheduled for coronary artery bypass grafting. Genomics. 2013 Feb 24.

Zhou X, Qiu W, Sathirapongsasuti JF, Cho MH, Mancini JD, Lao T, Thibault DM, Litonjua AA, Bakke PS, Gulsvik A, Lomas DA, Beaty TH, Hersh CP, Anderson C, Geigenmuller U, Raby BA, Rennard SI, Perrella MA, Choi AM, Quackenbush J, Silverman EK. Gene expression analysis uncovers novel hedgehog interacting protein (HHIP) effects in human bronchial epithelial cells. Genomics. 2013 May;101(5):263-72.

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 Comput Biol. 2013 Jan;9(1):e1002875.

Papillon-Cavanagh S, De Jay N, Hachem N, Olsen C, Bontempi G, Aerts HJ, Quackenbush J, Haibe-Kains B. Comparison and validation of genomic predictors for anticancer drug sensitivity. J Am Med Inform Assoc. 2013 Jan 26. [Epub ahead of print]

Kelly AD, Haibe-Kains B, Janeway KA, Hill KE, Howe E, Goldsmith J, Kurek K, Perez-Atayde AR, Francoeur N, Fan JB, April C, Schneider H, Gebhardt MC, Culhane A, Quackenbush J, Spentzos D. MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32. Genome Med. 2013 Jan 22;5(1):2. [Epub ahead of print]

Schröder MS, Gusenleitner D, Quackenbush J, Culhane AC, Haibe-Kains B.
RamiGO: an R/Bioconductor package providing an AmiGO visualize interface. Bioinformatics. 2013 Mar 1;29(5):666-8.


Related Links

The Computational Biology and Functional Genomics Laboratory - Our research work, centered at the Dana-Farber Cancer Center, has produced a wide range of publicly available resources and software tools available through our group's website.


Center for Cancer Computational Biology (CCCB) - The mission of the CCCB is to further the Institute’s commitment to advance the understanding, diagnosis, treatment, cure, and prevention of cancer and related diseases. We aim to do that by providing broad-based support for the analysis and interpretation of ‘omic data and in doing so further basic, clinical, and translational research and to conduct research that opens new ways of understanding human cancer.