John Quackenbush

Professor
This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Research

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.

 

Biography

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

Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, Ye Z, Makrigiorgos GM, Fennessy FMM, Mak RH, Gillies RJ, Quackenbush J, Aerts H. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res. 2017 May 31. pii: canres.0122.2017.

 

Hill KE, Kelly AD, Kuijjer ML, Barry W, Rattani A, Garbutt CC, Kissick H, Janeway K, Perez-Atayde A, Goldsmith J, Gebhardt MC, Arredouani MS, Cote G, Hornicek F, Choy E, Duan Z, Quackenbush J, Haibe-Kains B, Spentzos D. An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets. J
Hematol Oncol. 2017 May 15;10(1):107.

 

Yuan GC, Cai L, Elowitz M, Enver T, Fan G, Guo G, Irizarry R, Kharchenko P, Kim J, Orkin S, Quackenbush J, Saadatpour A, Schroeder T, Shivdasani R, Tirosh I. Challenges and emerging directions in single-cell analysis. Genome Biol. 2017 May 8;18(1):84.

 

Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J. Estimating Gene Regulatory Networks with pandaR. Bioinformatics. 2017 Mar 11.

 

Morrow JD, Zhou X, Lao T, Jiang Z, DeMeo DL, Cho MH, Qiu W, Cloonan S, Pinto-Plata V, Celli B, Marchetti N, Criner GJ, Bueno R, Washko GR, Glass K, Quackenbush J, Choi AM, Silverman EK, Hersh CP. Functional interactors of three genome-wide association study genes are differentially expressed in severe chronic obstructive pulmonary disease lung tissue. Sci Rep. 2017 Mar 13;7:44232.

 

Domenyuk V, Zhong Z, Stark A, Xiao N, O'Neill HA, Wei X, Wang J, Tinder TT, Tonapi S, Duncan J, Hornung T, Hunter A, Miglarese MR, Schorr J, Halbert DD, Quackenbush J, Poste G, Berry DA, Mayer G, Famulok M, Spetzler D. Plasma Exosome Profiling of Cancer Patients by a Next Generation Systems Biology Approach. Sci Rep. 2017 Feb 20;7:42741.

 

Quiroz-Zárate A, Harshfield BJ, Hu R, Knoblauch N, Beck AH, Hankinson SE, Carey V, Tamimi RM, Hunter DJ, Quackenbush J, Hazra A. Expression Quantitative Trait loci (QTL) in tumor adjacent normal breast tissue and breast tumor tissue. PLoS One. 2017 Feb 2;12(2):e0170181.

 

Campbell PT, Rebbeck TR, Nishihara R, Beck AH, Begg CB, Bogdanov AA, Cao Y, Coleman HG, Freeman GJ, Heng YJ, Huttenhower C, Irizarry RA, Kip NS, Michor F, Nevo D, Peters U, Phipps AI, Poole EM, Qian ZR, Quackenbush J, Robins H, Rogan PK, Slattery ML, Smith-Warner SA, Song M, VanderWeele TJ, Xia D, Zabor EC, Zhang X, Wang M, Ogino S. Proceedings of the third international molecular pathological epidemiology (MPE) meeting. Cancer Causes Control. 2017 Feb;28(2):167-176.

 

Safikhani Z, El-Hachem N, Smirnov P, Freeman M, Goldenberg A, Birkbak NJ, Beck AH, Aerts HJ, Quackenbush J, Haibe-Kains B. Safikhani et al. reply. Nature. 2016 Nov 30;540(7631):E2-E4.

 

Safikhani Z, El-Hachem N, Smirnov P, Freeman M, Goldenberg A, Birkbak NJ, Beck AH, Aerts HJ, Quackenbush J, Haibe-Kains B. Safikhani et al. reply. Nature. 2016 Nov 30;540(7631):E11-E12.

 

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.