Dana Pe'er

Dana Pe'er

Titles

Associate Professor, Departments of Biological Sciences and Computer Science

Affiliations

Department of Systems Biology
Center for Computational Biology and Bioinformatics
Center for Multiscale Analysis of Genomic and Cellular Networks (MAGNet)
Department of Biological Sciences
Department of Computer Science

Phone

(212) 854-4397

Dana Pe’er is an associate professor in the Departments of Biological Sciences and Computer Science. Her lab endeavors to understand the organization, function, and evolution of molecular networks, particularly how variation in DNA sequence alters regulatory networks and leads to the vivid phenotypic diversity of life. Her team develops computational methods that integrate diverse high-throughput data to provide a holistic, systems-level view of molecular networks. She is particularly interested in exploring how systems biology can be used to personalize care for people with cancer. By developing models that can predict how individual tumors will respond to certain drugs and drug combinations, her goal is to develop ways to determine the best drug regime for each patient. Her interest is not only in understanding which molecular components go wrong in cancer cells, but also in using this information to improve cancer therapeutics.

Dr. Pe’er is the recipient of the 2014 Overton Prize, and has been recognized with the Burroughs Wellcome Fund Career Award, an NIH Directors New Innovator Award, an NSF CAREER Award, and a Stand Up To Cancer Innovative Research Grant. She was also named a Packard Fellow in Science and Engineering.


Education History


Selected Publications

Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Simonds EF, Chen TJ, Shenfeld DK, Nolan GP, Pe'er D. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014 Apr 24;157(3):714-25.

Amir el-AD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC, Shenfeld DK, Krishnaswamy S, Nolan GP, Pe'er D. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol. 2013 Jun;31(6):545-52.

Pe'er D, Hacohen N. Principles and strategies for developing network models in cancer. Cell. 2011 Mar 18;144(6):864-73, March 2011.

Akavia UD, Litvin O, Kim J, Sanchez-Garcia F, Causton HC, Pochanard P,  Mozes E,  Kotliar D, Garraway LA, Pe'er D. An integrated approach to uncover drivers of cancer.  Cell. 2010 Dec 10;143(6):1005-17.

Litvin O, Chen BJ, Causton HC, Pe’er D. Modularity and interactions in the genetics of gene expression. Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6441-6.

Sachs K*, Perez O*, Pe’er D*, Lauffenburger D, Nolan G. Causal protein-signaling networks derived from multiparameter single-cell data. Science. 2005 Apr 22;308(5721):523-9. *These authors contributed equally.


Address

Mailing Address:
Columbia University
Department of Biological Sciences
1212 Amsterdam Avenue
New York, NY 10027

Office Location: 
Fairchild 607


Lab Staff

Research Staff

Research Staff

Helen Causton

Associate Research Scientist

Associate Research Scientist

Postdoctoral Scientists

Postdoctoral Scientists

Andreja Jovic

Postdoctoral Research Fellow

Postdoctoral Research Fellow

Postdoctoral Scientists

Smita Krishnaswamy

Postdoctoral Research Scientist

Postdoctoral Research Scientist

Postdoctoral Scientists

Manu Setty

Postdoctoral Research Scientist

Postdoctoral Research Scientist

Postdoctoral Scientists

Zhenmao Wan

Postdoctoral Research Scientist

Postdoctoral Research Scientist

Graduate Students

Graduate Students

Ambrose Carr

Graduate Student

Graduate Student

Graduate Students

Jacob Levine

Graduate Student

Graduate Student

Undergraduate Students

Undergraduate Students

Jiyoung Kim

Undergraduate Student

Undergraduate Student

Former Lab Members

Former Lab Members

Uri-David Akavia

Assistant Professor, Department of Biochemistry, McGill University

Assistant Professor, Department of Biochemistry, McGill University

Former Lab Members

El-Ad David Amir

Data Scientist, Custora

Data Scientist, Custora

Former Lab Members

Bo-Juen Chen

Bioinformatics Scientist, New York Genome Center

Bioinformatics Scientist, New York Genome Center

Former Lab Members

Jaline Geradin

Graduate Student, University of California, San Francisco

Graduate Student, University of California, San Francisco

Former Lab Members

Dylan Kotliar

MD/PhD Student, Harvard University

MD/PhD Student, Harvard University

Former Lab Members

Oren Litvin

Software Engineer, Google

Software Engineer, Google

Former Lab Members

Marta Luksza

Fellow, Institute for Advanced Study

Fellow, Institute for Advanced Study

Former Lab Members

Eual Moses

Programmer, National Cancer Institute

Programmer, National Cancer Institute

Former Lab Members

Mark Rocco

MD Student, Tulane University

MD Student, Tulane University

Former Lab Members

Tanya Schild

PhD Student, Weill-Cornell Medical Center

PhD Student, Weill-Cornell Medical Center

Former Lab Members

Anna Starikov

Medical Student, Cornell University

Medical Student, Cornell University

Former Lab Members

Yossi Tzur

Medical Student, Tel Aviv University

Medical Student, Tel Aviv University

Former Lab Members

Dani Valevski

Medical Student, Tel Aviv University

Medical Student, Tel Aviv University


Career Opportunities

We are currently have openings for:

  • Postdoctoral fellows
  • Graduate students (Columbia students only)
  • Undergraduate students
  • Staff programmer

Exceptional candidates are welcome to contact Dana Pe'er for more information about our research projects. In order to join the lab as a PhD student, you must first gain admittance to one of the graduate programs at Columbia University. Please e-mail a brief description of your research interests, background and CV to  Dana Pe'er .

Postdoctoral positions

Computational Biology and Machine Learning

Positions available for computational biologists with strong machine learning skill and desire to have a significant impact on biomedical research. The project involves the development of new computational algorithms and statistical approaches that integrate diverse types of high-throughput data towards modeling and understanding patient specific cancer networks and their response to drug, a step towards personalized medicine. We have access to unique datasets that measure genetics, genomics and single cell proteomics of individual cancers, following drug and other perturbations (Pe'er and HaCohen, Cell March 2011, Bendall et.el., Science May 2011). Our lab is an interdisciplinary environment, the computational postdoc will participate in the experimental design of the data, work side by side with biological and clinical collaborators and will have opportunity for biological validation of model predictions. The ideal candidate would have prior training in both machine learning and computational biology. Exceptional candidates with strong background in only one of machine learning, theoretical physic with strong programming abilities or computational biology will be considered.

Genomic Cancer Biology

Position available for researcher with extensive knowledge and background in cancer genomics or systems biology of signaling. Projects involve using high-throughput technologies to collect genomic and proteomic data probing tumor networks, developing novel tools and using available in-house tools to analyze this data integrated with additional available data (TCGA, ENCODE) to identify driver mutations, learn how these alter cell signal processing and arm a cell with the abilities to proliferate abnormally and evade drugs. The ideal candidate would have training in cancer genomics, genetics or bioengineering, with some computational programming background. Exceptional candidates with similar backgrounds will be considered.


News

Researchers Create Comprehensive Map of Human B Cell Development
A method that uses new single-cell technologies will improve researchers’ ability to investigate development in cells of all types and to identify rare aberrations in development that lead to disease.
Dana Pe’er Wins 2014 Overton Prize
The award from the International Society for Computational Biology recognizes one outstanding early- to mid-career scientist each year who has already made a significant contribution to the field.
New Tool for Visualizing High-Dimensional Single-Cell Data
A new software tool called viSNE can reveal closely related groups of cells within heterogeneous cell populations. It could potentially help identify drug-resistant cancer cells and detect minute quantities of cells that increase risk of relapse.
Computational and Functional Dissection of Drug Targets in Melanoma
CONEXIC, a novel Bayesian probabilistic algorithm developed in the lab of Dana Pe'er, integrates copy number and gene expression data in order to identify tumor-specific “driver” aberrations, as well as the cellular processes they affect.
Dana Pe'er Receives 2007 NIH Director's New Innovator Award
Part of an NIH Roadmap for Medical Research initiative, this award recognizes outstanding scientists who are "well-positioned to make significant — and potentially transformative — discoveries in a variety of areas.”