Dana Pe'er | |
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Born | 1971 (age 51–52) [1] |
Alma mater | Hebrew University of Jerusalem [2] |
Spouse | Itsik Pe'er [3] |
Children | Inbar, Carmel [4] [5] |
Awards |
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Scientific career | |
Fields | Biology Computational biology Systems biology |
Institutions | Harvard Medical School Columbia University Memorial Sloan Kettering Cancer Center |
Thesis | From Gene Expression to Molecular Pathways (2003) |
Doctoral advisor | Nir Friedman |
Other academic advisors | George M. Church |
Dana Pe'er (born 1971), Chair and Professor in Computational and Systems Biology Program at Sloan Kettering Institute is a researcher in computational systems biology. A Howard Hughes Medical Institute (HHMI) Investigator since 2021, she was previously a professor at Columbia Department of Biological Sciences. Pe'er's research focuses on understanding the organization, function and evolution of molecular networks, particularly how genetic variations alter the regulatory network and how these genetic variations can cause cancer.
Pe'er was born in Israel. [7] Her husband, Itsik Pe'er, is a computational biologist at Columbia University. Together, they have raised two daughters. [3]
Pe'er received a bachelor's degree in mathematics in 1995, as well as master's in 1999 and PhD degrees in computer science in 2003, from the Hebrew University of Jerusalem. She earned her master's degree under Avi Widgerson, and carried out her PhD research in the lab of Nir Friedman. [8] [9] [10]
She subsequently performed postdoctoral work with George Church at Harvard. [8] [9] [10] Her fellowship focused on how genetic variation changes the regulatory network between individuals and how this subsequently manifests in phenotypic diversity. [11] [12] [13]
In 2006, Pe'er established a research group in the Department of Biological Sciences and Systems Biology at Columbia University. Pe'er's group at Columbia developed computational methods that combine diverse sources of high throughput genomics data, with the aim of developing a holistic view of the cell at a systems level. [14]
In 2016, Pe'er joined the Sloan Kettering Institute in New York City. [15]
She was selected as a Howard Hughes Medical Institute (HHMI) Investigator in September, 2021. [16]
Pe'er is involved in the Human Cell Atlas as a member of the organizing committee, co-chair of the Analysis Working Group, and member of the Human Lung Cell Atlas initiative, and serves on the scientific advisory board of scverse. [17]
In her PhD work, Pe'er demonstrated that Bayesian networks can describe interactions between thousands of genes, enabling the analysis of data from newly available DNA microarrays, which generate thousands of noisy measurements of gene expression. [18] The approach has been widely applied to genome-scale sequencing data. In her postdoctoral work, she used this framework to study protein signaling networks in multivariate flow cytometry data. [19]
At Columbia, Pe'er applied Bayesian networks to integrate different data types for the study of gene regulatory networks, determining how DNA sequence variation alters the regulation of gene expression, with a view towards personalized medicine. [20]
The Pe'er research group has developed a series of methods for high-throughput single-cell data analysis, initially to address a new high-dimensional data type derived from mass cytometry, which quantifies a few dozen proteins per cell for millions of cells at a time. They introduced the application of non-linear dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) to visualize high-dimensional single-cell RNA sequencing data, [21] and the use of a nearest neighbors graph to represent the data manifold of RNA-defined cell states. [22] The Pe'er group used this formalization to identify discrete cell types or cell states by applying the Louvain community detection method to cluster data, [23] and demonstrated that cells can be ordered along differentiation trajectories from individual samples, due to the asynchrony of cells found in tissue samples. [22] By modeling trajectories as a Markov process, they showed that cells can be assigned probabilities for reaching any given terminal fate along a trajectory. [24] In 2020, the Pe'er and Fabian Theis groups presented CellRank, an algorithm that uncovers cellular dynamics by combining trajectories based on cell-cell similarity with local RNA velocity information, which identifies nascent transcriptional states by the proportion of spliced-to-unspliced RNA transcripts. [25]
Pe'er applies these methods to model biological questions around cellular plasticity and single-cell phenotypic variation in cancer, developmental biology, and immunology, including tumor microenvironments, [26] metastasis [26] and responses to treatments such as immunotherapy. "We are beginning to understand that plasticity is a key hallmark of cancer," said Dr. Pe'er. "It is the cancer cell's plasticity that allows it to make such a switch to survive."
Upon accepting the International Society for Computational Biology's Overton Prize in 2014, Pe'er said, "Math is rigorous, and biology is messy, so the trick is to find the pattern in the mess, and machine learning provides a powerful toolbox." [13]
David Haussler is an American bioinformatician known for his work leading the team that assembled the first human genome sequence in the race to complete the Human Genome Project and subsequently for comparative genome analysis that deepens understanding the molecular function and evolution of the genome.
Temple Ferris Smith is an emeritus professor in biomedical engineering who helped to develop the Smith-Waterman algorithm with Michael Waterman in 1981. The Smith-Waterman algorithm serves as the basis for multi sequence comparisons, identifying the segment with the maximum local sequence similarity, see sequence alignment. This algorithm is used for identifying similar DNA, RNA and protein segments. He was director of the BioMolecular Engineering Research Center at Boston University for twenty years and is now professor emeritus.
Eugene Wimberly "Gene" Myers, Jr. is an American computer scientist and bioinformatician, who is best known for contributing to the early development of the NCBI's BLAST tool for sequence analysis.
The International Society for Computational Biology (ISCB) is a scholarly society for researchers in computational biology and bioinformatics. The society was founded in 1997 to provide a stable financial home for the Intelligent Systems for Molecular Biology (ISMB) conference and has grown to become a larger society working towards advancing understanding of living systems through computation and for communicating scientific advances worldwide.
The ISCB Overton Prize is a computational biology prize awarded annually for outstanding accomplishment by a scientist in the early to mid stage of his or her career. Laureates have made significant contribution to the field of computational biology either through research, education, service, or a combination of the three.
The ISCB Accomplishment by a Senior Scientist Award is an annual prize awarded by the International Society for Computational Biology for contributions to the field of computational biology.
Trey Ideker is a professor of medicine and bioengineering at UC San Diego. He is the Director of the National Resource for Network Biology, the San Diego Center for Systems Biology, and the Cancer Cell Map Initiative. He uses genome-scale measurements to construct network models of cellular processes and disease.
Eran Segal is a computational biologist professor at the Weizmann Institute of Science. He works on developing quantitative models for all levels of gene regulation, including transcription, chromatin, and translation. Segal also works as an epidemiologist.
Aviv Regev is a computational biologist and systems biologist and Executive Vice President and Head of Genentech Research and Early Development in Genentech/Roche. She is a core member at the Broad Institute of MIT and Harvard and professor at the Department of Biology of the Massachusetts Institute of Technology. Regev is a pioneer of single cell genomics and of computational and systems biology of gene regulatory circuits. She founded and leads the Human Cell Atlas project, together with Sarah Teichmann.
Nir Friedman is an Israeli Professor of Computer Science and Biology at the Hebrew University of Jerusalem.
Chris Sander is a computational biologist based at the Dana-Farber Cancer Center and Harvard Medical School. Previously he was chair of the Computational Biology Programme at the Memorial Sloan–Kettering Cancer Center in New York City. In 2015, he moved his lab to the Dana–Farber Cancer Institute and the Cell Biology Department at Harvard Medical School.
Mass cytometry is a mass spectrometry technique based on inductively coupled plasma mass spectrometry and time of flight mass spectrometry used for the determination of the properties of cells (cytometry). In this approach, antibodies are conjugated with isotopically pure elements, and these antibodies are used to label cellular proteins. Cells are nebulized and sent through an argon plasma, which ionizes the metal-conjugated antibodies. The metal signals are then analyzed by a time-of-flight mass spectrometer. The approach overcomes limitations of spectral overlap in flow cytometry by utilizing discrete isotopes as a reporter system instead of traditional fluorophores which have broad emission spectra.
Ron Shamir is an Israeli professor of computer science known for his work in graph theory and in computational biology. He holds the Raymond and Beverly Sackler Chair in Bioinformatics, and is the founder and former head of the Edmond J. Safra Center for Bioinformatics at Tel Aviv University.
Gary Stormo is an American geneticist and currently Joseph Erlanger Professor in the Department of Genetics and the Center for Genome Sciences and Systems Biology at Washington University School of Medicine in St Louis. He is considered one of the pioneers of bioinformatics and genomics. His research combines experimental and computational approaches in order to identify and predict regulatory sequences in DNA and RNA, and their contributions to the regulatory networks that control gene expression.
Ziv Bar-Joseph is an Israeli computational biologist and Professor in the Computational Biology Department and the Machine Learning Department at the Carnegie Mellon School of Computer Science.
Hanah Margalit is a Professor in the faculty of medicine at the Hebrew University of Jerusalem. Her research combines bioinformatics, computational biology and systems biology, specifically in the fields of gene regulation in bacteria and eukaryotes.
Trajectory inference or pseudotemporal ordering is a computational technique used in single-cell transcriptomics to determine the pattern of a dynamic process experienced by cells and then arrange cells based on their progression through the process. Single-cell protocols have much higher levels of noise than bulk RNA-seq, so a common step in a single-cell transcriptomics workflow is the clustering of cells into subgroups. Clustering can contend with this inherent variation by combining the signal from many cells, while allowing for the identification of cell types. However, some differences in gene expression between cells are the result of dynamic processes such as the cell cycle, cell differentiation, or response to an external stimuli. Trajectory inference seeks to characterize such differences by placing cells along a continuous path that represents the evolution of the process rather than dividing cells into discrete clusters. In some methods this is done by projecting cells onto an axis called pseudotime which represents the progression through the process.
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Smita Krishnaswamy is an American scientist and associate professor in genetics and computer science at Yale University. She specializes in the development of machine learning techniques to analyze high-dimensional high-throughput biomedical data with applications in immunology, immunotherapy, cancer, neuroscience, developmental biology and health outcomes. She organized the Open Problems in Single-Cell Biology effort with the Chan Zuckerberg Initiative and remains a scientific advisor for the project.