The Computational Biology group at the New York Genome Center is a team of scientists, analysts and programmers who conduct research in cancer genomics, transcriptomics, genetics of Mendelian and complex diseases, statistical genetics, metagenomics, epigenomics, and genome and transcriptome assembly. This work includes the development of tools and analysis pipelines. The Computational Biology Group works […]
Dr. Darnell studies RNA biology and next-generation human genomics, work that arose from his clinical and basic studies of patients with paraneoplastic neurologic disorders (PNDs). PNDs are a group of rare brain diseases thought to arise when tumors – typically breast, ovarian, or lung cancers – start making proteins that are normally only made by […]
Yaniv Erlich’s lab focuses on developing new strategies at the New York Genome Center in order to harness datasets from millions of people through social media and scientific resources. With that information the lab will work to dissect the genetic architecture of complex traits to work toward bridging the missing heritability gap. Erlich’s lab consists […]
The Imielinski lab applies cancer genome assembly to study long range DNA structure in tumors. A key goal of the lab is to understand how complex somatic DNA rearrangements shape the tumor epigenome and drive cancer progression. More fundamentally, the lab is interested in using structural variation as a lens to study genomic plasticity and […]
Technology Innovation @ NYGC The Technology Innovation Lab at New York Genome Center is a creative hub for generating new technologies, protocol development, equipment evaluation, and informatics tool development/integration. Although our interests and focus are constantly evolving due to the fast-paced advancement of genomic technologies, we are committed to developing technologies with the potential to […]
The Landau Lab develops computational and experimental tools to study cancer evolution. Our research is geared towards uncovering basic principles in evolutionary biology, using both patient derived samples and experimental systems for modeling cancer evolution. In addition, we seek to apply this knowledge to designing the next generation of precision medicine tools to overcome cancer evolution, as a […]
Tuuli Lappalainen’s lab studies functional genetic variation in human populations. It is particularly interested in regulatory variation affecting the transcriptome, as well as cellular mechanisms underlying genetic associations to disease. The lab integrates computational analysis of genomic and transcriptomic data with population genetic and experimental analysis. The lab is also affiliated with the Department of […]
Joe Pickrell’s computational genomics lab develops novel statistical approaches to transform large-scale genomic data into improved understanding of human biology and evolution. Much of the Pickrell Lab’s work is focused on understanding natural human variation. What are the evolutionary and molecular mechanisms that lead to differences in phenotypes and disease risk between individuals? The Pickrell […]
Neville Sanjana’s lab develops technologies to understand how human genetic variants cause diseases of the nervous system and cancer. The lab employs a multi-disciplinary approach, combining genome engineering, pooled genetic screens, bioinformatics, electrophysiology, and imaging, to dissect the inner workings of the human genome and its dysfunction in autism and tumor evolution. The Sanjana lab […]
Rahul Satija’s lab studies the causes and consequences of cellular heterogeneity in complex biological systems. His group is particularly interested in single cell genomics, with active development in both the dry and wet lab. The lab integrates novel statistical and machine learning-based methods with experimental analysis in order to better understand how cells work together […]
Dr. Wigler’s research is presently focused on the genomics of cancer and the genetics of autism and related disorders. Together with members of his laboratory they have demonstrated the feasibility of single cell sequencing for genomic analysis and expects this work will eventually improve the targeting of cancer treatments and lead to early and less […]
Vikram Khurana, Jian Peng, Chee Yeun Chung, Pavan K. Auluck, Saranna Fanning, Daniel F. Tardiff, Theresa Bartels, Martina Koeva, Stephen W. Eichhorn, Hadar Benyamini, Yali Lou, Andy Nutter-Upham, Valeriya Baru, Yelena Freyzon, Nurcan Tuncbag, Michael Costanzo, Bryan-Joseph San Luis, David C. Schöndorf, M. Inmaculada Barrasa, Sepehr Ehsani, Neville Sanjana, Quan Zhong, Thomas Gasser, David P. Bartel, Marc Vidal, Michela Deleidi, Charles Boone, Ernest Fraenkel, Bonnie Berger, Susan Lindquist
Cell Systems, Available online 25 January 2017.
Wright JB, Sanjana NE.
Trends Genet. 2016 Sept. PMID: 27423542
Joshi RS, Garg P, Zaitlen N, Lappalainen T, Watson CT, Azam N, Ho D, Li X, Antonarakis SE, Brunner HG, Buiting K, Cheung SW, Coffee B, Eggermann T, Francis D, Geraedts JP, Gimelli G, Jacobson SG, Le Caignec C, de Leeuw N, Liehr T, Mackay DJ, Montgomery SB, Pagnamenta AT, Papenhausen P, Robinson DO, Ruivenkamp C, Schwartz C, Steiner B, Stevenson DA, Surti U, Wassink T, Sharp AJ.
Am J Hum Genet. 2016 Sep 1. PMID: 27569549
Chen W, Hill H, Christie A, Kim MS, Holloman E, Pavia-Jimenez A, Homayoun F, Ma Y, Patel N, Yell P, Hao G, Yousuf Q, Joyce A, Pedrosa I, Geiger H, Zhang H, Chang J, Gardner KH, Bruick RK, Reeves C, Hwang TH, Courtney K, Frenkel E, Sun X, Zojwalla N, Wong T, Rizzi JP, Wallace EM, Josey JA, Xie Y, Xie XJ, Kapur P, McKay RM, Brugarolas J.
Nature. 2016 Sep 5. PMID: 27595394