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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change.
Mohammadi P, Castel SE, Brown AA, Lappalainen T.
Genome Research. 2017 Oct. 11.
High-Throughput Approaches to Pinpoint Function within the Noncoding Genome.
Montalbano A, Canver, MC and Sanjana NE.
Molecular Cell. 2017 Oct. 5.
Genomic Patterns of De Novo Mutation in Simplex Autism.
Turner TN, Coe BP, Dickel DE, Hoekzema K, Nelson BJ, Zody MC, Kronenberg ZN, Hormozdiari F, Raja A, Pennacchio LA, Darnell RB,
Cell. 2017 September 28.
Interpreting short tandem repeat variations in humans using mutational constraint.
Gymrek M, Willems T, Reich D, Erlich Y.
Nature Genetics. 2017 September 11.
Detection of long repeat expansions from PCR-free whole-genome sequence data
, , , , , , , , , , RD, , , , , , , , R, , , J , , , , , , , , , , , , , , NS , , , , , , , ,
Genome Research. 2017 September 8.
An NF-kB Transcription-Factor-Dependent Lineage Specific Transcriptional Program Promotes Regulatory T Cell Identity and Function
Oh H, Grinberg-Bleyer Y, Liao W, Maloney D, Wang P, Wu Z, Wang J, Bhatt DM, Heise N, Schmid RM, Hayden MS, Klein U Rabadan R, Ghosh S.
Immunity. 2017 September 7.
Identifying genetic variants that affect viability in large cohorts.
PLOS Biology. 2017 September 5.
GUIDES: sgRNA design for loss-of-function screens.
Nature Methods. 2017 August 31.
ATRX is a regulator of therapy induced senescence in human cells.
Kovatcheva M, Liao W, Klein ME, Robine N, Geiger H, Crago AM, Dickson MA, Tap WD, Singer S, Koff A.
Nature Communications. 2017 August 30.
Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.
Kim-Hellmuth S, Bechheim M, Puetz B, Mohammadi P, Nedelec Y, Giangreco N, Becker J, Kaiser V, Fricker N, Beier E, Boor P, Castel S, Noethen MM, Barreiro LB, Pickrell JK, Mueller-Myhsok, Lappalainen T, Schumacher J, Hornung, V.
Nature Communications. 2017 August 16.
- Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change.