Peter N. Robinson, MD
We are living in a time of unparalleled opportunity to extend our understanding of human disease and improve the care of patients with precision genomic medicine. Sophisticated bioinformatics and computational biology are essential to achieve the full potential of genomics for science as well as for patients. Peter Robinson, Professor of Computational Biology at the Jackson Laboratory for Genomic Medicine, leads a research group dedicated to the development of algorithms and computational resources for genomics. Peter is a PI of the Monarch Initiative, an NIH supported project dedicated to the integration, alignment, and re-distribution of cross-species gene, genotype, variant, disease, and phenotype data. Highlights of the lab’s work include the Human Phenotype Ontology, the Exomiser suite of tools for exome and genome analysis, and algorithms for ChIP-seq and immunogenomics. Peter studied Mathematics (Bachelor) at Columbia College, Medicine at the University of Pennsylvania, and Computer Science (Master’s) at Columbia University. He completed an internship in Primary Care Internal Medicine at Yale University, and a residency (Facharzt) in Pediatrics at Charité - Universitätsmedizin Berlin in Berlin, Germany. Subsequent to that, he led the Bioinformatics group at the Institute for Medical Genetics and Human Genetics at the Charité from 2004-2016. In 2016, he relocated to the Jackson Laboratory for Genomic Medicine in Connecticut, USA.
To learn more: Jackson Laboratory for Genomic Medicine
We are hiring
The Robinson lab is currently hiring. Positions are available for bioinformaticians. Contact Professor Robinson for more information.
Hannah Blau, Ph.D.
Hannah Blau joined the Robinson Lab as Research Software Engineer in May, 2017. She completed her Ph.D. in Computer Science at the University of Massachusetts Amherst. She earned the B.A. in French from Yale University and the M.S.E. in Computer and Information Science from the University of Pennsylvania. Hannah gained international experience at the Artificial Intelligence Center of the Bull Corporation (Louveciennes, France), and in the Machine Learning Group of the Daimler-Benz Research Centre (Ulm, Germany). She worked as a Research Scientist in the Automated Reasoning Group of the Honeywell Technology Center (Minneapolis, Minnesota). While in grad school she served as data scientist in the lab of Professor M. Darby Dyar, Chair of Astronomy at Mount Holyoke College and member of the science team for the Mars Science Laboratory (Curiosity rover).
Leigh Carmody, Ph.D.
Leigh Carmody, Ph.D., obtained her doctorate of philosophy in Molecular Physiology & Biophysics from Vanderbilt University in Spring 2007 where she studied targeting of signaling molecules to F-actin cytoskeleton/dendritic spines. Dr. Carmody continued her studies as a postdoctoral fellow at Massachusetts Institute of Technology where she investigated the role of Rac1 in dendritic spine motility. Late 2008, she joined the Broad Institute of MIT & Harvard as a Scientist where she aided in early-stage drug discovery efforts to identify chemical leads directed at cancer targets and neglected parasitic infections. Dr. Carmody joined Jackson Laboratory in 2015 as a Project Manager, and is currently a Scientific Curator annotating phenotyping and genomics data for the human phenotype ontology (HPO) database.
Daniel Danis, M.Sc.
Daniel has a M.Sc. in Pharmacy from the Comenius University, Bratislava, Slovakia. He is currently working on his Ph.D., mainly focused on the molecular basis and pathomechanisms of rare hereditary diseases in human patients. He has experience in the assembly of custom UNIX-based bioinformatics pipelines for whole exome sequencing data analysis that have identified disease-causing variants in several Slovak families.
His visit in the Robinson lab involves the development of algorithms for prioritizing exome and genome variants. These new algorithms are designed to integrate into the Exomiser and Genomiser frameworks. In addition, he works on tools for the biocuration of published disease-causing variants.