Jackson Laboratory

The Robinson Lab

Principal Investigator: Peter Robinson, M.D., MSc.

The Jackson Laboratory
Farmington, CT

Contact me at: 860-837-2095

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Develops algorithms and software for the analysis of exome and genome sequences.

The Robinson Lab is primarily located at The Jackson Laboratory. Together with the Phenomics Team, our group develops the Human Phenotype Ontology and applications of this ontology to differential diagnostics and genomics analyses.

We develop algorithms for exome and genome sequencing, ChIP-seq, immunogenomics, and other genomics applications, Gene Ontology analysis, computational phenotype analysis, as well as various algorithms from the fields of Machine Learning and modeling of biological networks with the tools of linear algebra and graph theory. We also collaborate on the computational analysis of wetlab biological data, especially in the fields of human hereditary disease and the molecular basis of bone development and fracture healing.

Robinson Lab on Github

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Lab Staff

Peter Robinson, M.D., MSc.

Professor of Computational Biology

Develops algorithms and software for the analysis of exome and genome sequences.

Selected Publications

Highlighted Abstract

There are many thousands of hereditary diseases in humans, each of which has a specific combination of phenotypic features, but computational analysis of phenotypic data has been hampered by lack of adequate computational data structures. Therefore, we have developed a Human Phenotype Ontology (HPO) with over 8000 terms representing individual phenotypic anomalies and have annotated all clinical entries in Online Mendelian Inheritance in Man with the terms of the HPO. We show that the HPO is able to capture phenotypic similarities between diseases in a useful and highly significant fashion.

View full list of publications

Research HighlightJanuary 16, 2019
The rapid evolution of cancer cells under attack
JAX Associate Professor Jeff Chuang tracks the effects of chemotherapy on different subpopulations of cancer cells. The results indicate ways to improve therapy administration in human patients.
Press ReleaseJanuary 09, 2019
Linking therapy to an asthma-driving molecule
Armed with a $2.4 million grant from the National Institutes of Health, JAX Assistant Professor Adam Williams, Ph.D., is studying the role of molecules called long, non-coding RNAs in asthma.
Press ReleaseDecember 27, 2018
New Alzheimer's mouse models give insight into how human diversity affects disease onset
A new genetically diverse mouse population is opening possibilities for translational research into Alzheimer’s disease that accurately recreates human disease symptoms and progression.
Blog PostDecember 11, 2018
Nesting dolls and Narnia
Research has yielded fascinating discoveries in genomics, but just when it looks like we might come to a more complete understanding, we instead find more layers of complexity to explore.

Minute to UnderstandingJanuary 11, 2019
Minute to Understanding: What is gene therapy?
Mutations in our genes can cause life-threatening diseases, and sometimes there is no drug or surgery available for your doctor to prescribe. Enter gene therapy.
- Precision medicine
- Genetics and Genomics
Press ReleaseJanuary 08, 2019
JAX researcher lands Department of Defense grant to study endometriosis
Supported by a grant from the U.S. Department of Defense, Elise Courtois is researching the genetic mechanisms underlying endometriosis, a painful disease with no non-surgical treatment.
Research HighlightDecember 11, 2018
The unexpected advantages of outbred mice in research
Recently developed outbred mouse populations, such as the diversity outbred (DO) mice at The Jackson Laboratory (JAX), have created research options that parallel or even exceed human genetic diversity. Research with DO mice offers a full range of genetic diversity, and therefore more generalizability of responses across populations.
Research HighlightNovember 08, 2018
Working with uncertainty in cell populations
JAX researchers have developed a new computational algorithm that is capable of modeling the effects of both stochastic gene expression and cell-to-cell variability in a cell population.