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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Open Positions

Job	Job Title	Location	Description

Lab Staff

Peter Robinson, M.D., MSc.

Professor of Computational Biology

Develops algorithms and software for the analysis of exome and genome sequences.
Xingmin "Aaron" Zhang, Ph.D.

Postdoctoral Associate

Patient deep phenotyping from various data types in electronic health records; phenotype- and genomic-based disease modeling and algorithm development for translational research and differential diagnoses

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

Tech CornerJuly 17, 2019
Improved methods, expanded capabilities: Analyzing cells one at a time
Researchers are able to do many things today that were impossible five, three, even one year ago. Some of the new options are science-fiction-level impressive.
Search MagazineJune 18, 2019
Connected through Science
When Isabelle Chen, a senior at The Ethel Walker School in Simsbury, Conn., met Ann Bresnahan ‘69 during a science alumnae panel discussion at Bresnahan’s 50-year high school reunion, the two knew that they shared a passion for science.
Featured ArticleMay 29, 2019
Making a community impact beyond the bench
A JAX postdoctoral associate was recently awarded a fellowship in Science Policy from the Society for Industrial and Applied Mathematics that will allow him to advocate for support of science research with federal officials and congressional staff.
Research HighlightMay 20, 2019
Revealing cellular traits, one cell at a time
Scientists have developed an expanded toolkit that enables investigations of cellular traits at the single cell level at an unprecedented level of detail.

Press ReleaseJuly 15, 2019
Research team creates new research models for Snyder-Robinson Syndrome
Two new mouse models created to study neurological developments in Snyder-Robinson Syndrome to advance global understanding of this rare disease.
Tech CornerJune 17, 2019
The genome of Eve (the mouse)
C57BL/6—aka Black 6—is the most widely used mouse strain in research. Its reference genome was published in 2003 and has been carefully curated over the years since.
Search MagazineMay 22, 2019
JAX scientists speak out on the crisis of opioid addiction
JAX scientists are intensifying their efforts to shed light on the genetic and neurobiological causes of addiction, along with the impact of environmental influences.
Research HighlightMay 02, 2019
How can we take full advantage of EHR Data?
There has been an explosion of digital medical data in recent years, taking many forms. Much of the most valuable data—clinical patient data—is currently stored in electronic health record (EHR) systems, providing a theoretical gold mine for large-scale integration and analyses of patient traits, diseases, treatments, progression over time, outcomes and more.