Jackson Laboratory

The Robinson Lab

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

The Jackson Laboratory
Farmington, CT

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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

Hannah Blau, Ph.D.

Research Software Engineer

Applies machine learning and text mining techniques to the analysis and curation of the Human Phenotype Ontology and other ontologies under development in the Robinson Lab.
Leigh Carmody, Ph.D.

Scientific Curator

R. Leigh Carmody, PhD, obtained a Bachelor of Arts in Biology with a minor in Chemistry in May 1997 from Bryn Mawr College. After a short hiatus, Dr. Carmody started her doctoral work at Vanderbilt University.
Daniel Danis, Ph.D.

Postdoctoral Associate

I work on development of new algorithms designed to analyze big data to enable precision medicine
Peter Hansen, Ph.D.

Bioinformatics Analyst I

Develops software that implements statistical and algorithmic approaches to analyze and integrate immunoprofile and metagenomic data.
Kristen Marsh

Research Administrative Assistant I

Provides administrative support to Drs. Verhaak, Oh and Robinson along with their scientific staff.
Vida Ravanmehr, Ph.D.

Postdoctoral Associate

Machine learning, Translational bioinformatics, Big data, Deep learning
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

Press ReleaseFebruary 25, 2021
A new reference for global genomic diversity
Exactly 20 years after the successful completion of the "Human Genome Project," an international group of researchers, the Human Genome Structural Variation Consortium (HGSVC), has now sequenced 32 human genomes at high resolution.
Research HighlightFebruary 01, 2021
Novel antibody tests reveal complexity of immune response to covid-19
The interaction of SARS-CoV-2, the novel coronavirus causing the global COVID-19 pandemic, and the human immune system has been the focus of intensive research.
Research HighlightJanuary 18, 2021
JAX uses genetic models to fight CUP
A JAX team developed CUP-AI-Dx, a machine learning tool that uses RNA sequence data for analysis. The researchers show that CUP-AI-Dx provides an important clinical tool to help guide therapies for CUP patients.
Press ReleaseJanuary 07, 2021
Using the mouse model to fight against heart attacks
British Heart Foundation funds research aimed at supporting the lymphatic system to prevent heart failure.

Tech CornerFebruary 01, 2021
New coronavirus variants spark concerns
Mutations in SARS-CoV-2 are generating viral variants that change the properties of the virus and affect the response to the COVID-19 pandemic.
Tech CornerFebruary 01, 2021
Machine learning: Shining a light on genomic function
Machine learning platforms can be fed data to "teach" them to learn patterns and predict outcomes, and they are becoming more important all the time in biomedical research. But what are they? How do they work? And can we peer into the black box of their inner functions to make them more powerful for prediction and discovery?
Research HighlightJanuary 12, 2021
Few mutations, important difference in cancer cells
JAX Professor Sheng Li, Ph.D., presents a tool, epihet, that automates the epigenetic analysis of AML cells. epihet allows researchers to assess epigenetic differences between cells, identify genomic locations where the differences occur, and associate cellular epigenetic profiles with cancer-related biological function.
Research HighlightDecember 12, 2020
JAX pioneers way to analyze cancer image data
A team led by JAX Associate Professor Jeffrey Chuang, Ph.D., has developed image processing and convolutional neural network software that can combine images of cancers from different organs to reveal how they are related to one another.

The Robinson Lab

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

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Contact The Robinson Lab

Open Positions

Selected Publications

Highlighted Abstract

A new reference for global genomic diversity

Novel antibody tests reveal complexity of immune response to covid-19

JAX uses genetic models to fight CUP

Using the mouse model to fight against heart attacks

New coronavirus variants spark concerns

Machine learning: Shining a light on genomic function

Few mutations, important difference in cancer cells

JAX pioneers way to analyze cancer image data