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Richard H. Scheuermann, PhD, is the director of the La Jolla Campus at the J. Craig Venter Institute (JCVI) and an adjunct professor of pathology at U.C. San Diego. He received a BS in Life Sciences from the Massachusetts Institute of Technology, and a PhD in Molecular Biology from the University of California, Berkeley. After completing his doctoral research, he accepted an independent research position at the Basel Institute for Immunology in Basel, Switzerland. In 1992, he joined the faculty in the Department of Pathology at the University of Texas Southwestern Medical Center in Dallas where he rose to the rank of Professor with tenure. In 2001, he made a career shift into the discipline of bioinformatics, initiated with a sabbatical year at the San Diego Supercomputer Center. In 2012, Dr. Scheuermann moved to San Diego to become the Director of Informatics at JCVI, and in 2016 was promoted to Director of the La Jolla Campus.

Dr. Scheuermann has applied his deep knowledge in molecular immunology and infectious disease toward the development of novel computational data mining methods and knowledge representation approaches, including the development of biomedical ontologies and their use in data mining, novel methods for the analysis of gene expression, protein network and flow cytometry data, and novel comparative genomics methods. These computational methods have been made available through several public database and analysis resources, including the Influenza Research Database (IRD), the Virus Pathogen Resource (ViPR), and the Immunology Database and Analysis Portal (ImmPort) supported by the U.S. National Institutes of Health.

Research Priorities

Informatics database resources and knowledge representation standards

  • Principle investigator on the Influenza Research Database (IRD), the Virus Pathogen Resource (ViPR) and the Immunology Database and Analysis Portal (ImmPort) development projects.
  • Development of biomedical ontologies and other knowledge representation standards through the Open Biomedical Ontology (OBO) Foundry, including the Cell Ontology (CL), the Ontology for General Medical Sciences (OGMS), the Ontology for Biomedical Investigations (OBI), and the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt).

Flow cytometry analysis methods

  • Development of a suite of cytometry data processing and analysis methods, including FLOCK, FlowMap-FR, FCSTrans, and DAFi.
  • Co-founder of the FlowCAP Consortium for the critical evaluation of cytometry analysis methods using benchmark datasets and objective performance metrics.
  • Development of the FlowGate resource for user-friendly access to cytometry analysis methods.
  • Application of computational cytometry analysis methods to study immunological responses to vaccination, respiratory pathogen infection, autoimmunity, and leukemia and lymphoma.

Systems biology analysis methods

  • Identification of functional modules within gene/protein networks based on their topological properties using MoNet and dMoNet.
  • Dissemination of systems biology of infectious diseases data through the IRD and ViPR resources.

Transcriptomics and single cell genomics

  • Development of a suite of gene expression microarray analysis methods, including DFW, DFCM, CLASSIFI, and GO-Bayes.
  • Use of single cell RNA sequencing (scRNAseq) to study the cellular complexity of the human cerebral cortex, vaccine responses, and the development of multiple sclerosis.
  • Application of machine learning methods for scRNAseq analysis, including quality control procedures and marker gene feature selection.

Transcriptomic and Morphophysiological Evidence for a Specialized Human Cortical Gabaergic Cell Type

Automated Analysis of Clinical Flow Cytometry Data: A Chronic Lymphocytic Leukemia (CLL) Pilot

OGMS: Ontology for General Medical Science

A Bioinformatics Roadmap for the Human Vaccines Project

Early Innate Immune Responses to Hepatitis B Vaccination Using Single Cell RNA Sequencing

Influenza Research Database (IRD)

Virus Pathogen Resource (ViPR)

Development of a Semantically-Coherent and Statistically-Comparable Representation of Reference Cell Types for the Human Cell Atlas

CL: Cell Ontology

NSforest: A Machine Learning Method to Identify Marker Genes from Single Cell/Single Nuclei RNA Sequencing Data

Archaeopteryx.js: Web-based Visualization and Exploration of Annotated Phylogenetic Trees

Mosquito-Borne Virus Serodiagnostic Development

Characterizing Multiple Sclerosis Pathophysiology Using Unbiased Single Cell Transcriptional Phenotyping

Analysis and Classification of Human Herpesviridae Proteins Using Domain-Architecture Aware Inference of Orthologs (DAIO)

FlowGate: Transformative Computational Infrastructures for Cell-Based Biomarker Diagnostics

Contemporary Circulating EV-D68 Strains Show Differential Replication in Human Neuronal and Non-neuronal Cell Lines

Using Single Nuclei for RNA-Seq to Capture the Transcriptome of Postmortem Neurons

VDJML: A File Format with Tools for Capturing the Results of Inferring Immune Receptor Rearrangements

VDJPipe: A Pipelined Tool for Pre-Processing Immune Repertoire Sequencing Data

A Phylogeny-Based Global Nomenclature System and Automated Annotation Tool for H1 Hemagglutinin Genes from Swine Influenza A Viruses

Production of a Quality Control Pipeline for Single Nuclei RNA-seq and Its Application in the Analysis of Cell Type Diversity of Post-Mortem Human Brain Neocortex

Comprehensive Annotation of Mature Peptides and Genotypes for Zika Virus

Mapping Cell Populations in Flow Cytometry Data for Cross-Sample Comparison Using the Friedman-Rafsky Test Statistic as a Distance Measure

Diversifying Selection Analysis Predicts Antigenic Evolution of 2009 Pandemic H1N1 Influenza A Virus in Humans

Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure.
Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2016-01-01; 89.1: 71-88.
PMID: 26274018
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2016-01-01; 22.564-575.
PMID: 27897007
Critical assessment of automated flow cytometry data analysis techniques.
Nature methods. 2013-03-01; 10.3: 228-38.
PMID: 23396282
Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data.
Cytometry. Part B, Clinical cytometry. 2010-01-01; 78 Suppl 1.S69-82.
PMID: 20839340