Home › Bioinformatics Platform

Bioinformatics Platform

Vision Statement: Better Discovery, Sooner.

Mission Statement: To develop and apply innovative analytical methods to transform raw data into knowledge and to accelerate biomarker discovery for better prognostics and treatments in autoimmune diseases.

Successful execution of projects utilizing high throughput technologies, such as NextGen sequencing or microarray, relies on a capable IT infrastructure and robust data analysis pipelines to process large volume of data emanating from heterogeneous sources. The Bioinformatics Core at BRI, consisting of PhD computational biologists and biostatisticians, is actively engaged in developing and applying sophisticated analytical methods for biomarker identification and validation, and elucidation of mechanisms in autoimmune diseases. 

The Bioinformatics Core has access to a high-performance computing infrastructure and specialized software to support processing, analyzing and visualizing data from heterogeneous sources, including BRI and VMMC laboratories.  Core services include the following (This is an incomplete list. Please contact us if you have specific needs in data analysis):

Statistical Analysis

  • Sample size and power estimation (e.g. grant applications, project planning)
  • Basic statistical analysis (e.g., journal submission & rebuttal)
  • Advanced statistical analysis (e.g., generalized mixed modeling)
  • Consultation on statistical methods (e.g., explain methods used in certain papers of interest)

Bioinformatics Analysis

  • Microarray (from Genomics Core, public domain or external CROs)
  • RNASeq (from Genomics Core, public domain or external CROs), including single cell sequencing data
  • Fluidigm (from Genomics Core, public domain or external CROs) ), including single cell qPCR data
  • Other platforms (genome sequencing data, flow cytometry data, clinical data, Dr. Ni's data matrixes)

Gene/Target/Disease Analysis

  • Comprehensive gene report (e.g. tell me everything about TP53)
  • Report of disease related genes (e.g., show me a list of genes that are associated with Type 1 diabetes from literature)
  • Report of drug targets for a disease (e.g., show me all drug targets associated with multiple sclerosis)
  • Pathway or functional analysis (e.g., what pathways are associated with my gene lists)
  • Customized reports

Bioinformatics Software: Training and Consultation

  • Systems Immunology Tool kits (GXB, MAT, etc.)
  • Public domain bioinformatics tools (R/Bioconductor, Perl, Python, DAVID bioinformatics toolbox, Galaxy NGS web tools, NCBI tool set, etc.)
  • Commercial tools (SAS/JMP Genomics, GeneSpring, GeneGo MetaCore, etc.)

Representative examples of analysis results are shown in Figures 1-5.

Figure 1: High performance computing servers and storages used by Bioinformatics core at BRI.

Figure 1: High performance computing servers and storages used by Bioinformatics core at BRI.

 

Figure 2: Visualization of CD4 T cell receptor beta chain clonotypes enriched in Type 1 Diabetes, including V-J gene utilization, case-control association statistics and clonotype expansion in memory compartments (>8 million clonotypes analyzed). Collaboration between BRI (Karen Cerosaletti and Chester Ni) and Adaptive Biotechnologies, Seattle.

Figure 2: Visualization of CD4 T cell receptor beta chain clonotypes enriched in Type 1 Diabetes, including V-J gene utilization, case-control association statistics and clonotype expansion in memory compartments (>8 million clonotypes analyzed). Collaboration between BRI (Karen Cerosaletti and Chester Ni) and Adaptive Biotechnologies, Seattle.

 

Figure 3: Transcriptome profiling of immunology-related mouse tissues in diverse disease-treatment conditions (406 samples * 45,281 transcripts. Samples are colored by tissue types.). Participating BRI laboratories include Ziegler lab, Bettelli lab and Hamerman lab. Figure is generated by JMP/Genomics from SAS Institute.

Figure 3: Transcriptome profiling of immunology-related mouse tissues in diverse disease-treatment conditions (406 samples * 45,281 transcripts. Samples are colored by tissue types.). Participating BRI laboratories include Ziegler lab, Bettelli lab and Hamerman lab. Figure is generated by JMP/Genomics from SAS Institute.

 

Figure 4: IL-15 signaling pathway from GeneGo MetaCore, a systems biology application used by bioinformatics core scientists to analyze high throughput data.

Figure 4: IL-15 signaling pathway from GeneGo MetaCore, a systems biology application used by bioinformatics core scientists to analyze high throughput data.

 

Figure 5: Gene expressions of IL2R using high throughput sequencing platform, Illumina HiScan, to study clinical samples from CIS (Clinically Isolated Syndrome) patients and healthy controls.

Figure 5: Gene expressions of IL2Rg using high throughput sequencing platform, Illumina HiScan, to study clinical samples from CIS (Clinically Isolated Syndrome) patients and healthy controls.

 

Availability: Services are available to BRI and VMMC investigators as well as outside investigators. Examples of past and current collaborators include BRI and VMMC investigators, Immune Tolerance Network (ITN), TrialNet, Baylor Institute of Immunology Research, Seattle Children’s Hospital, University of Washington, Adaptive Biotechnologies, Dendreon, Sanofi-Pasteur, Bristol-Myers Squibb, Resolve Therapeutics and others. 

Manager: Chester Ni, PhD

Contact: Please contact CNi@benaroyaresearch.org or (206) 287-1062