Cancer Informatics Shared Resource
The mission of the Cancer Informatics Shared Resource (CISR) is to promote high quality, innovative cancer research by providing members of UW Carbone Cancer Center (UWCCC) with modern and efficient informatics, including advancements in bioinformatics, clinical informatics, image analysis and computational science.
CISR offers standardized computational pipeline analyses of many types of data to UWCCC members. Automated analyses supported include those for DNA variant calling, RNA-seq, ChIP-seq, ATAC-seq, metagenomics, and protein structure prediction. This service is offered through a collaboration with the Bioinformatics Resource Center (BRC), which has developed these pipelines over many years for use by UW–Madison researchers. CISR and BRC are working together to build additional pipelines of interest to UWCCC members. To use or inquire about this service, please submit a request form and answer “Yes” to the question regarding registering this project as part of CISR.
Leadership
Faculty Leader
Facility Manager
Using the Facility
CISR provides on-demand bioinformatics and advanced scientific analytics including machine learning as well as long-term research algorithm development for analysis and integration of biological omics, and clinical data.
Services include assistance with grant application and publication development.
Bioinformatics expertise covers a broad spectrum including: next generation sequencing data analysis; genomics, proteomics, metabolomics, and flow cytometry; medical imaging analysis, integration of various types of data.
Machine learning expertise include: techniques for predicting outcome or treatment response from imaging, clinical, and molecular data.
Requests for cancer informatics services and consultation are initiated by email or phone to the faculty leader or facility manager and the most appropriate informatics personnel is put in contact with the UW Carbone Cancer Center (UWCCC) member.
- The shared resource is available to all UW Carbone Cancer Center (UWCCC) members.
- A basic level of consulting/collaborative support is provided free of charge to all peer-reviewed, cancer-related projects. Early stage interaction is especially encouraged.
- UWCCC members who require more extensive biostatistical support are expected to provide funding through individual grants, usually by including effort, salary and computing support for the biostatistician.
- All consulting/collaborative support for non-peer-reviewed projects must be fully funded from appropriate funding source.
CISR personnel are available Monday through Friday 9am – 5pm by email.
Consulting Services
CISR promotes high quality, innovative cancer research by providing members of the UWCCC with an informatics intellectual resource, including advancements in Bioinformatics, Clinical Informatics, Image Analysis and Computational Biology. CISR faculty bring expertise in genetics/genomics, bioinformatics, imaging, visualization techniques and clinical informatics. Consulting services include:
- Genetics/Genomics
- Bioinformatics
- Image analysis
- Clinical data
- Laboratory and computational biology software tools
- EMR data mining (including NLP)
- Data visualization
Computational Pipelines
CISR supports RNA-seq, ChIP-seq, ATAC-seq, metagenomics, and protein structure prediction.
To use or inquire about this service, please submit a request form and answer “Yes” to the question regarding registering this project as part of CISR.
Any published research which received funding from the UWCCC Cancer Center Support Grant (CCSG), including the use of UWCCC Shared Resources to generate or analyze data or conduct clinical trials, must reference the UWCCC. See Acknowledging UWCCC in Publications, Posters and Presentations. Please acknowledge “UWCCC Cancer Informatics Shared Resource” for any work supported by CISR. Thank you!
Sample Acknowledgment
“The author(s) thank the UWCCC Cancer Informatics Shared Resource, supported by P30 CA014520, for use of its services.”
Scientific Accomplishments
Colin Dewey – GEM
Jamie Thomson – DT
- Key to RNA-seq is accurate mapping of reads to reference genome.
- Earlier read-mapping algorithms threw out reads that mapped to multiple sites.
- RSEM assigns probability distribution of sites for each read and refines by expectation-maximization
- Goal is to have lowest-curve few genes with high error in expression measurement
- RSEM outperforms competitors.
Elizabeth Burnside – CPC
C. David Page – GEM
- Goal is to accurately identify malignant abnormalities on mammogram.
- Data entered in National Mammography Database standard format.
- Ground truth malignant/benign from breast cancer registry, based on biopsy.
- Bayes net learning algorithm yields model with significantly better ROC area on held-out data than radiologists.
- New learning algorithm SAYU performs even better.
- Precision-recall curves are another way to plot ROC data for rare events and show room for further improvement.
CISR supported Emery Bresnick (GEM) and colleagues in illustrating exosome complex’s role during erythropoiesis. This work discovered that exosome complex is a vital determinant of a developmental signaling transition that dictates red blood cell proliferation versus differentiation. CISR staff scientist Peng Liu processedExosc3-/-ES cells RNA-seq data, performed differential gene expression analysis, and quantified the change of Kit expression levels, revealing Kit expression is 5.9-fold lower in Exosc3-/- ES cells than in normal ones.
- CISR staff scientist, Irene Ong, analyzed variants/ mutational profile of patient and xenograft samples, showing stability of grafted tumors; expanding the utility of studying tumors from head and neck cancer patients.
- Preliminary data for SPORE grant to show lab’s capability in analysis of next generation sequencing data. Three papers have been published based on these analyses.
- CISR supported Emery Bresnick (GEM) and colleagues in illustrating the signaling circuit triggered by Ras-MAPK-GATA-2.
- CISR staff scientist, Irene Ong, analyzed TCGA AML data to illustrate evidence of this novel mechanism, GATA-2 regulation of IL-1β and CXCL2, in AML tumors.
- CISR also generated Kaplan-Meier curves showing GATA-2-chemokine/cytokine circuit predicts poor prognosis of an AML subtype.
- CISR supported Yongna Xing (DT) and colleagues narrow the search space of proteins bound by B’α subunit of PP2A, which helps determine substrate recognition, regulation, and role in cytokinesis of PP2A-B’ holoenzyme, and also reduces the number of needed follow-up experiments.
- CISR staff scientist, Sean McIlwain, found consensus PP2A binding motifs within the human proteome and annotated them with secondary structures and domains, average disorder, predicted and known phosphorylation sites, and locations of the protein’s cellular super-complexes.
- CISR also performed Gene ontology analysis of the identified proteins to find enriched KEGG pathways.