Bioinformatics involves the analysis of high throughput sequence and molecular data for study of cancer genomes. The Bioinformatics Shared Resource (BISR) provides comprehensive analysis of genomic and proteomic data to University of Arizona Cancer Center members in support of their research. This contribution can result in short- or long-term projects, ranging from one day to many months, depending on the nature and extent of the support required.
The Shared Resource provides all levels of support from experimental design to analysis and publication of these data and development of grant applications. BISR staff works with researchers to select the data analysis approach that would help answer the research question. The resource assists in providing information about cancer data resources and utilization of bioinformatics pipelines and analytical tools for research projects. BISR also provides cohort identification in public clinically annotated molecular data for hypothesis generation. The goal is to provide assistance with data analysis that will lead to testable hypotheses and fundamentally important discoveries in cancer research.
The BISR specializes in the biological interpretation of data that may lead to a new understanding of cancer biology and the discovery of new diagnostic markers, risk genetic markers, and drug targets. The staff is well prepared to perform all of these types of analysis.
The Bioinformatics Shared Resource at The University of Arizona provides support in the following areas:
- Analysis of genome data (e.g. gene expression, non-coding RNAs, CGH, next gen sequence analysis), proteomics, and other types of molecular data sets of cancer cells and tissues
- Analysis of cancer molecular and clinical annotated datasets from public resources. NIH-TCGA (Cancer Genome Atlas) project, Cosmic (Catalogue of somatic mutations in Cancer), CCLE (Cancer Cell Line Encyclopedia), NIH LINCS project (Library of Network-based Cellular Signatures), NCBI GEO datasets and any other public resource.
- Application of broad range of statistical and computational approaches for integrative analysis of molecular data with clinical parameters and correlating molecular profiles to patient attributes and outcome.
- Support for pathway analysis, data visualization, systems biology analysis, analysis of genetic vulnerabilities for drug targeting and predictive patterns for outcome.
- Assist in generation of preliminary data for development of grant applications and supporting bioinformatics data for publications.
- Bioinformatics support for Cancer Center projects and other Shared Resources in the form of molecular databases, genome databases, and data sharing tools.
The Bioinformatics group includes the following staff members:
- Diogo De Oliveira Pessoa, MS: firstname.lastname@example.org
- Sharvari Narendra, MS: email@example.com
- Megha Padi, PhD, Director of Bioinformatics Shared Resource at The University of Arizona Cancer Center.
Research faculty associated with the Bioinformatics Shared Resource:
Please remember to acknowledge the Cancer Center Support Grant (P30 CA023074) when publishing manuscripts or abstracts that utilized the services of the University of Arizona Cancer Center’s Shared Resources and/or were derived from CCSG pilot funds. Suggested language: "Research reported in this [publication/press release] was supported by the National Cancer Institute of the National Institutes of Health under award number P30 CA023074.
Areas of expertise include both computational biology - biological sequence analysis, protein structure analysis, genome analysis, advanced computational analysis of large data sets such as gene expression, single nucleotide polymorphism (SNPs) and proteomics data and basic biological studies - population and molecular genetics, molecular and cell biology, biochemistry, and evolutionary biology. The goal is to provide assistance with data analysis that will lead to testable hypotheses and fundamentally important discoveries in cancer research. Staff specializes in the biological interpretation of data, leading to a new understanding of cancer biology, and the discovery of new diagnostic markers, risk genetic markers (haplotypes), patterns in data, and drug targets. The staff is well prepared to perform all of these types of analysis.
University of Arizona Cancer Center, Room 4943