School of Medicine and Health Sciences Poster Presentations
OncoMX: An integrated cancer mutation and expression knowledgebase for biomarker evaluation and discovery
Document Type
Poster
Abstract Category
Cancer/Oncology
Keywords
Cancer, Biomarkers, Genomics, Mutations, Differential Expression
Publication Date
Spring 5-1-2019
Abstract
The massive, multiform datasets generated by cancer genomics studies provide tremendous opportunities for the scientific community to explore and develop hypotheses. However, challenges arise during analysis due to the size and heterogeneity of these datasets, making it difficult to extrapolate meaningful results. A variety of technical characteristics including the pre-processing of data, file formats, attribute names, and reference data currently appear in variable formats across multiple databases. These databases are often designed to facilitate highly specific research. Consequently, attempting to utilize, analyze, or combine datasets from multiple sources proves inefficient and overly complicated. OncoMX is a knowledgebase and web portal currently being developed to quell such challenges by streamlining searches between unified datasets. The OncoMX mission is to create an integrated cancer mutation and expression resource for exploring cancer biomarkers to facilitate early cancer detection. OncoMX is a collaborative project between The George Washington University, NASA's Jet Propulsion Laboratory, the Swiss Institute of Bioinformatics, and the University of Delaware. OncoMX currently integrates sequencing-based mutation data from BioMuta, cancer/normal differential expression data from BioXpress, normal expression data across organisms from Bgee, biomarker data from EDRN, pathway data from Reactome, and literature mining evidence using custom applications of DEXTER and DiMeX. BioMuta and BioXpress serve as the foundational knowledgebases for OncoMX, where data is connected through Disease Ontology and Uberon terms. Normal expression data from Bgee and custom literature mining software augment the cancer data to improve functional interpretation of the reported variants and expression profiles. Additional data is currently being integrated into OncoMX, such as functional annotations, scRNA-seq data, and cancer mutation and expression literature mining results. The OncoMX web portal interface development is use case driven by the following four perspectives: 1) exploration of cancer biomarkers, 2) evaluation of mutation and expression in an evolutionary context, 3) side-by-side exploration of published literature for mutation and expression in cancer, 4) and exploration of a specific gene or biomarker within a pathway context. The unification of multiple types of cancer datasets allows researchers to explore or generate hypotheses within one database, execute targeted searches, and compare data sources. OncoMX, with direct community feedback, is projected to support a broad range of cancer research initiatives for the enrichment of cancer biomarker detection; paving the way for earlier cancer detection. The OncoMX web portal can be accessed at: https://www.oncomx.org/.
Open Access
1
OncoMX: An integrated cancer mutation and expression knowledgebase for biomarker evaluation and discovery
The massive, multiform datasets generated by cancer genomics studies provide tremendous opportunities for the scientific community to explore and develop hypotheses. However, challenges arise during analysis due to the size and heterogeneity of these datasets, making it difficult to extrapolate meaningful results. A variety of technical characteristics including the pre-processing of data, file formats, attribute names, and reference data currently appear in variable formats across multiple databases. These databases are often designed to facilitate highly specific research. Consequently, attempting to utilize, analyze, or combine datasets from multiple sources proves inefficient and overly complicated. OncoMX is a knowledgebase and web portal currently being developed to quell such challenges by streamlining searches between unified datasets. The OncoMX mission is to create an integrated cancer mutation and expression resource for exploring cancer biomarkers to facilitate early cancer detection. OncoMX is a collaborative project between The George Washington University, NASA's Jet Propulsion Laboratory, the Swiss Institute of Bioinformatics, and the University of Delaware. OncoMX currently integrates sequencing-based mutation data from BioMuta, cancer/normal differential expression data from BioXpress, normal expression data across organisms from Bgee, biomarker data from EDRN, pathway data from Reactome, and literature mining evidence using custom applications of DEXTER and DiMeX. BioMuta and BioXpress serve as the foundational knowledgebases for OncoMX, where data is connected through Disease Ontology and Uberon terms. Normal expression data from Bgee and custom literature mining software augment the cancer data to improve functional interpretation of the reported variants and expression profiles. Additional data is currently being integrated into OncoMX, such as functional annotations, scRNA-seq data, and cancer mutation and expression literature mining results. The OncoMX web portal interface development is use case driven by the following four perspectives: 1) exploration of cancer biomarkers, 2) evaluation of mutation and expression in an evolutionary context, 3) side-by-side exploration of published literature for mutation and expression in cancer, 4) and exploration of a specific gene or biomarker within a pathway context. The unification of multiple types of cancer datasets allows researchers to explore or generate hypotheses within one database, execute targeted searches, and compare data sources. OncoMX, with direct community feedback, is projected to support a broad range of cancer research initiatives for the enrichment of cancer biomarker detection; paving the way for earlier cancer detection. The OncoMX web portal can be accessed at: https://www.oncomx.org/.
Comments
Presented at Research Days 2019.