January 25, 2024
About LabKey
Cancer research is at the forefront of medical science, pushing the boundaries of what is possible in diagnosis and treatment. In this endeavor, bioinformatics software is an indispensable tool used by researchers to analyze and interpret biological data in diverse ways. These powerful applications have revolutionized how scientists and researchers approach cancer research, offering new insights and avenues for treatment.
Below are a few ways that bioinformatics software is used for cancer research:
Contents: Biomarker ID | Omics Data Integration | Large-Scale Collaboration | Analysis and Pattern Recognition | Drug Discovery
Identification of Biomarkers
The identification of biomarkers is a significant area in cancer research where bioinformatics plays a pivotal role. Using powerful computing and data management, bioinformatics software can help identify and validate biomarkers – biological molecules indicating the presence or state of a disease.
Software is used to analyze large datasets (e.g., gene expression profiles) to identify potential biomarkers that are consistently associated with the presence or progression of the researched cancer. These biomarkers are used for early cancer detection, prognosis, and monitoring the response to treatment in patients.
Multi-Omic Data Integration
Cancer research generates vast amounts of data from various sources like genomic sequencing, proteomics, and clinical trials. Bioinformatics software integrates and analyzes this heterogeneous data, and provides researchers with an understanding of the specific cancer’s biology.
Bioinformatics software integrates diverse data types, to provide a holistic understanding of cancer. These data types may include:
- Genomic data
- Proteomic data (the study of proteins)
- Transcriptomic data (the study of RNA molecules)
- Clinical data from patients
By analyzing these varied data types together, researchers can construct a more comprehensive picture of the cancer’s biology. For example, by integrating genetic analysis and assay results, bioinformatics software can help researchers reveal how certain genetic mutations might alter cellular signaling pathways, leading to uncontrolled cell growth typical of cancer.
Large-Scale Collaborative Research
Cancer research increasingly relies on large-scale, multi-institutional collaborations to gather and analyze vast amounts of data. Bioinformatics software provides the infrastructure for managing, sharing, and analyzing this data. Because of this, researchers from different parts of the world can access and work on shared datasets, facilitating a collaborative approach to understanding cancer.
For instance, projects such as the National Cancer Institute’s (NCI) Surveillance, Epidemiology, and End Results (SEER) program involve extensive data collection and collaborative analysis across multiple research centers. Covering about 30% of the US population, SEER requires standardized data collection, quality control, and detailed tracking of every cancer case in its regions. Projects like SEER require a scalable and efficient platform, where all combined data is secure, reliable, and compatible for analysis.
Features that support cancer research consortiums include:
- Ontology enforcement
- Secure file sharing
- Data extraction
- Cloud hosting
- Connected ELNs
- Data validation
Choosing the right bioinformatics platform, with comparisons like SDMS vs ELNs vs LIMS, enables the sharing and analysis of large datasets among researchers worldwide. This collaborative approach accelerates the pace of discovery in cancer research.
Comparative Analysis and Pattern Recognition for Cancer Research
Bioinformatics software is instrumental in comparative analysis, where genetic, proteomic, and other types of data from cancerous and normal tissues are compared. The comparisons are used to identify patterns and anomalies that are specific to the cancer being researched. These pattern recognition techniques also help classify different types of cancer, predicting the course of the disease and response to treatment.
Gene expression profiles are an example of this comparative analysis in use. By comparing the gene expression profiles of cancerous and non-cancerous cells, researchers can identify genes that are consistently upregulated or downregulated in the investigated cancer. This regulation indicates their potential role in the disease.
Drug Discovery and Development
Bioinformatics software, whether SDMS, LIMS, or another tool, is also important in drug discovery and development. Software tools aid in analyzing the interactions between drugs and their targets at a molecular level to help identify promising drug candidates. Uses include target identification, drug screenings, and analysis of mechanisms of drug action or resistance.
For example, bioinformatics is used in pharmacogenomics, the study of how genes affect a person’s response to drugs. Through the analysis of gene expression profiles in response to various drugs, bioinformatics can help identify which pathways are affected by a particular drug. This analysis aids in the understanding of its mechanisms and the development of personalized drugs.
Bioinformatics with LabKey Server SDMS
Server SDMS has been developed alongside scientists to meet bioinformatics needs of cancer researchers. This scientific data management system has the flexibility to meet the data management and analysis requirements of any bioinformatics use case. At a high level, Server SDSM allows researchers to:
- Centralize and align any type of scientific data
- Uncover trends and insights with visualization & reporting tools
- Securely collaborate across teams and organizations
- Protect sensitive research data and achieve regulatory compliance
To learn more about how Server SDMS can help your cancer research project, please book a demo with our experienced bioinformatics specialist.
