SUMMARY
The research interests of Jaime I. Davila, Ph.D., are in the creation and application of data analysis and bioinformatics methodologies that facilitate the use of next-generation sequencing (NGS) to provide insights into cancer biology. Using NGS transcriptomics, also known as RNA sequencing (RNA-seq), Dr. Davila develops algorithms to quantify features that are relevant to cancer, such as fusion events or mutational signatures. Furthermore, Dr. Davila develops models that help to understand the limitations of RNA-seq and how it is affected by sample characteristics such as age or degradation level. Finally, he integrates RNA-seq with clinical information to improve the understanding of cancer biology, which could be used as a test in a clinical setting.
Focus areas
- Development of bioinformatics tools for the accurate detection and interpretation of fusion events from RNA-seq
- Development of bioinformatics methodologies to detect and prioritize single nucleotide variants and their application in determining tumor mutational burden and mutational signatures from RNA-seq
- Modeling the limitations of RNA-seq on formalin-fixed, paraffin-embedded (FFPE) tissues and the impact that different sample level features have on its ability to detect transcriptomic anomalies
- Development and use of bioinformatics approaches to leverage RNA-seq as a clinical test used in the clinical management of patients with cancer
Significance to patient care
The detection of transcriptomic anomalies such as fusion events using RNA-seq can help provide diagnosis and determine therapy options for patients with cancer. In particular, most samples that are available in clinical practice come from FFPE tissues, which are more degraded than fresh-frozen materials that are commonly used for RNA-seq. This creates opportunities for the development of novel bioinformatics methodologies for the analysis of RNA-seq from FFPE samples, and the assessment of those methodologies' potential and limitations in the context of clinical care.