Sara E. Kalla, Ph.D.

Assistant Professor

Sara E. Kalla, Ph.D. Contact Information


  • Ph.D., Rice University 2011
  • B.S., University of Pittsburgh 2000

Research Interests

I am committed to drawing upon my experience in evolutionary biology, genetics and bioinformatics to build systems and tools that enable analysis of genetic variation in clinical settings. As a Keck Fellow in the Biomedical Data Training Program during my doctoral training at Rice, I integrated evolutionary biology and computational biology in looking at the effects of purifying selection on GC content in the social amoebae, Dictyostelium discoideum. My post-doctoral work at Cornell further strengthened my interest in bioinformatics, where I developed custom analysis pipelines for next-generation sequence (NGS) data. At the same time, as part of the genetics lab in the Clinical Sciences department, I assisted many collaborators with computational analyses, and saw firsthand the importance of automating tools and making them more accessible to the broader biomedical research community. At the HGSC-CL, I have continued to develop NGS pipelines and secondary tools to analyze patient genomic data and deliver reportable results to clinicians. Going forward, I aim to develop tools that further clinical analysis capabilities.

Clinical analysis currently involves manual review by qualified clinicians and personnel and is limited largely by aggregation of manual data. Assignment of a pathogenic variant classification is frequently reliant on time-consuming review of literature. Development of tools to minimize reviewer involvement in curation of this information while still maintaining accuracy is critical to high throughput clinical genomics. As clinical genetics expands and matures as a field, reanalysis is becoming an increasingly difficult task as both the number of variants and the amount of new information (ClinVar entries, additional literature, etc.) increases. This requires a robust framework to track samples and maintain up-to-date information on variant interpretation.

Development of these frameworks can also enable retrospective and meta analyses of projects. This will further our knowledge of human genetics by identifying variants that may not be causative but nonetheless contribute to disease. Unlike GWAS and other studies which only identify the region in linkage with causative variants, large scale aggregate studies of whole exome and whole genome sequences can identify the variants and regions involved with higher resolution. This in turn, will improve the utility of clinical genetics.