Christy Henzler

1.     What are your current research interests?

I lead a group of bioinformatics analysts at the Minnesota Supercomputing Institute – we’re the liaisons between scientists using various ‘omics approaches and technologies (genomics, transcriptomics, epigenomics, etc.) and the high performance computing resources necessary to analyze the data. In this role, we analyze data from all kinds of research systems and questions, and from many different technologies. I’m currently very interested in developments in spatial transcriptomics and long read sequencing, and how technical advancements and better algorithms for handling both of these types of data will allow us to address biological questions that have been previously intractable.

2.     How do you define Data Science?

There are lots of definitions of data science floating around, and I prefer broader ones, such as, simply, the application of statistical methods to analyze complex data. This inclusive understanding of data science opens it up into an incredibly – and excitingly – broad field. I work in bioinformatics, a specific subfield which is just a corner of the large tent that is data science, but tools developed in other areas of data science can and are applied in my field, and vice versa. In fact, the synergies across data science, with methods developed for one application being co-opted and readapted for another application are one of the things that makes it such a dynamic field.

3.     Can you share an interesting or surprising result you’ve found in your data?

My group is always working on fascinating projects, so picking one biological result is impossible. Throughout my career, I’ve worked on human genetic data analysis for a large variety of projects, from germline genetic diagnostic pipelines to somatic variant calling in cancer to pharmacogenomics. Each one reminds me yet again of both the deep complexities of human genetics, and the challenges of tying genetic mutations to phenotype or outcome, as well as the limitations and challenges of the different types of data and algorithms that can be used to answer these questions. True precision medicine is coming, but we still have so much to explore and understand in the human genome!

4.     Are there any interesting new tools or libraries you or your students have been using?

In general, bioinformatics software developers have been slow to adapt their software for GPUs. However, I’m excited about NVIDIA’s parabricks software suite, which we have installed at MSI, and which provides a set of standard bioinformatics software and pipelines implemented for GPUs. Parabricks implementations of standard software for tasks such as alignment and variant calling bring huge increases in speed, while still allowing for customized analyses.

5.     What are you most excited about in the field of data science in the next 5 years?

In my corner of data science, I'm very excited about advances in spatial transcriptomics, and how a spatial understanding of gene expression in complex tissues will transform biological research – from biomedical applications to plant science . More broadly, I'm interested in how machine learning and artificial intelligence are already transforming how we approach data and are improving analysis.