Donna Slonim's Research Interests

Current work in the Slonim Lab covers a number of areas of bioinformatics, with a focus on translational work related to human development. However, many of our projects have wider applications. Several current and recent areas of interest are detailed below. You can also find a more complete list of publications here.

Gene and Pathway Regulation in Human Development

Trisomy 21 karyotype
We are interested in what can be learned about fetal and neonatal health from transcriptional profiles, and we have performed a number of studies profiling expression in maternal or neonatal peripheral blood, amniotic fluid, and cord blood, both under normal conditions and in the presence of developmental anomalies. Our early work in this area demonstrated that the limited gene functional annotation in this domain made it challenging to interpret such studies. Thus, we have also worked to improve the annotation infrastructure relating to human development, contributing annotations to the Gene Ontology and to DFLAT, a collection of expanded gene sets within the GO framework, tailored to support gene set analyses of developmental transcriptomes. We are currently applying molecular methods to predict complications in NICU patients and to understand the neurodevelopmental and metabolic impacts of maternal opioid use.

Sample publications:

Precision Medicine: Characterizing Individual Samples

In the era of precision medicine, we need techniques for identifying significant and meaningful characteristics of individual patients given high dimensional genomic data such as gene expression or sequence variation. We have pioneered new computational approaches to anomaly detection (the analogous machine learning challenge), and applied these methods to find functional pathways whose anomalous regulatory expression patterns characterize individual anomalous samples. Such methods are particularly useful when individual anomalous samples are known to be rare unique anomalies, or when multiple samples are typically classed together by symptoms but thought to be caused by heterogeneous molecular processes.

Sample publications:

Feature prediction image

Molecular Networks, Pathways, and Disease Genes

Regulatory network graph inferred from single cell data
One of our key interests is understanding more about how genes and proteins interact to carry out the molecular functions needed by living organisms. We are interested in understanding network properties, using these properties to infer functional roles of genes and their relationships to disease, and adding biological context to reported interactions. Studying the connectivity and temporal properties of biological networks may help us design better diagnostics and therapeutics, identify novel drug targets, and understand broad properties of cellular behavior and evolution. We are also interested in using computational approaches to discover new disease genes and the roles that functional processes or pathways play in disease processes.

Sample publications:

Pharmacogenomics and Drug Discovery

Pharmacogenomics - the use of genomic techniques to develop better drugs to treat disease - remains an exciting research area for bioinformatics scientists. I've long been interested in developing predictive models of drug response to help tailor medications to those individuals most likely to benefit or to identify individuals at highest risk of adverse side effects. I'm also interested in using bioinformatics to help identify novel indications for existing compounds. In the future, developing new methods to identify better drug targets has the potential to revolutionize the pharmaceutical industry, leading to better understanding of mechanisms of action, more personalized treatments, and lower costs for both producers and consumers.

Sample publications:

Kaplan Meier plot of different subgroups of RCC patients

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