LASSEINGE LAB

Because
Science.

An integrative genomics lab, applying machine learning and data-driven approaches to elucidate disease mechanisms.

About

We develop and apply genomic strategies to map molecular processes contributing to the etiology, progression, and treatment of diseases originating in the brain and/or the kidney. We investigate the impact of cell- and tissue-specific gene and transcript regulation and expression, cell-cell communication, and sex-associated molecular changes on disease manifestation.

Through machine learning and data-driven approaches, we identify optimal precision preclinical models to streamline drug targets and repositioning candidates for treating diseases.

Publications

Funding

Kidney Research • Brain Research • Drug repurposing • Methods & Tools •

Team

Our Team

Our vibrant team is dedicated to making meaningful contributions to the science community through innovative research and collaboration.

Dr. Lasseigne

BRITTANY LASSEIGNE, PHD

PRINCIPAL INVESTIGATOR | ASSOCIATE PROFESSOR

Dr. Lasseigne is an Associate Professor in the Department of Cell, Developmental and Integrative Biology with a secondary appointment in the Department of Biomedical Informatics and Data Sceince, and an Associate Scientist in the Precision Medicine Institute (PMI), the Nephrology Research & Training Center (NRTC), Comprehensive Neuroscience Center (CNC), the Center for Neurodegeneration and Experimental Therapeutics (CNET), and the Experimental Therapeutics section of the O’Neal Comprehensive Cancer Center at The University of Alabama at Birmingham (UAB) Heersink School of Medicine.

She previously completed a BS in Biological Engineering at Mississippi State University, an interdisciplinary biology, chemistry, and chemical engineering PhD in Biotechnology Science and Engineering from The University of Alabama in Huntsville (identifying biomarkers and genomic signatures of renal cell carcinoma diagnosis and progression, respectively), and a postdoctoral fellowship in genetics and genomics at the HudsonAlpha Institute for Biotechnology (determining predisposing variants and genes contributing to neurodegenerative disorders).

Brittany is passionate about developing and applying genomics technologies to human diseases and teaching genomics and data science to students and scientists of all backgrounds.

To that end, she also served as the PI, Director, and a student research mentor for the current NHGRI-funded R25 Summer Undergraduate and Postbacc Experiences in Research in Genomic Medicine (SUPER-GM) program, which provided research experiences for students from diverse backgrounds, especially Alabama students attending Historically Black Colleges and Universities (HBCUs).

Team

T.C. Howton, PhD

Senior Computational Biologist

Dr. Howton earned his PhD in Biology from the University of Alabama at Birmingham. During his doctoral studies, T.C. focused on using network-based approaches to elucidate the molecular interactions between plants and bacterial pathogens. T.C. is currently serving as a lab manager as well as research scientist within the Lasseigne lab.

Lizzy Wilk

Senior Computational Biologist

Lizzy earned her B.S. in Biology with a minor in Chemistry from the University of Alabama at Huntsville and M.S. in Biotechnology with a specialization in Bioinformatics from University of Maryland Global Campus. As a staff scientist, she focuses on rare diseases and polycystic kidney disease, leveraging machine learning (ML) and artificial intelligence (AI) to deepen mechanistic understanding of genetic disease variants and prioritize repurposed drug candidates for patients with genetic disorders.

Tabea Soelter, PhD

Computational Biologist

Dr. Soelter has a B.S. in Cell and Molecular Biology from Piedmont University and earned her Ph.D. in Cell Biology from the University of Alabama at Birmingham. During her doctoral studies, Tabea investigated the role of altered glia-neuron communication and its downstream effects on gene expression and gene regulation in Alzheimer’s disease using snRNA-seq and network approaches. As a staff scientist, Tabea is working on identifying a transcriptomic exercise signature from mouse hippocampus snRNA-seq data using machine learning approaches, investigating alternative splicing across contexts (i.e., disease and sex) in long-read RNA-seq data, and leading the creation of a harmonized public-domain polycystic kidney disease transcriptomic data hub.