Andreas Pfenning
Associate Professor, Ray and Stephanie Lane Computational Biology Department Carnegie Mellon University
- Pittsburgh PA
Andreas Pfenning has published a number of high-impact papers on genomic tools to study sequence differences.
Biography
Areas of Expertise
Media Appearances
Similar genetic elements underlie vocal learning in mammals
Phys.org online
2024-02-29
"New artificial intelligence methods were needed to help find evolutionary signals in regulatory elements across hundreds of genomes," said Pfenning, a corresponding author on the new study and an associate professor in CMU's Ray and Stephanie Lane Computational Biology Department affiliated with the Neuroscience Institute and Department of Biological Sciences. "We're entering an exciting era where AI is improving our ability to trace human evolutionary history. Studying the gene regulatory elements requires building a map of which ones are active in the relevant brain region of species with vocal learning behavior."
What bats can teach us about the evolution of human speech
UC Berkeley News online
2024-02-29
“We found that the types of cells that form long range connections in the human and bat brain are the same ones that we discovered as most relevant to vocal learning based on the genetic analysis,” Pfenning said. “The anatomy and genetics are both pointing to the same mechanism underlying the evolution of vocal learning across mammals and speech production in humans.”
New Technique Isolates Brain Cells Associated With Parkinson's Disease
CMU News online
2020-11-30
"Even a small portion of the brain can have dozens of different cell types," said Andreas Pfenning, an assistant professor in CMU's Computational Biology Department. "Each of these cell types has different roles in the behavior of an animal and also in disease." Separating cells of a certain type from their neighbors is thus a critical first step for researchers who want to study them.
Expanding set of viral tools targets almost any brain cell type
The Transmitter online
2024-11-19
What’s more, transgenic mice aren’t always the most appropriate model organism. Rodents don’t naturally develop the plaques and tangles associated with Alzheimer’s disease, for example, but scientists use them anyway because the genetic tools to investigate the condition in them are established, says Andreas Pfenning, associate professor of computational biology at Carnegie Mellon University, who was not involved in the work. “What would be really cool is to move beyond mice and into these more appropriate models for certain things,” Pfenning says.
Media
Social
Industry Expertise
Education
Duke University
Ph.D.
Computational Biology
2012
Carnegie Mellon University
B.S.
Computer Science
2006
Languages
- English
- German
Articles
Spatial, transcriptomic, and epigenomic analyses link dorsal horn neurons to chronic pain genetic predisposition
Cell Reports2024
Key mechanisms underlying chronic pain occur within the dorsal horn. Genome-wide association studies (GWASs) have identified genetic variants predisposed to chronic pain. However, most of these variants lie within regulatory non-coding regions that have not been linked to spinal cord biology. Here, we take a multi-species approach to determine whether chronic pain variants impact the regulatory genomics of dorsal horn neurons. First, we generate a large rhesus macaque single-nucleus RNA sequencing (snRNA-seq) atlas and integrate it with available human and mouse datasets to produce a single unified, species-conserved atlas of neuron subtypes.
RERconverge expansion: using relative evolutionary rates to study complex categorical trait evolution
Molecular Biology and Evolution2024
Comparative genomics approaches seek to associate molecular evolution with the evolution of phenotypes across a phylogeny. Many of these methods lack the ability to analyze non-ordinal categorical traits with more than two categories. To address this limitation, we introduce an expansion to RERconverge that associates shifts in evolutionary rates with the convergent evolution of categorical traits. The categorical RERconverge expansion includes methods for performing categorical ancestral state reconstruction, statistical tests for associating relative evolutionary rates with categorical variables, and a new method for performing phylogeny-aware permutations, “permulations”, on categorical traits.
AI-designed DNA sequences regulate cell-type-specific gene expression
Nature2024
Different parts of a cell’s genome can be active or inactive depending on the cell’s function in the body, and whether it is in a disease state. The instructions for activating or repressing a gene are encoded in the genome, and each type of cell has its own genomic ‘language’ that is based on highly complex patterns of nucleotides that describe whether a gene will be expressed. Writing in Nature, Gosai et al.1 apply artificial intelligence (AI) methods to learn the ‘regulatory grammar’ of that language — that is, the patterns of nucleotides in the genome that relate to gene-regulatory activity — for different cell types. The authors then apply those models, along with experimental genomics techniques, to create synthetic DNA sequences that can drive gene expression in specific cell types, which has implications for targeted cell and gene therapy.
Regression convolutional neural network models implicate peripheral immune regulatory variants in the predisposition to Alzheimer’s disease
PLOS Computational Biology2024
Alzheimer’s disease (AD) involves aggregation of amyloid β and tau, neuron loss, cognitive decline, and neuroinflammatory responses. Both resident microglia and peripheral immune cells have been associated with the immune component of AD. However, the relative contribution of resident and peripheral immune cell types to AD predisposition has not been thoroughly explored due to their similarity in gene expression and function. To study the effects of AD-associated variants on cis-regulatory elements, we train convolutional neural network (CNN) regression models that link genome sequence to cell type-specific levels of open chromatin, a proxy for regulatory element activity. We then use in silico mutagenesis of regulatory sequences to predict the relative impact of candidate variants across these cell types.
Erratum: Loss of epigenetic information as a cause of mammalian aging (Cell (2023) 186 (2)(305–326. e27),(S0092867422015707),(10.1016/j. cell. 2022.12. 027))
Cell2024
(Cell 186, 305–326. e1–e14; January 19, 2023) Our paper used a system called “ICE”(inducible changes to the epigenome) to study whether a loss of epigenetic information leads to aging and whether the expression of a subset of Yamanka factors can reverse these age-associated changes, as tests of the Information Theory of Aging. We provide additional information about our experimental design and reference previous, relevant papers from our group and others that had not been cited in the original submission. We apologize for any confusion that may have arisen due to this information not being available in the original published paper. In our Correction, we add details about the transgenic construct design, tamoxifen administration, and temporal and spatial control of I-PpoI in the Results, Discussion, and STAR Methods sections.
