We study myogenesis and macrophage differentiation using functional genomics assays such RNA-seq, microRNA-seq, ATAC-seq, and ChIP-seq in order to understand how gene expression and chromatin states change in time courses of differentiation.
Gene Regulatory Networks
We are building models of gene regulatory networks using either bulk or single-cell RNA-seq, and ATAC-seq in human, rodents, and other vertebrates in order to discover how the logic of development is encoded in the genome.
We are leveraging the PacBio and Oxford Nanopore platforms to sequence full-length transcripts in order to characterize the true extent of alternative splicing. We are both sequencing transcriptomes as well as developing tools to enable these analyses.
We also use functional genomics in order to study animals such as nematodes, hydra, or the deer mouse to understand their developmental adaptations.
We are part of a NIA Consortium to build better late-onset Alzheimer’s Disease models in mouse. Our part includes both bioinformatics as well as single-cell transcriptomics in these new models.
We are using functional genomics to understand the mechanisms driving the pathology of Facioscapulohumeral muscular dystrophy using single-cell and single-nucleus techniques.
We celebrate the graduation of three of our PhD students this spring! Congratulations to Drs. Dana Wyman, Sorena Rahmanian, and Lorrayne Serra Clague! Best of luck to all of you.
Kate and Mandy’s single-nucleus FSHD2 study, entitled “Single-nucleus RNA-seq identifies divergent populations of FSHD2 myotube nuclei”, was published in PLoS Genetics. Read it here.
Narges, an MCSB PhD student, has officially joined the lab! Welcome! She is currently working as a bioinformatician working on the Model AD project.
Aide’s Hydra opsin gene study, “Molecular evolution and expression of opsin genes in Hydra vulgaris”, was published in BMC Genomics. You can read it here.
Camden’s gene-regulatory network study that uses linked SOMs on scATAC and scRNA-seq, “Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps”, has been published in PLoS Computational Biology. You can read it here.