Behavior
Pavlovian fear conditioning, stress-enhanced fear learning (SEFL), and cue-induced reinstatement. DeepLabCut for behavioral scoring.
Bench
RT-qPCR, RNA-seq, ATAC-seq, and CUT&RUN.
Transcriptomics
Primarily DESeq2, into analysis pipelines using gene-by-environment interaction models, WGCNA, fgsea. Cell composition assessed with BayesPrism, and DoRothEA and CUT&RUN for transcription factors.
Target prioritization
The goal of our work is to identify high-priority genes that are causally involved in behavioral change for later manipulation. I built a pipeline for narrowing down candidate genes. In this pipeline, genes are assessed for convergence across several kinds of evidence at once: differential expression, hub status in the WGCNA network, a hit from human genome-wide association studies, and expression quantitative trait loci (as we use genetically heterogeneous animals in our experiments). The point is to get from tens of thousands of transcripts to a list small enough to actually test.
Code
R and Python. I also wrote an open-source Snakemake workflow that starts from a public Sequence Read Archive accession and runs through to DESeq2 results; the whole thing is dry-run in continuous integration.