Dr. Michael Love (Associate Professor, Genetics and Biostatistics) had a successful competitive renewal of his R01 grant titled 鈥淎 modular framework for accurate, interpretable, and reproducible analysis of long read RNA-seq data鈥� from NHGRI.
In the proposal, the Love laboratory will 1) expand quantification tools for long-read RNA-seq including novel models of errors and technical bias 2) simplify multi-sample analysis with augmented execution contexts and extend our automatic provenance detection framework to support new use cases in long read, and 3) broaden user engagement by publishing workflows and tutorials, and hosting in-person and virtual workshops on AnVIL.