Abstract: Computational recognition of distant sequence homology is a key to studying ancient events in molecular evolution. The better our sequence analysis methods are, the deeper in evolutionary time we can see. A major aim in the field is to improve the resolution of homology recognition methods by building increasingly realistic, complex, parameter-rich models. I will describe current and future research in homology search algorithms based on probabilistic inference methods, including hidden Markov models (HMMs) and stochastic context-free grammars (SCFGs). We make these methods available in the HMMER and Infernal software from my laboratory, in collaboration with sequence family databases including Pfam and Rfam.  Biography: Dr. Diaz is an Associate Professor in the Department of Neurosurgery at the University of California San Francisco. He received his PhD in Applied Mathematics from Cornell University, and later went on to do post-doctoral training in molecular biology at UCSF. At the time, induced pluripotency was somewhat of a mechanistic black box. Dr. Diaz’s research identified genetic regulators of pluripotency using screening and systems approaches (Qin and Diaz, Cell 2014). Since its inception in 2015, the Diaz lab has contributed studies on brain-tumor heterogeneity, immuno-oncology, and bioinformatics. In recent work (Nature Cancer 2022), they elucidate the cellular response of glioblastoma to standard therapy, through a longitudinal study of clinical specimens using single-cell and spatial approaches. Here they found that recurrent glioblastoma is characterized by a mesenchymal transition driven by activator protein 1 signaling. This study built on their prior work (Cancer Disc. 2019), where they resolved the lineage relationships between distinct glioma stem-cell populations via single-cell lineage tracing. These papers cap a multiyear effort to understand glioblastoma evolution under therapy (e.g., Genome Biol. 2021, Neuro-Onc. 2020), as well as the contributions of cellular ontogeny and genetics to tumor immunogenicity (Genome Biol. 2017, 2021, 2022). Additionally, the Diaz lab has developed novel computational approaches for inference and classification from genomics data (Mol Sys Biol 2016, Bioinformatics 2016, 2018, 2020). Dr. Diaz is the PI of awards from the NIH, DOD, and other charitable organizations. His work has been recognized twice with the Adult Basic Research Award from the Society for Neuro-Oncology. He serves on the editorial boards of Neuro-Oncology and Genome Medicine.