Our Perspective article on Computational Strategies for Cross-Species Knowledge Transfer is now published in @natmethods.nature.com! This was a collab b/w @krishnanlab.bsky.social & @fishevodevogeno.bsky.social, led by the amazing Hao Yuan @yhbioinfo.bsky.social. 🧵 www.nature.com/articles/s41...

2/10 #ResearchOrganisms like 🐭 & 🐟 are crucial for studying genes, functions, cell types, & disease. But translating findings to 👨‍⚕️ is tricky. We explore data-driven methods to bridge the gap & introduce the concept of "agnologs" — functional equivalents identified independent of evolutionary origin.

3/10 Our article covers methods that tackle 4 key questions in cross-species research: 1. Predicting function/disease-gene relationships across species 2. Identifying agnologous molecular components 3. Inferring perturbed transcriptomes across species 4. Mapping agnologous cell types and states

4/10 Traditional approaches rely heavily on homology. But shared ancestry ≠ shared function & vice-versa. Here, we introduce the concept of Agnology, which embraces this complexity: "agno-" = unknown/not known, reflecting data-driven functional equivalence regardless of evolutionary origin.

5/10 With the explosion of large-scale multi-species genomics data and advanced #AI & #ML methods, it's an exciting time to rethink cross-species translational biomedicine. Our article offers a roadmap to navigate this frontier and maximize the value of research organisms.

7/10 Kudos to resources like @geneontology.bsky.social , @monarchinitiative.bsky.social, @alliancegenome.bsky.social, & @bgee.org for grounding so much data & knowledge in this space in structured formats. These & many others are included in our catalog ☝🏽

8/10 Key future directions we highlight: - Capturing specific facets of complex diseases - Building networks for more species & contexts - Automated ontology/knowledge graph construction - Better benchmarking for cross-species single-cell methods - Leveraging non-traditional research organisms

9/10 By embracing data-driven, evolution-agnostic approaches, we believe that the field can accelerate discoveries in both common and rare diseases, improving model organism selection and ultimately paving the way for more reliable therapeutic interventions.