Computational strategies for cross-species knowledge transfer

TL;DR

This review discusses computational methods for transferring biological knowledge across species using transcriptome data and molecular networks, addressing challenges and future directions in cross-species functional annotation and equivalence.

Contribution

It provides a comprehensive overview of current computational strategies for cross-species knowledge transfer, highlighting new concepts like 'agnology' for functional equivalence.

Findings

Summarizes methods for transferring gene and disease annotations.

Highlights approaches for identifying functionally equivalent molecular components.

Discusses future challenges and the concept of 'agnology' in cross-species analysis.

Abstract

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. Our review addresses four key areas: (1) transferring disease and gene annotation knowledge across species, (2) identifying functionally equivalent molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying equivalent cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer, including introducing the concept of "agnology" to describe functional equivalence of biological entities, regardless of their evolutionary origins. This concept is becoming pervasive in integrative data-driven models where evolutionary origins of functions can remain unresolved.