Using sequence similarity networks for visualization of relationships across diverse protein superfamilies.

ABSTRACT: The dramatic increase in heterogeneous types of biological data--in particular, the abundance of new protein sequences--requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualiz

SUBMITTER: Atkinson HJ 

PROVIDER: S-EPMC2631154 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature