Pythoscape: a framework for generation of large protein similarity networks

Alan E. Barber,Patricia C. Babbitt

doi:10.1093/bioinformatics/bts532

Alan E. Barber, Patricia C. Babbitt

Open Access

https://doi.org/10.1093/bioinformatics/bts532

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Abstract

Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among proteins for which pairwise all-by-all similarity connections have been calculated. Mapping of biological and other information to network nodes or edges enables hypothesis creation about sequence–structure–function relationships across sets of related proteins. Pythoscape provides several options to calculate pairwise similarities for input sequences or structures, applies filters to network edges and defines sets of similar nodes and their associated data as single nodes (termed representative nodes) for compression of network information and output data or formatted files for visualization.Contact: babbitt@cgl.ucsf.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Highlights

The rapid growth of databases of protein information provides both new opportunities and challenges for analysis and clustering by similarity
Global analysis of entire superfamilies and association of their members with biological information and other types of metadata has become a useful tool for functional annotation and discovery (Brown and Babbitt, 2012)
Mapping orthogonal sources of biological information onto Protein similarity networks (PSNs) provides a powerful way to view functional trends across the set that can be interpreted in the context of their similarities. (See Atkinson et al, 2009 for an initial analysis of some uses and statistical validation of PSNs.)

Summary

INTRODUCTION

The rapid growth of databases of protein information (e.g. sequences and structures) provides both new opportunities and challenges for analysis and clustering by similarity. Global analysis of entire superfamilies and association of their members with biological information and other types of metadata has become a useful tool for functional annotation and discovery (Brown and Babbitt, 2012) As these sets become larger (sometimes many thousands of sequences) and their members more divergent, their fast exploration on a large-scale becomes less feasible using traditional approaches such as alignments and trees. While PSNs are inherently amenable to association with orthogonal information sources, the many information types available complicate development of a single software solution for managing such diverse features Pythoscape addresses these issues and provides a software framework to create PSNs and develop new analyses for inference of functional properties in proteins

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