Graphery: interactive tutorials for biological network algorithms

Author:

Zeng Heyuan12,Zhang Jinbiao3,Preising Gabriel A2,Rubel Tobias2,Singh Pramesh2,Ritz Anna2ORCID

Affiliation:

1. Computer Science Department, Reed College, 3203 SE Woodstock Blvd, Portland, OR 97202, USA

2. Biology Department, Reed College, 3203 SE Woodstock Blvd, Portland, OR 97202, USA

3. Information and Communication Technology Department, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor Darul Ehsan, Malaysia

Abstract

Abstract Networks have been an excellent framework for modeling complex biological information, but the methodological details of network-based tools are often described for a technical audience. We have developed Graphery, an interactive tutorial webserver that illustrates foundational graph concepts frequently used in network-based methods. Each tutorial describes a graph concept along with executable Python code that can be interactively run on a graph. Users navigate each tutorial using their choice of real-world biological networks that highlight the diverse applications of network algorithms. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Graphery is available at https://graphery.reedcompbio.org/.

Funder

National Science Foundation

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Genetics

Reference48 articles.

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