RT Journal Article
SR Electronic
T1 Tribe: The collaborative platform for reproducible web-based analysis of gene sets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 055913
DO 10.1101/055913
A1 René A Zelaya
A1 Aaron K. Wong
A1 Alex T. Frase
A1 Marylyn D. Ritchie
A1 Casey S. Greene
YR 2016
UL http://biorxiv.org/content/early/2016/05/27/055913.abstract
AB Background The adoption of new bioinformatics webservers provides biological researchers with new analytical opportunities but also raise workflow challenges. These challenges include sharing collections of genes with collaborators, translating gene identifiers to the most appropriate nomenclature for each server, tracking these collections across multiple analysis tools and webservers, and maintaining effective records of the genes used in each analysis.Description In this paper, we present the Tribe webserver (available at https://tribe.greenelab.com), which addresses these challenges in order to make multi-server workflows seamless and reproducible. This allows users to create analysis pipelines that use their own sets of genes in combinations of specialized data mining webservers and tools while seamlessly maintaining gene set version control. Tribe’s web interface facilitates collaborative editing: users can share with collaborators, who can then view, download, and edit these collections. Tribe’s fully-featured API allows users to interact with Tribe programmatically if desired. Tribe implements the OAuth 2.0 standard as well as gene identifier mapping, which facilitates its integration into existing servers. Access to Tribe’s resources is facilitated by an easy-to-install Python application called tribe-client. We provide Tribe and tribe-client under a permissive open-source license to encourage others to download the source code and set up a local instance or to extend its capabilities.Conclusions The Tribe webserver addresses challenges that have made reproducible multi-webserver workflows difficult to implement until now. It is open source, has a user-friendly web interface, and provides a means for researchers to perform reproducible gene set based analyses seamlessly across webservers and command line tools.