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Methods. 2019 Aug 30. pii: S1046-2023(19)30138-0. doi: 10.1016/j.ymeth.2019.08.010. [Epub ahead of print]

Generating topological protein interaction scores and data visualization with TopS.

Author information

1
Stowers Institute for Medical Research, Kansas City, MO 64110, USA.
2
Stowers Institute for Medical Research, Kansas City, MO 64110, USA; Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA. Electronic address: mpw@stowers.org.

Abstract

Detecting subnetworks in large networks is of great interest. Recently, we developed a topological score framework for the analysis of protein interaction networks and implemented it as a web application, called TopS. Given a multivariate data presented as a matrix, TopS generates topological scores between any column and row in the matrix aiming to identify overwhelming preference interactions. This information can be further used into visualization tools such as clusters and networks to investigate how networks benefit from these interactions. We present a web tool called TopS that aims to have an intuitive user interface. Users can upload data from a simple delimited CSV file that can be created in a spreadsheet program. As an output, user is provided with a scoring matrix as tab-delimited file that can be interchanged with other software, heatmap and clustering figures in pdf format. Here we demonstrate the current capabilities of TopS using an existing dataset generated for the study of the human Sin3 chromatin remodeling complex.

KEYWORDS:

HDAC; Histone deacetylase; Protein interaction network; Quantitative proteomics; SIN3; Topological scoring

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