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Longabaugh, W. J. R.

Publications and source records attributed to Longabaugh, W. J. R..

2 recordsLinked to original sources

BioFabric Visualization of Network Alignments

BackgroundDozens of global network alignment algorithms have been developed over the past fifteen years. Effective network visualization tools are lacking and would enhance our ability to gain an intuitive understanding of the strengths and weaknesses of these algorithms. ResultsWe have created a plugin to the existing network visualization tool BioFabric, called VISNAB: Visualization of Network Alignments using BioFabric. We leverage BioFabrics unique approach to layout (nodes are horizontal lines connected by vertical lines representing edges) to improve understanding of network alignment performance. Our visualization tool allows the user to clearly spot deficiencies in alignments that cannot be detected through simply evaluating and comparing standard numerical topological measures such as the Edge Coverage (EC) or Symmetric Substructure Score (S3). Furthermore, we provide new automatic layouts that allow researchers to identify problem areas in an alignment. Finally, our new definitions of node groups and link groups that arise from our visualization technique allows us to also introduce novel numeric measures for assessing alignment quality. ConclusionsOur new approach to visualize network alignments will allow researchers to gain a new, and better, understanding of the strengths and shortcomings of the many available network alignment algorithms.

bioinformatics

Absolute quantification of transcription factors reveals principles of gene regulation in erythropoiesis

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). Yet, despite years of studies we still do not know the protein copy number of TFs in the nucleus. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first Gene Regulatory Network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that in the nucleus, corepressors are dramatically more abundant than coactivators at the protein, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

systems biology