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Comparative Microbial Modules Resource: Generation and Visualization of Multi-species Biclusters

Overview of attention for article published in PLoS Computational Biology, December 2011
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Title
Comparative Microbial Modules Resource: Generation and Visualization of Multi-species Biclusters
Published in
PLoS Computational Biology, December 2011
DOI 10.1371/journal.pcbi.1002228
Pubmed ID
Authors

Thadeous Kacmarczyk, Peter Waltman, Ashley Bate, Patrick Eichenberger, Richard Bonneau

Abstract

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures - results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation.

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Geographical breakdown

Country Count As %
United States 4 5%
United Kingdom 1 1%
Israel 1 1%
Belgium 1 1%
Poland 1 1%
Unknown 65 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 32%
Researcher 23 32%
Professor > Associate Professor 9 12%
Student > Master 4 5%
Student > Doctoral Student 3 4%
Other 8 11%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 63%
Biochemistry, Genetics and Molecular Biology 10 14%
Computer Science 4 5%
Immunology and Microbiology 2 3%
Mathematics 1 1%
Other 5 7%
Unknown 5 7%