Molecular Assemblies, Genes and Genomics Integrated Efficiently (MAGGIE) Page: 3 of 6
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To understand how microbial functions are regulsted we have developed
complementary algorithms for reconstructing gene regulatory networks (SRNs).
Whereas the network inference algorithms Monkey and Inferelator developed
enable de novo reconstruction of predictive models for GRNs from diverse systems
biology data, the RegPrecise and RegPredict framework developed uses
evolutionary comparisons of genomes from closely related organisms to
reconstruct conserved regulons. We have integrated the two complementary
algorithms to rapidly generate comprehensive models for gene regulation of
understudied organisms. Our preliminary analyses of these reconstructed GRNs
have revealed novel regulatory mechanisrms and cis-regulatory motifs, as well as
others that are conserved across species.
Finally, we are supporting scientific efforts rn ENIGMA with data
management solutions and by integrating all of the algorithms, software and data
into a Knowledgebase. For Instance, we have developed the RegPrecise
database (http:/reQpreciseIbLpov) which represents manually curated sets of
regulons laying the basis for automatic annotation of regulatory interactions in
closely related species. We are also in the midst of scaling up MicrobesOnline to
handle the growing volume of sequence and functional genornics data. Over the
last year our efforts have been focused on providing support for additional genomic
and functional genomic data types. Similarly, we have developed several
visualization tools to help with the exploration of complex systems biology
datasets A case in point is the Gaggle Genorne Browser (GG21), which was
enhanced with visualizations for plotting peptide detections and protein-DNA
binding alongside transciplome structure, plus the ability to interactively filter by
signal intensity or p-value. Finally, we recognize that future advances to
computational infrastructure cannot be anticipated and new software will be
developed as dictated by scientific needs within ENIGMA and elsewhere. To
account for this reality of how software environments evolve we have made
advances to the Gaggle and Firegoose framework that enables interoperability and
integration of diverse software and databases. Specifically, we have updated the
R-goose package which provides connectivity between several Gaggle compliant
bioinformatics tools and R; and prmtotyped a JSON based upgrade to the Gaggle
protocol to make this environment extensible and more language neutral than the
previous Java-based protocol.
- Yoon SH, Reiss DJ, Bare JC, Tenenbaum D, Pan M, Slagel J, Lim S,
Hackett M, Menon AL. Adams MW, Barnebey A, Yanrione SM, Leigh JA,
and Baliga NS. "Parallel evolution of transcriptome architecture during
genome reorganization" (Geneme Research in review)
2. Robinson CK, Webb K. Kaur A. Jaruga P, Dizdaroglu M. Baliga NS, Place
A, Diruggiero J. A Major Role for Non-Enzymatic Antioxidant Processes in
the Radioresistarice of Halobacterium salinarun J Bacteriol, 2011 Jan 25.
3 Brooks AN, Turkarsian , eer KD. Yin Lo F, Ba-iga NS. Adaptation of cells
to new environments. Wiley Interdiscip Rev Syst Bial Med. 2010 Dec 31
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Baliga, Nitin S. Molecular Assemblies, Genes and Genomics Integrated Efficiently (MAGGIE), text, May 26, 2011; United States. (digital.library.unt.edu/ark:/67531/metadc846214/m1/3/: accessed December 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.