Molecular interactions are the underlying basis of all processes that are executed in an organism, and their complete mapping would be a great aid in understanding and interpreting both normal and disease functioning. Transcriptional regulatory interactions are of particular interest as they are critical in the proper spatial and temporal regulation of genes. This proposal aims to develop several novel and complementary computational methods for predicting transcription factor interactions and specificities, and for uncovering their conservation and variation across organisms. Taken together, these methods will vastly expand our knowledge of eukaryotic regulatory networks and their underlying principles. We will devise a combined constrained optimization and statistical approach to predict the DNA-binding specificities of multidomain C2H2 zinc finger proteins;these proteins comprise the largest class of transcription factors in eukaryotic genomes. We will also establish a novel comparative sequence framework for determining binding specificity variation amongst homologous transcription factors, as network divergence underlies much of the observed phenotypic and functional diversity between and within organisms;this framework will be applied to explore the extent to which changes in transcription factors can affect regulatory network variation across organisms. Finally, we will develop a cross-genomic framework for predicting genomic binding sites for transcription factors with known specificities, along with analysis techniques for inferring interactions amongst these transcription factors across organisms. The DNA-binding specificities for an increasing number of transcription factors are being determined, and this large-scale data presents new opportunities to map transcription factor binding sites and to uncover transcription factor- transcription factor interactions across organisms;these interactions are an important component of regulatory networks and their variation plays a key role in network divergence. Successful completion of these aims will result in computational methods that will significantly increase the rate with which transcriptional networks are characterized and will reveal fundamental aspects of their functioning and evolution. All developed software will be made publicly available.

Public Health Relevance

Cellular networks underlie all processes that are executed in an organism, and their complete mapping would aid in understanding both normal and disease functioning. The proposed research will yield software for uncovering and characterizing protein interactions and specificities. These computational tools will help to place proteins, including those important for disease, within the broader context of their cellular pathways, thereby expanding our understanding of diseases and providing an important avenue for uncovering putative drug targets.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Wu, Mary Ann
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Princeton University
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United States
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Pritykin, Yuri; Brito, Tarcisio; Schupbach, Trudi et al. (2017) Integrative analysis unveils new functions for the Drosophila Cutoff protein in noncoding RNA biogenesis and gene regulation. RNA 23:1097-1109
Ochoa, Alejandro; Singh, Mona (2017) Domain prediction with probabilistic directional context. Bioinformatics 33:2471-2478
Ochoa, Alejandro; Storey, John D; Llinás, Manuel et al. (2015) Beyond the E-Value: Stratified Statistics for Protein Domain Prediction. PLoS Comput Biol 11:e1004509
Persikov, Anton V; Wetzel, Joshua L; Rowland, Elizabeth F et al. (2015) A systematic survey of the Cys2His2 zinc finger DNA-binding landscape. Nucleic Acids Res 43:1965-84
Pritykin, Yuri; Ghersi, Dario; Singh, Mona (2015) Genome-Wide Detection and Analysis of Multifunctional Genes. PLoS Comput Biol 11:e1004467
Nadimpalli, Shilpa; Persikov, Anton V; Singh, Mona (2015) Pervasive variation of transcription factor orthologs contributes to regulatory network evolution. PLoS Genet 11:e1005011
Ghersi, Dario; Singh, Mona (2014) Interaction-based discovery of functionally important genes in cancers. Nucleic Acids Res 42:e18
Jiang, Peng; Singh, Mona (2014) CCAT: Combinatorial Code Analysis Tool for transcriptional regulation. Nucleic Acids Res 42:2833-47
Ghersi, Dario; Singh, Mona (2014) molBLOCKS: decomposing small molecule sets and uncovering enriched fragments. Bioinformatics 30:2081-3
Persikov, Anton V; Rowland, Elizabeth F; Oakes, Benjamin L et al. (2014) Deep sequencing of large library selections allows computational discovery of diverse sets of zinc fingers that bind common targets. Nucleic Acids Res 42:1497-508

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