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Full-Text Articles in Genetics and Genomics
Genetic And Molecular Analysis Of Phytochromes From The Filamentous Fungus Neurospora Crassa, Allan C. Froehlich, Bosl Noh, Richard D. Vierstra, Jennifer Loros, Jay C. Dunlap
Genetic And Molecular Analysis Of Phytochromes From The Filamentous Fungus Neurospora Crassa, Allan C. Froehlich, Bosl Noh, Richard D. Vierstra, Jennifer Loros, Jay C. Dunlap
Dartmouth Scholarship
Phytochromes (Phys) comprise a superfamily of red-/far-red-light-sensing proteins. Whereas higher-plant Phys that control numerous growth and developmental processes have been well described, the biochemical characteristics and functions of the microbial forms are largely unknown. Here, we describe analyses of the expression, regulation, and activities of two Phys in the filamentous fungus Neurospora crassa. In addition to containing the signature N-terminal domain predicted to covalently associate with a bilin chromophore, PHY-1 and PHY-2 contain C-terminal histidine kinase and response regulator motifs, implying that they function as hybrid two-component sensor kinases activated by light. A bacterially expressed N-terminal fragment of PHY-2 covalently …
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Dartmouth Scholarship
The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …