Searching journal content for articles similar to Hasegawa and Struhl 29 (12): 1939.

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  1. Research: Promoter architecture determines cotranslational regulation of mRNA
    • Lorena Espinar,
    • Miquel Àngel Schikora Tamarit,
    • Júlia Domingo,
    • and Lucas B. Carey
    Genome Res. April 2018 28: 509-518; Published in Advance March 22, 2018, doi:10.1101/gr.230458.117
    ...without one (TATA-less). TATA-binding protein (TBP) binds TATA+ promoters at the TATA box, but TBP also binds TATA-less promoters at a TATA-like sequence that is one or two mismatches away from the TATA consensus. TATA-less promoters are thus also referred to as TATA-like. While recent work suggests...
  2. Research: Understanding transcriptional regulation by integrative analysis of transcription factor binding data
    • Chao Cheng,
    • Roger Alexander,
    • Renqiang Min,
    • Jing Leng,
    • Kevin Y. Yip,
    • Joel Rozowsky,
    • Koon-Kiu Yan,
    • Xianjun Dong,
    • Sarah Djebali,
    • Yijun Ruan,
    • Carrie A. Davis,
    • Piero Carninci,
    • Timo Lassman,
    • Thomas R. Gingeras,
    • Roderic Guigó,
    • Ewan Birney,
    • Zhiping Weng,
    • Michael Snyder,
    • and Mark Gerstein
    Genome Res. September 2012 22: 1658-1667; doi:10.1101/gr.136838.111
    ..., the TATA-binding protein (TBP) is a common subunit required by all three of the human RNA polymerases, I, II, and III (Kornberg 2007). Binding of these general TFs is essential for transcriptional initiation of most promoters, and therefore it makes sense that their binding signals have the highest...
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  3. Resource: Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors
    • Jie Wang,
    • Jiali Zhuang,
    • Sowmya Iyer,
    • XinYing Lin,
    • Troy W. Whitfield,
    • Melissa C. Greven,
    • Brian G. Pierce,
    • Xianjun Dong,
    • Anshul Kundaje,
    • Yong Cheng,
    • Oliver J. Rando,
    • Ewan Birney,
    • Richard M. Myers,
    • William S. Noble,
    • Michael Snyder,
    • and Zhiping Weng
    Genome Res. September 2012 22: 1798-1812; doi:10.1101/gr.139105.112
    ...Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors Jie Wang 1 , 8 , Jiali Zhuang 1 , 8 , Sowmya Iyer 1 , 2 , 8 , XinYing Lin 1 , 8 , Troy W. Whitfield 1 , Melissa C...
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  4. Research: Temporal uncoupling of the DNA methylome and transcriptional repression during embryogenesis
    • Ozren Bogdanović,
    • Steven W. Long,
    • Simon J. van Heeringen,
    • Arie B. Brinkman,
    • Jose Luis Gómez-Skarmeta,
    • Hendrik G. Stunnenberg,
    • Peter L. Jones,
    • and Gert Jan C. Veenstra
    Genome Res. August 2011 21: 1313-1327; Published in Advance June 2, 2011, doi:10.1101/gr.114843.110
    ...reprogramming of themethylome during the dynamic early phase of development. Results DNA methylation profiles of X. tropicalis embryos Genomic DNA isolated from blastula and gastrula (NieuwkoopFaber stages 9 and 12.5) embryos was purified by affinity capture (MethylCap) using the methyl-CpG-binding domain (MBD...
  5. Research: TATA is a modular component of synthetic promoters
    • Ilaria Mogno,
    • Francesco Vallania,
    • Robi D. Mitra,
    • and Barak A. Cohen
    Genome Res. October 2010 20: 1391-1397; Published in Advance July 13, 2010, doi:10.1101/gr.106732.110
    ...-regulation (Gertz and Cohen 2009). One important variable in gene expression is the interaction of RNA polymerase with DNA. A critical step in the regulation of eukaryotic genes is the recruitment of the TATA binding protein (TBP), which bends the DNA to allow binding of RNA polymerase II and transcription...
  6. Methods: Generic eukaryotic core promoter prediction using structural features of DNA
    • Thomas Abeel,
    • Yvan Saeys,
    • Eric Bonnet,
    • Pierre Rouzé,
    • and Yves Van de Peer
    Genome Res. February 2008 18: 310-323; Published in Advance December 20, 2007, doi:10.1101/gr.6991408
    ...to identify promoters. The more recent and sophisticated Promoter Prediction Programs (PPPs) look for these promoter-specific characteristics by using machine learning techniques such as discriminant analyses, Hidden Markov Models, and Artificial Neural Networks to predict and delineate promoters (for reviews...
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