Searching journal content for articles similar to Li et al. 29 (2): 281.

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  1. Method: Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data
    • Kaixuan Luo,
    • Jianling Zhong,
    • Alexias Safi,
    • Linda K. Hong,
    • Alok K. Tewari,
    • Lingyun Song,
    • Timothy E. Reddy,
    • Li Ma,
    • Gregory E. Crawford,
    • and Alexander J. Hartemink
    Genome Res. June 2022 32: 1183-1198; Published in Advance May 24, 2022, doi:10.1101/gr.272203.120
    ...: By doing one DNase-seq experiment at each time point, we obtained occupancy predictions for hundreds of TF motifs, allowing us to screen for TFs showing significant changes in occupancy. For example, TOP results suggest a significant role for FOX and GATA factors in GC-induced transcriptional response (Fig...
  2. Method: Evidence of widespread, independent sequence signature for transcription factor cobinding
    • Manqi Zhou,
    • Hongyang Li,
    • Xueqing Wang,
    • and Yuanfang Guan
    Genome Res. February 2021 31: 265-278; Published in Advance December 10, 2020, doi:10.1101/gr.267310.120
    ...Roadmap Projects have provided invaluable resources of the in vivo biochemical signatures, including chromatin accessibility and TF binding. Many exciting computational approaches have been developed to predict transcription factor binding sites (TFBSs) and to study what controls the binding patterns...
  3. Method: Fast decoding cell type–specific transcription factor binding landscape at single-nucleotide resolution
    • Hongyang Li and
    • Yuanfang Guan
    Genome Res. April 2021 31: 721-731; Published in Advance March 19, 2021, doi:10.1101/gr.269613.120
    ...the average area under receiver operating characteristic curve (AUROC) of 0.982 and the average area under precision recall curve (AUPRC) of 0.208. Our method substantially outperformed the state-of-the-art methods Anchor and FactorNet, improving the predictive AUPRC by 19% and 27%, respectively, when...
  4. Research: Kinetic networks identify TWIST2 as a key regulatory node in adipogenesis
    • Arun B. Dutta,
    • Daniel S. Lank,
    • Roza K. Przanowska,
    • Piotr Przanowski,
    • Lixin Wang,
    • Bao Nguyen,
    • Ninad M. Walavalkar,
    • Fabiana M. Duarte,
    • and Michael J. Guertin
    Genome Res. March 2023 33: 314-331; Published in Advance February 21, 2023, doi:10.1101/gr.277559.122
    ...; Supplemental Fig. S1H). We find distal REs are more likely to show accessibility changes, whereas promoters are more likely to show bidirectional transcription changes.Defining predicted TF binding events as trans-edges in the networkWe determined candidate functional TFs within the set of REs by searching...
    OPEN ACCESS ARTICLE
  5. Research: Evolution at two levels of gene expression in yeast
    • Carlo G. Artieri and
    • Hunter B. Fraser
    Genome Res. March 2014 24: 411-421; Published in Advance December 6, 2013, doi:10.1101/gr.165522.113
    ...acting at both levels of regulation. Finally, we also uncover multiple instances of stop-codon readthrough that are conserved between species. Our analysis reveals the underappreciated complexity of post-transcriptional regulatory divergence and indicates that partitioning the search for the locus...
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