Diversity and Complexity in DNA Recognition by Transcription Factors

By Gwenael Badis, M. Berger, A. Philippakis, Shaheynoor Talukder, Andrew R. Gehrke, S. Jaeger, Esther T. Chan, Genita Metzler, A. Vedenko, Xiaoyu Chen and 7 more. 2009

1,016 citations recorded by the source corpus.

Subfields: Molecular Biology

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Citations in the corpus

Impact of cytosine methylation on DNA binding specificities of human transcription factors (2017, methodology): “The 16× HK array design with 40,000 unique DNA features [as described in (11, 35)] were double stranded as described in (11).”
Densely interconnected transcriptional circuits control cell states in human hematopoiesis (2011, neutral): “The 396 enriched motifs were compared to a library of previously published motifs of known transcription factors (Badis et al., 2009; Berger et al., 2008; Chang et al., 2008; Matys et al., 2003; Sandelin et al., 2004).”
UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein–DNA interactions (2014, methodology): “Publications with data in UniPROBE whose protein pages currently link to TFBSshape (and vice versa) are: Berger et al. , 2006 (1); Berger et al. , 2008 (26); Zhu et al. , 2009 (27); Badis et al. , 2009 (28); Lesch et al. , 2009 (30); Scharer et al. , 2009 (32).”
Linking disease associations with regulatory information in the human genome (2012, result): “This is similar to cases of variable spacer length previously observed for transcription factor binding motifs (Badis et al. 2009).”
JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles (2015, background): “The database provides non-redundant profiles (one profile per TF) with the exception of specific TFs which recognize TFBS in two or more distinct forms (17), either mediated by two distinct DBDs in the same TF or in a flexible spacing between protein–DNA contacts (e.g. SREBF1 or TFAP2A).”
Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. (2010, methodology): “By including recent profiles generated by protein-binding microarrays (Berger et al. 2008; Badis et al. 2009) and considering protein-level similarities in DBDs, these models can be extended to ;300–400 human TFs.”
Conservation of transcription factor binding specificities across 600 million years of bilateria evolution (2015, methodology): “Similar to earlier studies (Badis et al., 2009; Jolma et al., 2013), a relatively low success rate was observed for C2H2 zinc fingers and basic helix-loop-helix proteins, probably at least in part due to difficulty in computational identification of DNA-binding zinc finger proteins, and the fact tha”
JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework (2017, background): “Excep-tionally, multiple TF-binding profiles are associated to a TF when it is known to interact with DNA with multiple distinct sequence preferences, due to differential splicing for example (6,7).”
DNA-binding specificities of plant transcription factors and their potential to define target genes (2014, background): “In line with our results, previous studies with mouse TFs showed that approximately half of them also recognized secondary elements, which were annotated into four different classes (3).”
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity (2014, neutral): “, 2006) and Z scores (that scale approximately with binding affinity) (Badis et al., 2009).”
Evaluation of methods for modeling transcription-factor sequence specificity (2013, background): “Team_C (7) PWM + k-mers + Random forests Constructs blended predictions from random forests of contiguous k-mers (length 4 through 6) and RankMotif++27 PWMs.”