DNA-binding specificities of human transcription factors.

By A. Jolma, Jian Yan, Thomas Whitington, Jarkko Toivonen, K. Nitta, Pasi Rastas, E. Morgunova, M. Enge, Mikko Taipale, Gong-hong Wei and 9 more. 2013

1,264 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): “Although not all 40 bp sequences were interrogated due to the limiting amount of DNA, TFs do not generally bind to exact matches of 40 bp (80 bits of information); using the amounts of DNA used, HT-SELEX allows for identification of motifs whose information content is in the order of ~40 bits, excee”
Ensembl regulation resources (2016, methodology): “Other techniques such as UniPROBE (51) or SELEX (52) will allow us to describe far richer binding interactions at enhancer and promoter sites.”
Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments (2013, background): “Experimental and evolutionary validation of novel motifs Following the motif discovery and selection of these putative novel motifs, a study released hundreds of new motifs generated using high-throughput SELEX (16).”
Distinct structural bases for sequence-specific DNA binding by mammalian BEN domain proteins (2022, neutral): “The BEND3-BD4 site is particularly similar to EGR family proteins, which can directly recognize CGCCCACGC motifs (Jolma et al. 2013), and inclusion of EGR sites among the catalog of known DNA binding motifs (http://jaspar.genereg.net) underlies its recovery in CentriMo analysis.”
The Human Transcription Factors. (2018, positive): “To develop the current TF catalog, we have drawn heavily upon motif collections such as TRANSFAC (Matys et al., 2006), JASPAR (Mathelier et al., 2016), HT-SELEX (Jolma et al., 2013; Jolma et al., 2015; Yin et al., 2017), UniPROBE (Hume et al., 2015), and CisBP (Weirauch et al., 2014), along with pre”
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity (2014, positive): “In contrast, it is relatively straightforward to derivemotifs from all of the commonmethods for in vitro analysis of TF sequence specificity, including protein bindingmicroarrays (PBMs), bacterial 1-hybrid (B1H), and high-throughput in vitro selection (HT-SELEX) (Stormo and Zhao, 2010), all of which”
Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. (2013, positive): “The clusters were heavily enriched in motifs, most of which were similar to known in vitro motifs (Badis et al., 2009; Jolma et al., 2013) for CTCF and for different classes of TFs, including C2H2 zinc finger, ETS, bZIP, nuclear receptor, GATA, homeodomain, NFI, and forkhead proteins (Figure 2A).”
Transcription factors activate genes through the phase separation capacity of their activation domains (2018, background): “These DBDs selectively bind specific DNA sequences that range from 4 to 12 bp, and the DNA binding sequences favored by hundreds of TFs have been described (Hume et al., 2015; Jolma et al., 2013; Khan et al., 2018).”
Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX) (2018, background): “This uncertainty in the segmentation of sequences has made overlapping k-mers one of the most popular representations in machine learning for all areas of bioinformatics research, including proteomics [5, 9], genomics [10, 11], epigenomics [12, 13], and metagenomics [14, 15].”