Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity

By Matthew T. Weirauch, A. Yang, Mihai Albu, Atina G. Coté, Alejandro Montenegro-Montero, Philipp Drewe, H. Najafabadi, Samuel A. Lambert, I. Mann, Kate B. Cook and 18 more. 2014

1,710 citations recorded by the source corpus.

SUMMARY Transcription factor (TF) DNA sequence preferences direct their regulatory activity, but are currently known for only ~1% of all eukaryotic TFs. Broadly sampling DNA-binding domain (DBD) types from multiple eukaryotic clades, we determined DNA sequence preferences for >1,000 TFs encompassing 54 different DBD classes from 131 diverse eukaryotes. We find that closely related DBDs almost always have very similar DNA sequence preferences, enabling inference of motifs for ~34% of the ~170,000 known or predicted eukaryotic TFs. Sequences matching both measured and inferred motifs are enriched in ChIP-seq peaks and upstream of transcription start sites in diverse eukaryotic lineages. SNPs defining expression quantitative trait loci in Arabidopsis promoters are also enriched for predicted TF binding sites. Importantly, our motif “library” (http://cisbp.ccbr.utoronto.ca) can be used to identify specific TFs whose binding may be altered by human disease risk alleles. These data present a powerful resource for mapping transcriptional networks across eukaryotes.

Subfields: Molecular Biology

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Citations in the corpus

AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors (2018, methodology): “The TF motif matrices of human for prediction was gathered from TRANSFAC, JASPAR, HOCOMOCO and CIS-BP databases.”
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”
Conservation of transcription factor binding specificities across 600 million years of bilateria evolution (2015, background): “As suggested by previous studies (Wei et al., 2010; Jolma et al., 2013; Weirauch et al., 2014), different TF structural families had clearly divergent specificities, and amino-acid sequence similarity was predictive of overall DNA binding specificity in most TF families.”
Mapping genome-wide transcription-factor binding sites using DAP-seq (2017, methodology): “Enriched motifs such as those produced by the MEME motif discovery software can be compared to comprehensive databases of known TF binding sites 9 .”
HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis (2017, methodology): “For HOCOMOCO v11 we performed a careful reprocessing of the experimental data rather than the aggregation of TFBS models reported by other studies (34–36).”
JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles (2015, methodology): “The curated profiles were derived from PBM (14,16,19,20), HT-SELEX (15) and ChIP-seq (21) experiments.”
Distinct structural bases for sequence-specific DNA binding by mammalian BEN domain proteins (2022, positive): “PBM data were generated with motifs derived using Top10AlignZ (Weirauch et al. 2014).”
Cistrome and Epicistrome Features Shape the Regulatory DNA Landscape (2016, positive): “The DAPseq-derived motif matched the motifs derived from both ChIPseq and PBM (Weirauch et al., 2014), although the DAP- and ChIP-seq motifs shared more sequence similarity at the edges (Figure 1E).”
JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework (2017, background): “While the non-redundancy of binding profiles is one of the guiding principles of JASPAR, TFs with similar DBDs often have similar binding preferences (35,36).”