ArticlePlant physiology2019
Enhanced Maps of Transcription Factor Binding Sites Improve Regulatory Networks Learned from Accessible Chromatin Data.
Article in Plant physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 31 citations in OpenAlex.
- Modulating the activity of the APC/C regulator SISAMBA improves the sugar and antioxidant content of tomato fruits.Plant biotechnology journal · 2025Article
- A redundant transcription factor network steers spatiotemporal Arabidopsis triterpene synthesis.Nature plants · 2023Article
- RhizogenicProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice.Frontiers in plant science · 2023Article
- Prediction of Transcription Factor Regulators and Gene Regulatory Networks in Tomato Using Binding Site Information.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
- Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators.Nature plants · 2021Article
- Review
- Prediction of condition-specific regulatory genes using machine learning.Nucleic acids research · 2020Article
- Considerations in the analysis of plant chromatin accessibility data.Current opinion in plant biology · 2020Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining where transcription factors (TFs) bind in genomes provides insight into which transcriptional programs are active across organs, tissue types, and environmental conditions. Recent advances in high-throughput profiling of regulatory DNA have yielded large amounts of information about chromatin accessibility. Interpreting the functional significance of these data sets requires knowledge of which regulators are likely to bind these regions. This can be achieved by using information about TF-binding preferences, or motifs, to identify TF-binding events that are likely to be functional. Although different approaches exist to map motifs to DNA sequences, a systematic evaluation of these tools in plants is missing. Here, we compare four motif-mapping tools widely used in the Arabidopsis (
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