ArticlePLoS computational biology2009
Inferring binding energies from selected binding sites.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
131 citing papers in PubMed.
- ShapeME: A Tool and Web Front-end for De Novo Discovery of Structural Motifs Underpinning Protein-DNA Interactions.Journal of molecular biology · 2026Article
- SELEX-Based Aptamer Technologies for Toxin Analysis: Screening, Optimization, and Computational Assisted Design.Toxins · 2026Review
- A resource of RNA-binding protein motifs across eukaryotes reveals evolutionary dynamics and gene-regulatory function.Nature biotechnology · 2026Article
- RaptScore: a large language model-based algorithm for versatile aptamer evaluation.Nucleic acids research · 2026Article
- Characterizing the regulatory logic of transcriptional control at the DNA sequence level by ensembles of thermodynamic models.Bioinformatics (Oxford, England) · 2025Article
- Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction.Journal of chemical theory and computation · 2025Article
- ShapeME: A tool and web front-end for de novo discovery of structural motifs underpinning protein-DNA interactions.bioRxiv : the preprint server for biology · 2025Article
- Active learning of enhancers and silencers in the developing neural retina.Cell systems · 2025Article
- Geometric deep learning of protein-DNA binding specificity.Nature methods · 2024Article
- Active learning of enhancer and silencer regulatory grammar in photoreceptors.bioRxiv : the preprint server for biology · 2023Article
- A reverse chromatin immunoprecipitation technique based on the CRISPR-dCas9 system.Plant physiology · 2023Article
- Physicochemical models of protein-DNA binding with standard and modified base pairs.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Article
- Bayesian Markov models improve the prediction of binding motifs beyond first order.NAR genomics and bioinformatics · 2021Article
- RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information.Nucleic acids research · 2020Article
- Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions.Toxicology · 2020Review
- Quantitative analysis of transcription factor binding and expression using calling cards reporter arrays.Nucleic acids research · 2020Article
- A preliminary computational outputs versus experimental results: Application of sTRAP, a biophysical tool for the analysis of SNPs of transcription factor-binding sites.Molecular genetics & genomic medicine · 2020Article
- Article
71 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific parameters that may include non-independent contributions of bases to the interaction. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. On simulated data, where the true energy model is known and samples are generated with a variety of parameter values, we show that our method returns much more accurate estimates of the true parameters and much better predictions of the selected binding site distributions. We also introduce a new high-throughput SELEX (HT-SELEX) procedure to determine the binding specificity of a transcription factor in which the initial randomized library and the selected sites are sequenced with next generation methods that return hundreds of thousands of sites. We show that after a single round of selection our method can estimate binding parameters that give very good fits to the selected site distributions, much better than standard motif identification algorithms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.