ArticleGenome research2010
Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities.
Article in Genome research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 276 papers, 1 of them a synthesis that pooled it.
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
276 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association, characterisation and meta-analysis of SNPs linked to general reading ability in a German dyslexia case-control cohort.Scientific reports · 2016Pooled it
- Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
- GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors.Nature methods · 2026Article
- Extensive binding of poorly characterized human transcription factors to genomic dark matter.Nature communications · 2026Article
- Molecular basis of UV lesion binding and repair inhibition by ETS-family transcription factors.Nucleic acids research · 2026Article
- SELEX-Based Aptamer Technologies for Toxin Analysis: Screening, Optimization, and Computational Assisted Design.Toxins · 2026Review
- Autoimmune non-coding variants perturb transcription factor-cofactor complex assembly linked to enhancer activity.bioRxiv : the preprint server for biology · 2026Article
- Systematic evaluation of scallop (Argopecten irradians) SoxH reveals its crucial role in spermiogenesis with limited evidence for sex determination.Communications biology · 2026Article
- A Data-Analysis Pipeline for High-Throughput Systematic Evolution of Ligands by Exponential Enrichment (HT-SELEX) in the Characterization of Telomeric Proteins.bioRxiv : the preprint server for biology · 2026Article
- Single-molecule parallel analysis for rapid exploration of sequence space.Nature protocols · 2026Review
- RaptScore: a large language model-based algorithm for versatile aptamer evaluation.Nucleic acids research · 2026Article
- Molecular basis of UV lesion binding and repair inhibition by ETS-family transcription factors.bioRxiv : the preprint server for biology · 2026Article
- Decoding cnidarian cell type gene regulation.Nature ecology & evolution · 2026Article
- ssG4-seq for global profiling of strand-specific G-quadruplex structures in mammalian genomes.Nature communications · 2025Article
- Unveiling a pervasive DNA adenine methylation regulatory network in the early-diverging fungus Rhizopus microsporus.Nature communications · 2025Article
- Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.Communications biology · 2025Article
- TRAFICA: an open chromatin language model to improve transcription factor binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- Genome-wide investigation of transcription factor footprints and dynamics using cFOOT-seq.Protein & cell · 2025Article
- Article
- Article
216 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
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genetic code-the binding specificity of all transfer-RNAs--defines how protein primary structure is determined by DNA sequence. DNA also dictates when and where proteins are expressed, and this information is encoded in a pattern of specific sequence motifs that are recognized by transcription factors. However, the DNA-binding specificity is only known for a small fraction of the approximately 1400 human transcription factors (TFs). We describe here a high-throughput method for analyzing transcription factor binding specificity that is based on systematic evolution of ligands by exponential enrichment (SELEX) and massively parallel sequencing. The method is optimized for analysis of large numbers of TFs in parallel through the use of affinity-tagged proteins, barcoded selection oligonucleotides, and multiplexed sequencing. Data are analyzed by a new bioinformatic platform that uses the hundreds of thousands of sequencing reads obtained to control the quality of the experiments and to generate binding motifs for the TFs. The described technology allows higher throughput and identification of much longer binding profiles than current microarray-based methods. In addition, as our method is based on proteins expressed in mammalian cells, it can also be used to characterize DNA-binding preferences of full-length proteins or proteins requiring post-translational modifications. We validate the method by determining binding specificities of 14 different classes of TFs and by confirming the specificities for NFATC1 and RFX3 using ChIP-seq. Our results reveal unexpected dimeric modes of binding for several factors that were thought to preferentially bind DNA as monomers.
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.