ArticleBMC bioinformatics2016
Evaluating tools for transcription factor binding site prediction.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed, 149 citations in OpenAlex.
- Massively parallel characterization and predictive modelling of neuronal regulatory variation.bioRxiv : the preprint server for biology · 2026Article
- NovelbioRxiv : the preprint server for biology · 2026Article
- Widespread low-affinity motifs enhance chromatin accessibility and regulatory potential in mESCs.bioRxiv : the preprint server for biology · 2025Article
- Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data.Briefings in bioinformatics · 2025Article
- Profiling conserved transcription factor binding motifs in Phaseolus vulgaris through comparative genomics.BMC genomics · 2025Article
- YBX1 alleviates ferroptosis in osteoporosis via the ATF4/FSP1 axis in an mJournal of orthopaedic surgery and research · 2025Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data.NAR genomics and bioinformatics · 2024Article
- BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function.International journal of molecular sciences · 2024Article
- Harnessing regulatory networks in Actinobacteria for natural product discovery.Journal of industrial microbiology & biotechnology · 2024Review
- Comprehensive analysis of transcription factor binding sites and expression profiling of rice pathogenesis related genes (Frontiers in plant science · 2024Article
- Recent advances in exploring transcriptional regulatory landscape of crops.Frontiers in plant science · 2024Review
- Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.Nucleic acids research · 2023Article
- Predicting the impact of sequence motifs on gene regulation using single-cell data.Genome biology · 2023Article
- Removal of evolutionarily conserved functional MYC domains in a tilapia cell line using a vector-based CRISPR/Cas9 system.Scientific reports · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Pharmacogenomics: Driving Personalized Medicine.Pharmacological reviews · 2023Review
- Quantitative model for genome-wide cyclic AMP receptor protein binding site identification and characteristic analysis.Briefings in bioinformatics · 2023Article
- Article
- Methodology for Constructing a Knowledgebase for Plant Gene Regulation Information.Methods in molecular biology (Clifton, N.J.) · 2023Article
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 at 1 institution in 1 country.
Funding
Abstract
backgroundBinding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease. TFBSs are generally recognized by scanning a position weight matrix (PWM) against DNA using one of a number of available computer programs. Thus we set out to evaluate the best tools that can be used locally (and are therefore suitable for large-scale analyses) for creating PWMs from high-throughput ChIP-Seq data and for scanning them against DNA.
resultsWe evaluated a set of de novo motif discovery tools that could be downloaded and installed locally using ENCODE-ChIP-Seq data and showed that rGADEM was the best-performing tool. TFBS prediction tools used to scan PWMs against DNA fall into two classes - those that predict individual TFBSs and those that identify clusters. Our evaluation showed that FIMO and MCAST performed best respectively.
conclusionsSelection of the best-performing tools for generating PWMs from ChIP-Seq data and for scanning PWMs against DNA has the potential to improve prediction of precise transcription factor binding sites within regions identified by ChIP-Seq experiments for gene finding, understanding regulation and in evaluating the effects of single nucleotide variations in causing disease.
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.