ArticleNucleic acids research2016
Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed.
- Ultra-fast variant effect prediction using biophysical transcription factor binding models.Nucleic acids research · 2025Article
- Asymmetry of Motif Conservation Within Their Homotypic Pairs Distinguishes DNA-Binding Domains of Target Transcription Factors in ChIP-Seq Data.International journal of molecular sciences · 2025Article
- Coactivator networks orchestrating noncanonical AR programs in enzalutamide-resistant CRPC.Frontiers in oncology · 2025Review
- Enhanced detection of RNA modifications and read mapping with high-accuracy nanopore RNA basecalling models.Genome research · 2024Article
- MethylSeqLogo: DNA methylation smart sequence logos.BMC bioinformatics · 2024Article
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data.NAR genomics and bioinformatics · 2024Article
- Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq.Nucleic acids research · 2024Article
- Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.Nucleic acids research · 2023Article
- Prediction of cooperative homeodomain DNA binding sites from high-throughput-SELEX data.Nucleic acids research · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks.Frontiers in cell and developmental biology · 2023Article
- Investigating the sequence landscape in thePeerJ · 2023Article
- NF-κB signaling controls H3K9me3 levels at intronic LINE-1 and hematopoietic stem cell genes in cis.The Journal of experimental medicine · 2022Article
- RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites.PLoS computational biology · 2022Article
- Motif models proposing independent and interdependent impacts of nucleotides are related to high and low affinity transcription factor binding sites in Arabidopsis.Frontiers in plant science · 2022Article
- Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins.Bioinformatics (Oxford, England) · 2021Article
- Bayesian Markov models improve the prediction of binding motifs beyond first order.NAR genomics and bioinformatics · 2021Article
- GTRD: an integrated view of transcription regulation.Nucleic acids research · 2021Article
- Systematic Evaluation of DNA Sequence Variations onFrontiers in genetics · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Position weight matrices (PWMs) are the standard model for DNA and RNA regulatory motifs. In PWMs nucleotide probabilities are independent of nucleotides at other positions. Models that account for dependencies need many parameters and are prone to overfitting. We have developed a Bayesian approach for motif discovery using Markov models in which conditional probabilities of order k - 1 act as priors for those of order k This Bayesian Markov model (BaMM) training automatically adapts model complexity to the amount of available data. We also derive an EM algorithm for de-novo discovery of enriched motifs. For transcription factor binding, BaMMs achieve significantly (P = 1/16) higher cross-validated partial AUC than PWMs in 97% of 446 ChIP-seq ENCODE datasets and improve performance by 36% on average. BaMMs also learn complex multipartite motifs, improving predictions of transcription start sites, polyadenylation sites, bacterial pause sites, and RNA binding sites by 26-101%. BaMMs never performed worse than PWMs. These robust improvements argue in favour of generally replacing PWMs by BaMMs.
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.