ArticleNucleic acids research2008
MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs.
Article in Nucleic acids research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 43 citations in OpenAlex.
- A systematic study of motif pairs that may facilitate enhancer-promoter interactions.Journal of integrative bioinformatics · 2022Article
- Article
- PETModule: a motif module based approach for enhancer target gene prediction.Scientific reports · 2016Article
- MicroRNA modules prefer to bind weak and unconventional target sites.Bioinformatics (Oxford, England) · 2015Article
- Lhx1 controls terminal differentiation and circadian function of the suprachiasmatic nucleus.Cell reports · 2014Article
- SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data.Nucleic acids research · 2014Article
- JunD/AP1 regulatory network analysis during macrophage activation in a rat model of crescentic glomerulonephritis.BMC systems biology · 2013Article
- Using graph models to find transcription factor modules: the hitting set problem and an exact algorithm.Algorithms for molecular biology : AMB · 2013Article
- Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response.Frontiers in genetics · 2013Article
- Systematic prediction of cis-regulatory elements in the Chlamydomonas reinhardtii genome using comparative genomics.Plant physiology · 2012Article
- Motif analysis unveils the possible co-regulation of chloroplast genes and nuclear genes encoding chloroplast proteins.Plant molecular biology · 2012Article
- CORECLUST: identification of the conserved CRM grammar together with prediction of gene regulation.Nucleic acids research · 2012Article
- The dependence of expression of NF-κB-dependent genes: statistics and evolutionary conservation of control sequences in the promoter and in the 3' UTR.BMC genomics · 2012Article
- Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices.Nucleic acids research · 2012Article
- Expression of brain-derived neurotrophic factor is regulated by the Wnt signaling pathway.Neuroreport · 2012Article
- Thousands of cis-regulatory sequence combinations are shared by Arabidopsis and poplar.Plant physiology · 2012Article
- Article
- Tissue-specific prediction of directly regulated genes.Bioinformatics (Oxford, England) · 2011Article
- Evolutionary divergence and limits of conserved non-coding sequence detection in plant genomes.Nucleic acids research · 2011Article
- Combinatorial regulation of transcription factors and microRNAs.BMC systems biology · 2010Article
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
The identification of cis-regulatory modules (CRMs) can greatly advance our understanding of eukaryotic regulatory mechanism. Current methods to predict CRMs from known motifs either depend on multiple alignments or can only deal with a small number of known motifs provided by users. These methods are problematic when binding sites are not well aligned in multiple alignments or when the number of input known motifs is large. We thus developed a new CRM identification method MOPAT (motif pair tree), which identifies CRMs through the identification of motif modules, groups of motifs co-occurring in multiple CRMs. It can identify 'orthologous' CRMs without multiple alignments. It can also find CRMs given a large number of known motifs. We have applied this method to mouse developmental genes, and have evaluated the predicted CRMs and motif modules by microarray expression data and known interacting motif pairs. We show that the expression profiles of the genes containing CRMs of the same motif module correlate significantly better than those of a random set of genes do. We also show that the known interacting motif pairs are significantly included in our predictions. Compared with several current methods, our method shows better performance in identifying meaningful CRMs.
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.