ArticleBMC genomics2007
MetaProm: a neural network based meta-predictor for alternative human promoter prediction.
Article in BMC genomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 33 citations in OpenAlex.
- Exploring the Promoter Generation and Prediction ofInternational journal of molecular sciences · 2024Article
- G-quadruplex forming regions in GCK and TM6SF2 are targets for differential DNA methylation in metabolic disease and hepatocellular carcinoma patients.Scientific reports · 2024Article
- Alternative promoters in CpG depleted regions are prevalently associated with epigenetic misregulation of liver cancer transcriptomes.Nature communications · 2023Article
- Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.Briefings in bioinformatics · 2022Article
- Temporal Control of the WNT Signaling Pathway During Cardiac Differentiation Impacts Upon the Maturation State of Human Pluripotent Stem Cell Derived Cardiomyocytes.Frontiers in molecular biosciences · 2022Article
- Identification of Cooperative Gene Regulation Among Transcription Factors, LncRNAs, and MicroRNAs in Diabetic Nephropathy Progression.Frontiers in genetics · 2020Article
- SEASTAR: systematic evaluation of alternative transcription start sites in RNA.Nucleic acids research · 2018Article
- Article
- Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network.PloS one · 2013Article
- ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor.Nucleic acids research · 2011Article
- Next generation sequencing has lower sequence coverage and poorer SNP-detection capability in the regulatory regions.Scientific reports · 2011Article
- Ensemble approach combining multiple methods improves human transcription start site prediction.BMC genomics · 2010Article
- A transcription factor affinity-based code for mammalian transcription initiation.Genome research · 2009Article
- Genome-wide analysis of alternative promoters of human genes using a custom promoter tiling array.BMC genomics · 2008Article
- ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles.Bioinformatics (Oxford, England) · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDe novo eukaryotic promoter prediction is important for discovering novel genes and understanding gene regulation. In spite of the great advances made in the past decade, recent studies revealed that the overall performances of the current promoter prediction programs (PPPs) are still poor, and predictions made by individual PPPs do not overlap each other. Furthermore, most PPPs are trained and tested on the most-upstream promoters; their performances on alternative promoters have not been assessed.
resultsIn this paper, we evaluate the performances of current major promoter prediction programs (i.e., PSPA, FirstEF, McPromoter, DragonGSF, DragonPF, and FProm) using 42,536 distinct human gene promoters on a genome-wide scale, and with emphasis on alternative promoters. We describe an artificial neural network (ANN) based meta-predictor program that integrates predictions from the current PPPs and the predicted promoters' relation to CpG islands. Our specific analysis of recently discovered alternative promoters reveals that although only 41% of the 3' most promoters overlap a CpG island, 74% of 5' most promoters overlap a CpG island.
conclusionOur assessment of six PPPs on 1.06 x 109 bps of human genome sequence reveals the specific strengths and weaknesses of individual PPPs. Our meta-predictor outperforms any individual PPP in sensitivity and specificity. Furthermore, we discovered that the 5' alternative promoters are more likely to be associated with a CpG island.
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.