ArticlePloS one2017
Nucleotide patterns aiding in prediction of eukaryotic promoters.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Evolution and Spatiotemporal Expression ofJournal of developmental biology · 2024Article
- AtSNP_TATAdb: Candidate Molecular Markers of Plant Advantages Related to Single Nucleotide Polymorphisms within Proximal Promoters ofInternational journal of molecular sciences · 2024Article
- EEF1A1 transcription cofactor gene polymorphism is associated with muscle gene expression and residual feed intake in Nelore cattle.Mammalian genome : official journal of the International Mammalian Genome Society · 2022Article
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- Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.Briefings in bioinformatics · 2022Article
- Genome-Wide Prediction of Transcription Start Sites in Conifers.International journal of molecular sciences · 2022Article
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- In Silico Identification of the Complex Interplay between Regulatory SNPs, Transcription Factors, and Their Related Genes inInternational journal of molecular sciences · 2021Article
- Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.Methods in molecular biology (Clifton, N.J.) · 2021Article
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- Structural variants in 3000 rice genomes.Genome research · 2019Article
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- Sequence based prediction of enhancer regions from DNA random walk.Scientific reports · 2018Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational analysis of promoters is hindered by the complexity of their architecture. In less studied genomes with complex organization, false positive promoter predictions are common. Accurate identification of transcription start sites and core promoter regions remains an unsolved problem. In this paper, we present a comprehensive analysis of genomic features associated with promoters and show that probabilistic integrative algorithms-driven models allow accurate classification of DNA sequence into "promoters" and "non-promoters" even in absence of the full-length cDNA sequences. These models may be built upon the maps of the distributions of sequence polymorphisms, RNA sequencing reads on genomic DNA, methylated nucleotides, transcription factor binding sites, as well as relative frequencies of nucleotides and their combinations. Positional clustering of binding sites shows that the cells of Oryza sativa utilize three distinct classes of transcription factors: those that bind preferentially to the [-500,0] region (188 "promoter-specific" transcription factors), those that bind preferentially to the [0,500] region (282 "5' UTR-specific" TFs), and 207 of the "promiscuous" transcription factors with little or no location preference with respect to TSS. For the most informative motifs, their positional preferences are conserved between dicots and monocots.
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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 Socratic graph.