Eukaryotic promoter prediction tools




















Annu Rev Microbiol 54 — Local and global regulation of transcription initiation in bacteria. Nat Rev Microbiol 14 — Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat Biotechnol 30 — Systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria.

PLoS One 9 :e Genome-wide functional characterization of Escherichia coli promoters and regulatory elements responsible for their function. Nucleic Acids Res 44 — Sigma70 promoters in Escherichia coli: specific transcription in dense regions of overlapping promoter-like signals. J Mol Biol — Escherichia coli promoter sequences: analysis and prediction. Methods Enzymol — Sequence alignment kernel for recognition of promoter regions. Bioinformatics 19 — Improved prediction of bacterial transcription start sites.

Bioinformatics 22 — Nucleic Acids Res 35 :e BacPP: bacterial promoter prediction—a tool for accurate sigma-factor specific assignment in enterobacteria.

J Theor Biol — On DNA numerical representations for genomic similarity computation. PLoS One 12 :e Numerical representation of DNA sequences, p — Song K. Recognition of prokaryotic promoters based on a novel variable-window Z-curve method. Nucleic Acids Res 40 — Analysis of n-gram based promoter recognition methods and application to whole genome promoter prediction. In Silico Biol 9 :S1—S Enhanced regulatory sequence prediction using gapped k-mer features.

PLoS Comput Biol 10 :e Bioinformatics 33 — Machine learning applications in genetics and genomics. Nat Rev Genet 16 — Next-generation machine learning for biological networks.

Cell — A primer on deep learning in genomics. Nat Genet 51 — Noble WS. What is a support vector machine? Nat Biotechnol 24 — Krogh A. What are artificial neural networks?

Nat Biotechnol 26 — Logistic regression. Nat Methods 13 — Kingsford C, Salzberg SL. What are decision trees? Eddy SR. What is a hidden Markov model? Nat Biotechnol 22 — Optimized mixed Markov models for motif identification. BMC Bioinformatics 7 Solovyev V, Salamov A. Automatic annotation of microbial genomes and metagenomic sequences, p 61— In Li RW. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks. Image-based promoter prediction: a promoter prediction method based on evolutionarily generated patterns.

Sci Rep 8 Bioinformatics 21 — Nucleic Acids Res 37 :D61—D Mol Genet Genomics — BMC Syst Biol 12 Bioinformatics 34 — MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics 35 — Assessing the effects of data selection and representation on the development of reliable E.

PLoS One 10 :e Random sequences rapidly evolve into de novo promoters. Nat Commun 9 Bacterial promoter architecture: subsite structure of UP elements and interactions with the carboxy-terminal domain of the RNA polymerase alpha subunit. Genes Dev 13 — Typas A, Hengge R. Mol Microbiol 55 — Nucleic Acids Res 39 — Redefining fundamental concepts of transcription initiation in bacteria.

Nat Rev Genet doi Construction and model-based analysis of a promoter library for E. BMC Biotechnol 7 Predicting the strength of UP-elements and full-length E. Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. PLoS Comput Biol 8 :e Biochemistry 58 — The application of powerful promoters to enhance gene expression in industrial microorganisms.

Ioshikhes, I. Large-scale human promoter mapping using cpg islands. Juven-Gershon, T. The rna polymerase ii core promoter—the gateway to transcription. Cell Biol. Kanhere, A. A novel method for prokaryotic promoter prediction based on dna stability. BMC Bioinform. Kim, J. Evaluation of myc e-box phylogenetic footprints in glycolytic genes by chromatin immunoprecipitation assays. Kingma, D. Adam: a method for stochastic optimization. Knudsen, S. Bioinformatics 15, — Krizhevsky, A.

Pereira, C. Burges, L. Bottou, and K. Lander, E. M, Birren, B. Initial sequencing and analysis of the human genome. Nature , — LeCun, Y. Deep learning.

Lin, H. Identifying sigma70 promoters with novel pseudo nucleotide composition. Matsumine, H. A microdeletion of d6s in a family of autosomal recessive juvenile parkinsonism park2. Genomics 49, — Nazari, I. Branch point selection in rna splicing using deep learning.

IEEE Access. Ohler, U. Interpolated markov chains for eukaryotic promoter recognition. Oubounyt, M. Deep learning models based on distributed feature representations for alternative splicing prediction. IEEE Access 6, — The eukaryotic promoter database epd. Ponger, L. Cpgprod: identifying cpg islands associated with transcription start sites in large genomic mammalian sequences. Bioinformatics 18, — Prestridge, D.

Predicting pol ii promoter sequences using transcription factor binding sites. Qian, Y. Quang, D. Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of dna sequences. Reese, M. Application of a time-delay neural network to promoter annotation in the drosophila melanogaster genome. Scherf, M. Highly specific localization of promoter regions in large genomic sequences by promoterinspector: a novel context analysis approach1.

Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. Schuster, M. Bidirectional recurrent neural networks. IEEE Trans. Signal Process. Shi, W. Frequency distribution of tata box and extension sequences on human promoters. BMC Bioinformat. Smale, S. The rna polymerase ii core promoter. Szegedy, C. Tahir, M. Umarov, R. Promoter analysis and prediction in the human genome using sequence-based deep learning models. Bioinformatics bty Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks.

Wei, L. Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches.

EP3 is fast, it can make predictions for a whole genome animals, plants, etc. EP3 is accurate: it performs better than the current state-of-the-art promoter prediction programs on the human genome. EP3 requires no training is is applicable to all eukaryotic genomes.



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