Human Pol II promoter recognition based on primary sequences and free energy of dinucleotides

Yang, Jianyi; Zhou, Yu; Yu, Zu-Guo; Anh, Vo; Zhou, Liqian

doi:10.1186/1471-2105-9-113

Cited by 25 publications

(28 citation statements)

References 52 publications

(78 reference statements)

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“…It has been useful in bacterial analysis to construct phylogenetic trees (Yu et al, 2003) and clustering of protein structures due to hydrophobic free energy and solvent accessibility of proteins (Yu et al, 2006). Recently, multifractal analysis has shown its utility in predicting the existence of promoters for RNA polymerase II (Yang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

The Caenorhabditis elegans genome: a multifractal analysis

Vélez

Garreta²,

Martínez³

et al. 2010

Genet. Mol. Res.

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ABSTRACT. The Caenorhabditis elegans genome has several regular and irregular characteristics in its nucleotide composition; these are observed within and between chromosomes. To study these particularities, we carried out a multifractal analysis, which requires a large number of exponents to characterize scaling properties. We looked for a relationship between the genetic information content of the chromosomes and multifractal parameters and found less multifractality compared to the human genome. Differences in multifractality among chromosomes and in regions of chromosomes, and two group averages of chromosome regions were observed. All these differences were mainly dependent on differences in the contents of repetitive DNA. Based on these properties, we propose a nonlinear model for the structure of the C. elegans genome, with some biological implications. These results suggest that examining differences in multifractality is a viable approach for measuring local variations of genomic information contents along chromosomes. This approach could be extended to other genomes in order to characterize structural and functional regions of chromosomes.

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Section: Introductionmentioning

confidence: 99%

The Caenorhabditis elegans genome: a multifractal analysis

Vélez

Garreta²,

Martínez³

et al. 2010

Genet. Mol. Res.

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“…Some works have used promoters and coding sequences, and promoters and non-coding sequences, respectively, to examine their approaches (Gordon et al 2003). Other researches have proposed a more realistic method that used promoters and non-promoters (coding sequences and non-coding sequences) to train their approaches (Ohler 2006;Yang et al 2008;Rani et al 2007). However, these works have not considered the difference between coding sequences and non-coding sequences.…”

Section: Resultsmentioning

confidence: 98%

“…Ten-fold cross-validated accuracy of 87% was obtained. Another work has described a fisher discriminant algorithm to predict H. sapiens promoters (Yang et al 2008). The overall accuracy of *89% was achieved.…”

Section: Comparison Accuraciesmentioning

confidence: 97%

“…Consequently, some on-line available tools, such as Eponine (Down and Hubbard 2002), Corepromoter (Zhang 2005), CpGProD (Ponger and Mouchiroud 2002), Promoter2.0 (Knudsen 1999), FirstEF (Davuluri et al 2001), promH , Promoter Scan (Prestridge 1995), Dragon promoter finder (Bajic et al 2002), NNPP2.2 (Reese 2001), McPromoter (Ohler 2006), ARTS (Sonnenburg et al 2006), ProSOM (Abeel et al 2008a, b), and RBF-TSS (Mahdi and Rouchka 2009) have been designed for the detection of promoters. Although contemporary methods have achieved great progress in promoter recognition, they were still limited in predictive performance (Anwar et al 2008;Abeel et al 2008a, b;Yang et al 2008). The performances of these methods are unreliable with poor specificity or poor sensitivity.…”

Section: Introductionmentioning

confidence: 97%

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Eukaryotic and prokaryotic promoter prediction using hybrid approach

Lin

2010

Theory Biosci.

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Promoters are modular DNA structures containing complex regulatory elements required for gene transcription initiation. Hence, the identification of promoters using machine learning approach is very important for improving genome annotation and understanding transcriptional regulation. In recent years, many methods have been proposed for the prediction of eukaryotic and prokaryotic promoters. However, the performances of these methods are still far from being satisfactory. In this article, we develop a hybrid approach (called IPMD) that combines position correlation score function and increment of diversity with modified Mahalanobis Discriminant to predict eukaryotic and prokaryotic promoters. By applying the proposed method to Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli, and Bacillus subtilis promoter sequences, we achieve the sensitivities and specificities of 90.6 and 97.4% for D. melanogaster, 88.1 and 94.1% for H. sapiens, 83.3 and 95.2% for C. elegans, 84.9 and 91.4% for E. coli, as well as 80.4 and 91.3% for B. subtilis. The high accuracies indicate that the IPMD is an efficient method for the identification of eukaryotic and prokaryotic promoters. This approach can also be extended to predict other species promoters.

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“…Owing to Z curve variables contain many forms from single to multi-nucleotides, a great deal of information can be reflected by it. This theory has been widely used in protein-coding gene recognition [24][25][26][27] , exon and intron recognition 28 , promoter recognition 29,30 , translation start recognition 31 and nucleosome position mapping 32 .…”

Section: Introductionmentioning

confidence: 99%

Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots

et al. 2016

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Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics. Inspired by the principle of Z curve theory, we improved PseDNC to give the phase-specific PseDNC (psPseDNC). In this study, we used the prediction of recombination spots as a case to illustrate the capability of psPseDNC and also PseDNC fused with Z curve theory based on a novel machine learning method named large margin distribution machine (LDM). We verified that combining the two widely used approaches could generate better performance compared to only using PseDNC with a support vector machine based (SVM-based) model. The best Mathew's correlation coefficient (MCC) achieved by our LDM-based model was 0.7037 through the rigorous jackknife test and improved by ∼6.6%, ∼3.2%, and ∼2.4% compared with three previous studies. Similarly, the accuracy was improved by 3.2% compared with our previous iRSpot-PseDNC web server through an independent data test. These results demonstrate that the joint use of PseDNC and Z curve enhances performance and can extract more information from a biological sequence. To facilitate research in this area, we constructed a user-friendly web server for predicting hot/cold spots, HcsPredictor, which can be freely accessed from . In summary, we provided a united algorithm by integrating Z curve with PseDNC. We hope this united algorithm could be extended to other classification issues in DNA elements.

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Human Pol II promoter recognition based on primary sequences and free energy of dinucleotides

Cited by 25 publications

References 52 publications

The Caenorhabditis elegans genome: a multifractal analysis

The Caenorhabditis elegans genome: a multifractal analysis

Eukaryotic and prokaryotic promoter prediction using hybrid approach

Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots

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