Dataset Information

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.

ABSTRACT: BACKGROUND:Promoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, ?70 promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It's also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification of ?70 promoter regions in DNA sequence is not easy. RESULTS:In order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNPSS combined with 64 features of PseEIIP obtained the best performance for sigma70 promoter identification, with a promising accuracy and the Matthews correlation coefficient (MCC) at 95.56% and 0.90, respectively. CONCLUSION:The jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/ .

SUBMITTER: He W

PROVIDER: S-EPMC5998878 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.

He Wenying W Jia Cangzhi C Duan Yucong Y Zou Quan Q

BMC systems biology 20180424 Suppl 4

<h4>Background</h4>Promoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ<sup>70</sup> promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It's also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification o ...[more]

PMID: 29745856

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Project description:BACKGROUND: In prokaryotes, sigma factors are essential for directing the transcription machinery towards promoters. Various sigma factors have been described that recognize, and bind to specific DNA sequence motifs in promoter sequences. The canonical sigma factor ?(70) is commonly involved in transcription of the cell's housekeeping genes, which is mediated by the conserved ?(70) promoter sequence motifs. In this study the ?(70)-promoter sequences in Lactobacillus plantarum WCFS1 were predicted using a genome-wide analysis. The accuracy of the transcriptionally-active part of this promoter prediction was subsequently evaluated by correlating locations of predicted promoters with transcription start sites inferred from the 5'-ends of transcripts detected by high-resolution tiling array transcriptome datasets. RESULTS: To identify ?(70)-related promoter sequences, we performed a genome-wide sequence motif scan of the L. plantarum WCFS1 genome focussing on the regions upstream of protein-encoding genes. We obtained several highly conserved motifs including those resembling the conserved ?(70)-promoter consensus. Position weight matrices-based models of the recovered ?(70)-promoter sequence motif were employed to identify 3874 motifs with significant similarity (p-value<10(-4)) to the model-motif in the L. plantarum genome. Genome-wide transcript information deduced from whole genome tiling-array transcriptome datasets, was used to infer transcription start sites (TSSs) from the 5'-end of transcripts. By this procedure, 1167 putative TSSs were identified that were used to corroborate the transcriptionally active fraction of these predicted promoters. In total, 568 predicted promoters were found in proximity (? 40 nucleotides) of the putative TSSs, showing a highly significant co-occurrence of predicted promoter and TSS (p-value<10(-263)). CONCLUSIONS: High-resolution tiling arrays provide a suitable source to infer TSSs at a genome-wide level, and allow experimental verification of in silico predicted promoter sequence motifs.

Project description:BackgroundIdentification of transcription factor binding sites (TFBSs) is a central problem in Bioinformatics on gene regulation. de novo motif discovery serves as a promising way to predict and better understand TFBSs for biological verifications. Real TFBSs of a motif may vary in their widths and their conservation degrees within a certain range. Deciding a single motif width by existing models may be biased and misleading. Additionally, multiple, possibly overlapping, candidate motifs are desired and necessary for biological verification in practice. However, current techniques either prohibit overlapping TFBSs or lack explicit control of different motifs.ResultsWe propose a new generalized model to tackle the motif widths by considering and evaluating a width range of interest simultaneously, which should better address the width uncertainty. Moreover, a meta-convergence framework for genetic algorithms (GAs), is proposed to provide multiple overlapping optimal motifs simultaneously in an effective and flexible way. Users can easily specify the difference amongst expected motif kinds via similarity test. Incorporating Genetic Algorithm with Local Filtering (GALF) for searching, the new GALF-G (G for generalized) algorithm is proposed based on the generalized model and meta-convergence framework.ConclusionGALF-G was tested extensively on over 970 synthetic, real and benchmark datasets, and is usually better than the state-of-the-art methods. The range model shows an increase in sensitivity compared with the single-width ones, while providing competitive precisions on the E. coli benchmark. Effectiveness can be maintained even using a very small population, exhibiting very competitive efficiency. In discovering multiple overlapping motifs in a real liver-specific dataset, GALF-G outperforms MEME by up to 73% in overall F-scores. GALF-G also helps to discover an additional motif which has probably not been annotated in the dataset. http://www.cse.cuhk.edu.hk/%7Etmchan/GALFG/

Dataset Information

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.

Publications

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.

Similar Datasets

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