iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

By Hao Lin, En-Ze Deng, Hui Ding, Kuo-Chen Chou. Nucleic Acids Research, 2014

495 citations recorded by the source corpus.

The σ54 promoters are unique in prokaryotic genome and responsible for transcripting carbon and nitrogen-related genes. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the σ54 promoters. Here, a predictor called ‘iPro54-PseKNC’ was developed. In the predictor, the samples of DNA sequences were formulated by a novel feature vector called ‘pseudo k-tuple nucleotide composition’, which was further optimized by the incremental feature selection procedure. The performance of iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmark data set. As a user-friendly web-server, iPro54-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iPro54-PseKNC. For the convenience of the vast majority of experimental scientists, a step-by-step protocol guide was provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented in this paper just for its integrity. Meanwhile, we also discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the σ54 promoters.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6b4cc054facf43ca69c746f671e170e1d975b323

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Xuan Xiao
2. Kuo-Chen Chou
3. Jianhua Jia
4. Zi Liu
5. Bingxiang Liu
6. Hao Lin
7. Xin Gao
8. Hua Tang

Citations in the corpus

• BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches (2019, background): “Since the dimension of some feature extraction methods is tremendously high, which will result in highdimension disaster (57).”
• pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. (2016, methodology): “According to the 5-step rule [11] and demonstrated in a series of recent publications [15-22], to establish a really useful sequence-based statistical predictor for a biological system, we need to consider the following five procedures: (1) construct or select a valid benchmark dataset to train and ”
• Impacts of bioinformatics to medicinal chemistry. (2015, methodology): “The PseKNC approach has been successfully used to identify recombination spots [29,122], identifying splicing sites [123], predicting nucleosome positioning in genomes [124], predicting translation initiation site in human genes [125], and identifying sigma-54 promoters [126].”
• Identifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition (2019, methodology): “2 (b), it shows the heatmap of 61 features of PseKNC that represents the long-range or global-range sequence information.”