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

Abstract

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.

Numerical Characterization of Protein Sequences Based on the Generalized Chou’s Pseudo Amino Acid Composition

“…In literature, a set of metrics are often used to measure the prediction quality. To make it intuitive and easy to understand for readers, here we adopt the definition and notations used in [40,41,[56][57][58][59][60] to describe the corresponding evaluation metrics: N + . It should be pointed out that the set of metrics above is valid only for the single-label system (such as the case at hand).…”

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

“…As demonstrated by a series of recent publications (Chou 2011;Chen et al 2014;Ding et al 2014;Lin et al 2014;Xu et al 2014;Liu et al 2015) in response to the call (Chou 2011) to establish a really useful sequencebased statistical predictor for a biological system, we need to consider the following procedures: (a) construct or select a valid benchmark dataset to train and test the predictor; (b) formulate the biological sequence samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted; (c) introduce or develop a powerful algorithm (or engine) to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a userfriendly web server for the predictor that is accessible to the public. Below, let us describe how to address these steps one by one.…”

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

“…"Distance Pair" method incorporates the amino acid distance pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) [108] vector, which is very useful for analysing DNA-binding proteins [15,170,189,275]. PDT is the abbreviation for "physicochemical distance transformation", which can incorporate considerable sequence-order information or important patterns of protein/peptide sequences into Pseudo components [28], which is very useful for conducting various proteome analyses [17, 23, 215-217, 224, 225, 231, 235, 276-289] and genome analysis as well [216,218,220,223,229,255,277,290].…”