ABSTRACT
The sigma (σ) factor of RNA holoenzymes is essential for identifying and binding to promoter regions during gene transcription in prokaryotes. σ54 promoters carried out various ancillary methods and environmentally responsive procedures; therefore, it is crucial to accurately identify σ54 promoter sequences to comprehend the underlying process of gene regulation. Herein, we come up with a convolutional neural network (CNN) based prediction tool named "iProm-Sigma54" for the prediction of σ54 promoters. The CNN consists of two one-dimensional convolutional layers, which are followed by max pooling layers and dropout layers. A one-hot encoding scheme was used to extract the input matrix. To determine the prediction performance of iProm-Sigma54, we employed four assessment metrics and five-fold cross-validation; performance was measured using a benchmark and test dataset. According to the findings of this comparison, iProm-Sigma54 outperformed existing methodologies for identifying σ54 promoters. Additionally, a publicly accessible web server was constructed.
Subject(s)
DNA-Directed RNA Polymerases , Sigma Factor , RNA Polymerase Sigma 54/genetics , RNA Polymerase Sigma 54/metabolism , DNA-Directed RNA Polymerases/metabolism , Sigma Factor/genetics , Sigma Factor/metabolism , Promoter Regions, Genetic/genetics , Neural Networks, ComputerABSTRACT
The capability of field-programmed separation in frit inlet asymmetrical flow field-flow fractionation (FI-AFlFFF) has been examined for separating a high molecular weight sodium hyaluronate (NaHA) by varying the field programming parameters. Experiments were performed with on-line coupling of the field programming FI-AFlFFF and multiangle light scattering (MALS) detection. Sample relaxation, a pre-requisite step to establish equilibrium states of sample materials prior to the beginning of separation in most forms of FFF techniques, is obtained by hydrodynamically in FI-AFlFFF without stopping the migration flow. Thus, the procedures of sample injection -- hydrodynamic relaxation -- separation in FI-AFlFFF are continuously achieved without halting the sample migration. In this study, field programming in FI-AFlFFF was investigated for the separation of NaHA, water-soluble polysaccharides, by examining the influence of field decay pattern, initial field strength condition, and ionic strength of carrier solution on the successful separation of a degraded NaHA sample. Results were compared with molecular weight calculations of eluting materials among different field programming conditions from multiangle light scattering (MALS) signals. It was found that when the field programming was utilized in FI-AFlFFF, a proper selection of initial cross-flow rate, the field decay pattern, and an appropriate control of final field strength needed to be carefully selected in achieving a successful separation of a broad molecular weight water-soluble polymer sample.
