iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network

Abstract

The increased interest in phages as antibacterial agents has resulted in a rise in the number of sequenced phage genomes, necessitating the development of user-friendly bioinformatics tools for genome annotation. A promoter is a DNA sequence that is used in the annotation of phage genomes. In this study we proposed a two layer model called “iProm-phage” for the prediction and classification of phage promoters. Model first layer identify query sequence as promoter or non-promoter and if the query sequence is predicted as promoter then model second layer classify it as phage or host promoter. Furthermore, rather than using non-coding regions of the genome as a negative set, we created a more challenging negative dataset using promoter sequences. The presented approach improves discrimination while decreasing the frequency of erroneous positive predictions. For feature selection, we investigated 10 distinct feature encoding approaches and utilized them with several machine-learning algorithms and a 1-D convolutional neural network model. We discovered that the one-hot encoding approach and the CNN model outperformed based on performance metrics. Based on the results of the 5-fold cross validation, the proposed predictor has a high potential. Furthermore, to make it easier for other experimental scientists to obtain the results they require, we set up a freely accessible and user-friendly web server at http://nsclbio.jbnu.ac.kr/tools/iProm-phage/.

Expanding the RNA polymerase biocatalyst solution space for mRNA manufacture

“…Existing promoter prediction algorithms are primarily trained based on known promoter sequences, and their predictive ability is accordingly limited for novel test enzymes with significantly different sequences and structures. [ 24,35 ] Indeed, given the paucity of characterized RNAP promoters, novel “test” enzymes may recognize sequence motifs and architectures that are (i) substantially different to those used to train/design current algorithms, and accordingly (ii) beyond the predictive capabilities of available tools. Rationalizing that divergence in promoter structure/sequence would be underpinned by differences at the amino acid level, we investigated whether enzyme inactivity was associated with DNA binding domain sequences that varied significantly to those of well‐studied biocatalysts.…”

“…Existing promoter prediction algorithms are primarily trained based on known promoter sequences, and their predictive ability is accordingly limited for novel test enzymes with significantly different sequences and structures. [24,35] Indeed, given the paucity of characterized RNAP promoters, novel "test"…”

Underexplored viral auxiliary metabolic genes in soil: Diversity and eco‐evolutionary significance

“…AMG flags are then assigned, as described by Shaffer et al (2020) to highlight the metabolic potential of the genes and qualify confidence in the genes being viral. In addition to probability scoring cut-offs, promoter and terminator recognition are also helpful for determining the viral origin of potential AMGs (Shujaat et al, 2022). After identifying the AMGs using the methods described above, there is still the possibility of erroneous functional annotation based solely on sequence similarity searches.…”

iProm-Sigma54: A CNN Base Prediction Tool for σ54 Promoters

“…Thus, this study utilized a one-hot feature encoding scheme. Several recent cutting edge bioinformatics techniques have used this technique e [ 40 , 41 , 42 , 43 ]. Representation of each nucleotide for A , C , G , and T , characterized as follows: …”