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

Kim, Jeehong; Shujaat, Muhammad; Tayara, Hilal

doi:10.1016/j.ygeno.2022.110384

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…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:

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Section: Feature Encoding Schemementioning

confidence: 99%

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

Shujaat

Kim

Tayara

et al. 2023

Cells

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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.

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Section: Feature Encoding Schemementioning

confidence: 99%

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

Shujaat

Kim

Tayara

et al. 2023

Cells

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“…One-hot encoding techniques are used by many state-ofthe-art bioinformatics tools (Umarov and Solovyev, 2017;Liu and Li, 2019;Shujaat et al, 2021;Kim et al, 2022). Each nucleotide in a DNA sequence is represented by a four-dimensional vector, which is a vector of zeros with a single one.…”

Section: One-hot Feature Encodingmentioning

confidence: 99%

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

et al. 2022

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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/.

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“…In silico identification of promoter and enhancer sequences using DL was attempted in several studies. Most tools, like DeePromoter ( Oubounyt et al 2019 ), Cr-Prom ( Shujaat et al 2021 ), Depicter ( Zhu et al 2021 ), HPMI ( Wang et al 2022 ), or iProm-Zea ( Kim et al 2022 ), predicted promoters as a sequence stretch around the TSS. The networks in these studies performed a fundamentally different predictive task than actual promoter sequence prediction.…”

Section: Introductionmentioning

confidence: 99%

Predmoter—cross-species prediction of plant promoter and enhancer regions

Kindel,

Triesch,

Schlüter

et al. 2024

Bioinformatics Advances

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Motivation Identifying cis-regulatory elements (CREs) is crucial for analyzing gene regulatory networks. Next generation sequencing methods were developed to identify CREs but represent a considerable expenditure for targeted analysis of few genomic loci. Thus, predicting the outputs of these methods would significantly cut costs and time investment. Results We present Predmoter, a deep neural network that predicts base-wise Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) and histone Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) read coverage for plant genomes. Predmoter uses only the DNA sequence as input. We trained our final model on 21 species for 13 of which ATAC-seq data and for 17 of which ChIP-seq data was publicly available. We evaluated our models on Arabidopsis thaliana and Oryza sativa. Our best models showed accurate predictions in peak position and pattern for ATAC- and histone ChIP-seq. Annotating putatively accessible chromatin regions provides valuable input for the identification of CREs. In conjunction with other in silico data, this can significantly reduce the search space for experimentally verifiable DNA-protein interaction pairs. Availability The source code for Predmoter is available at: https://github.com/weberlab-hhu/Predmoter. Predmoter takes a fasta file as input and outputs h5, and optionally bigWig and bedGraph files. Supplementary information Supplementary data are available at Bioinformatics Advances online.

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

Cited by 7 publications

References 34 publications

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

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

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

Predmoter—cross-species prediction of plant promoter and enhancer regions

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