OriC-ENS: A sequence-based ensemble classifier for predicting origin of replication in S. cerevisiae

Azim, Sayed Mehedi; Haque, Masudul; Shatabda, Swakkhar

doi:10.1016/j.compbiolchem.2021.107502

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Inspired by these earlier approaches, similar methods have been developed for predicting replication origins in eukaryotic genomes (e.g. Ori-Deep [22], iORI-Euk [4], OriC-ENS [1], Ori-Finder 3 [28]). However, due to the differences in replication mechanisms, such methods are unsuitable for prokaryotes [21].…”

Section: Introductionmentioning

confidence: 99%

ORCA: Predicting replication origins in circular prokaryotic chromosomes

van Meel,

Baaijens

2024

Preprint

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The proximity of genes to the origin of replication plays a key role in replication and transcription-related processes in bacteria. Computational prediction of potential origin locations has an important role in origin discovery, critically reducing experimental costs. We present ORCA (Origin of RepliCation Assessment) as a fast and lightweight tool for the visualisation of nucleotide disparities and the prediction of the location of replication origins. ORCA uses the analysis of nucleotide disparities, dnaA-box regions, and target gene positions to find potential origin sites, and has a random forest classifier to predict which of these sites are likely origins. ORCA's prediction and visualization capabilities make it a valuable in silico method to assist in experimental determination of replication origins. ORCA is written in Python-3.11, works on any operating system with minimal effort, and can process large databases. Full implementation details are provided in the supplementary material and the source code is freely available on GitHub: https://github.com/ZoyavanMeel/ORCA.

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Section: Introductionmentioning

confidence: 99%

ORCA: Predicting replication origins in circular prokaryotic chromosomes

van Meel,

Baaijens

2024

Preprint

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

Azim,

Sabab,

Noshadi

et al. 2023

Informatics in Medicine Unlocked

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A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation

Azim

Sharma

Noshadi

et al. 2022

Sci Rep

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AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bond. AMPylators catalyze this process as covalent attachment of adenosine monophosphate to the amino acid side chain of a peptide. Recent studies have shown that this post-translational modification is directly responsible for the regulation of neurodevelopment and neurodegeneration and is also involved in many physiological processes. Despite the importance of this post-translational modification, there is no peptide sequence dataset available for conducting computation analysis. Therefore, so far, no computational approach has been proposed for predicting AMPylation. In this study, we introduce a new dataset of this distinct post-translational modification and develop a new machine learning tool using a deep convolutional neural network called DeepAmp to predict AMPylation sites in proteins. DeepAmp achieves 77.7%, 79.1%, 76.8%, 0.55, and 0.85 in terms of Accuracy, Sensitivity, Specificity, Matthews Correlation Coefficient, and Area Under Curve for AMPylation site prediction task, respectively. As the first machine learning model, DeepAmp demonstrate promising results which highlight its potential to solve this problem. Our presented dataset and DeepAmp as a standalone predictor are publicly available at https://github.com/MehediAzim/DeepAmp.

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OriC-ENS: A sequence-based ensemble classifier for predicting origin of replication in S. cerevisiae

Cited by 4 publications

References 21 publications

ORCA: Predicting replication origins in circular prokaryotic chromosomes

ORCA: Predicting replication origins in circular prokaryotic chromosomes

Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation

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