Futuristic Methods in Virus Genome Evolution Using the Third-Generation DNA Sequencing and Artificial Neural Networks

Shim, Hyunjin

doi:10.1007/978-3-030-29022-1_17

“…Bacteriophages exhibit an extraordinary degree of genetic diversity. Unlike bacteria and archaea, for which reference genomes are relatively abundant, the lack of comprehensive reference databases for bacteriophages complicates the alignment and assembly processes during sequencing [55]. Bacteriophages exhibit rapid rates of evolution, leading to genomic changes that occur over short periods [56].…”

Section: Discussionmentioning

confidence: 99%

A pangenome analysis of ESKAPE bacteriophages: the underrepresentation may impact machine learning models

Lee,

Hunter,

Shim

2024

Preprint

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Bacteriophages are the most prevalent biological entities in the biosphere. However, limitations in both medical relevance and sequencing technologies have led to a systematic underestimation of the genetic diversity within phages. This underrepresentation not only creates a significant gap in our understanding of phage roles across diverse biosystems but also introduces biases in computational models reliant on these data for training and testing. In this study, we focused on publicly available genomes of bacteriophages infecting high-priority ESKAPE pathogens to show the extent and impact of this underrepresentation. First, we demonstrate a stark underrepresentation of ESKAPE phage genomes within the public genome and protein databases. Next, a pangenome analysis of these ESKAPE phages reveals extensive sharing of core genes among phages infecting the same host. Furthermore, genome analyses and clustering highlight close nucleotide-level relationships among the ESKAPE phages, raising concerns about the limited diversity within current public databases. Lastly, we uncover a scarcity of unique lytic phages and phage proteins with antimicrobial activities against ESKAPE pathogens. This comprehensive analysis of the ESKAPE phages underscores the severity of underrepresentation and its potential implications. This lack of diversity in phage genomes may restrict the resurgence of phage therapy and cause biased outcomes in data-driven computational models due to incomplete and unbalanced biological datasets.

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“…With the advent of next generation sequencing, predictions have become feasible or at least possible using complete genome sequence data [14]. Genomic data have been used to classify COVID-19 variants and other viruses using deep learning approaches [15][16][17].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Guided Prediction Modeling of Dengue Virus Evolving Serotype

Mumtaz,

Shahbaz,

Qureshi

et al. 2024

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Evolution remains an incessant process in viruses, allowing them to elude host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Predicting emerging viral genomes is crucial, particularly in diseases like dengue, where viruses disrupt host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging Dengue Virus (DENV) serotype. While Recurrent Neural Networks (RNN) were originally designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generative model showed accuracy of 93% and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score all to be around 99.00%, demonstrated the classification model’s reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99% similarity to DENV-4 and around 63-65% similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum 90% similarity underscored their uniqueness. We analyzed the conserved motifs in the genome through MEME Suite (version 5.5.5). Our research strives to contribute to the ongoing fight against the Dengue virus by offering predictive insights into its genomic evolution. Looking ahead, proactive predictive modeling before mutations occur holds potential for guiding vaccine design and diagnostic kit development.

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“…Bacteriophages exhibit an extraordinary degree of genetic diversity. Unlike bacteria and archaea, for which reference genomes are relatively abundant, the lack of comprehensive reference databases for bacteriophages complicates the alignment and assembly processes during sequencing (Shim, 2019a). Bacteriophages exhibit rapid rates of evolution, leading to genomic changes that occur over short periods (Shim, 2019b).…”

Section: Discussionmentioning

confidence: 99%

A pangenome analysis of ESKAPE bacteriophages: the underrepresentation may impact machine learning models

Lee,

Hunter,

Shim

2024

Front. Mol. Biosci.

0

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Bacteriophages are the most prevalent biological entities in the biosphere. However, limitations in both medical relevance and sequencing technologies have led to a systematic underestimation of the genetic diversity within phages. This underrepresentation not only creates a significant gap in our understanding of phage roles across diverse biosystems but also introduces biases in computational models reliant on these data for training and testing. In this study, we focused on publicly available genomes of bacteriophages infecting high-priority ESKAPE pathogens to show the extent and impact of this underrepresentation. First, we demonstrate a stark underrepresentation of ESKAPE phage genomes within the public genome and protein databases. Next, a pangenome analysis of these ESKAPE phages reveals extensive sharing of core genes among phages infecting the same host. Furthermore, genome analyses and clustering highlight close nucleotide-level relationships among the ESKAPE phages, raising concerns about the limited diversity within current public databases. Lastly, we uncover a scarcity of unique lytic phages and phage proteins with antimicrobial activities against ESKAPE pathogens. This comprehensive analysis of the ESKAPE phages underscores the severity of underrepresentation and its potential implications. This lack of diversity in phage genomes may restrict the resurgence of phage therapy and cause biased outcomes in data-driven computational models due to incomplete and unbalanced biological datasets.

show abstract

Futuristic Methods in Virus Genome Evolution Using the Third-Generation DNA Sequencing and Artificial Neural Networks

Cited by 5 publications

References 62 publications

A pangenome analysis of ESKAPE bacteriophages: the underrepresentation may impact machine learning models

A pangenome analysis of ESKAPE bacteriophages: the underrepresentation may impact machine learning models

Deep Learning Guided Prediction Modeling of Dengue Virus Evolving Serotype

A pangenome analysis of ESKAPE bacteriophages: the underrepresentation may impact machine learning models

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