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PhageScanner: a reconfigurable machine learning framework for bacteriophage genomic and metagenomic feature annotation.

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Title: PhageScanner: a reconfigurable machine learning framework for bacteriophage genomic and metagenomic feature annotation.
Authors: Albin, Dreycey, Ramsahoye, Michelle, Kochavi, Eitan, Alistar, Mirela
Source: Frontiers in Microbiology; 10/1/2024, p1-17, 17p
Subject Terms: MACHINE learning, GRAPHICAL user interfaces, CYTOSKELETAL proteins, DEEP learning, AMINO acid sequence
Abstract: Bacteriophages are the most prolific organisms on Earth, yet many of their genomes and assemblies from metagenomic sources lack protein sequences with identified functions. While most bacteriophage proteins are structural proteins, categorized as Phage Virion Proteins (PVPs), a considerable number remain unclassified. Complicatingmatters further, traditional lab-basedmethods for PVP identification can be tedious. To expedite the process of identifying PVPs, machine-learning models are increasingly being employed. Existing tools have developed models for predicting PVPs from protein sequences as input. However, none of these efforts have built software allowing for both genomic and metagenomic data as input. In addition, there is currently no framework available for easily curating data and creating new types of machine learning models. In response, we introduce PhageScanner, an open-source platform that streamlines data collection for genomic and metagenomic datasets, model training and testing, and includes a prediction pipeline for annotating genomic and metagenomic data. PhageScanner also features a graphical user interface (GUI) for visualizing annotations on genomic and metagenomic data. We further introduce a BLAST-based classifier that outperforms ML-based models and an efficient Long Short-Term Memory (LSTM) classifier. We then showcase the capabilities of PhageScanner by predicting PVPs in six previously uncharacterized bacteriophage genomes. In addition, we create a new model that predicts phage-encoded toxins within bacteriophage genomes, thus displaying the utility of the framework. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Microbiology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Biomedical Index
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  Data: PhageScanner: a reconfigurable machine learning framework for bacteriophage genomic and metagenomic feature annotation.
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  Data: Frontiers in Microbiology; 10/1/2024, p1-17, 17p
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  Data: <searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22GRAPHICAL+user+interfaces%22">GRAPHICAL user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22CYTOSKELETAL+proteins%22">CYTOSKELETAL proteins</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22AMINO+acid+sequence%22">AMINO acid sequence</searchLink>
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  Data: Bacteriophages are the most prolific organisms on Earth, yet many of their genomes and assemblies from metagenomic sources lack protein sequences with identified functions. While most bacteriophage proteins are structural proteins, categorized as Phage Virion Proteins (PVPs), a considerable number remain unclassified. Complicatingmatters further, traditional lab-basedmethods for PVP identification can be tedious. To expedite the process of identifying PVPs, machine-learning models are increasingly being employed. Existing tools have developed models for predicting PVPs from protein sequences as input. However, none of these efforts have built software allowing for both genomic and metagenomic data as input. In addition, there is currently no framework available for easily curating data and creating new types of machine learning models. In response, we introduce PhageScanner, an open-source platform that streamlines data collection for genomic and metagenomic datasets, model training and testing, and includes a prediction pipeline for annotating genomic and metagenomic data. PhageScanner also features a graphical user interface (GUI) for visualizing annotations on genomic and metagenomic data. We further introduce a BLAST-based classifier that outperforms ML-based models and an efficient Long Short-Term Memory (LSTM) classifier. We then showcase the capabilities of PhageScanner by predicting PVPs in six previously uncharacterized bacteriophage genomes. In addition, we create a new model that predicts phage-encoded toxins within bacteriophage genomes, thus displaying the utility of the framework. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Frontiers in Microbiology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3389/fmicb.2024.1446097
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      – Code: eng
        Text: English
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        Type: general
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      – SubjectFull: CYTOSKELETAL proteins
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      – SubjectFull: AMINO acid sequence
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      – TitleFull: PhageScanner: a reconfigurable machine learning framework for bacteriophage genomic and metagenomic feature annotation.
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            – D: 01
              M: 10
              Text: 10/1/2024
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              Y: 2024
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