ClassiPhages 2.0: Sequence-based classification of phages using Artificial Neural Networks

Cm, Chibani; Meinecke, Frank C.; Farr, Anton; Dietrich, Sascha; Liesegang, Heiko

doi:10.1101/558171

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…Over the last few years, whole genome and proteome analysis has been applied as a basis for new taxonomic insights that may lead to a phylogeny‐based classification of phages (Chibani et al., 2019; Adriaenssens et al., 2020) and, potentially, of all viruses (Moreno‐Gallego and Reyes, 2021). However, although these new insights may open new perspectives for the future classification of phages, the QPS WG concluded that the actual taxonomic situation is still unsettled and not generally accepted.…”

Section: Assessmentmentioning

confidence: 99%

Update of the list of qualified presumption of safety (QPS) recommended microorganisms intentionally added to food or feed as notified to EFSA

Koutsoumanis¹,

Allende²,

Álvarez‐Ordóñez³

et al. 2023

EFS2

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The qualified presumption of safety (QPS) provides a generic pre-assessment of the safety of microorganisms intended for use in the food or feed chains, to support the work of EFSA's Scientific Panels. QPS assessment allows a fast track evaluation of strains belonging to QPS taxonomic units (TUs): species for bacteria, yeast, fungi, protists/microalgae and families for viruses. QPS TUs are assessed for their body of knowledge and safety. Safety concerns related to a QPS TU are reflected, when possible, as 'qualifications', which should be tested at strain and/or product level. Based on the possession of potentially harmful traits by some strains, filamentous fungi, bacteriophages, oomycetes, streptomycetes, Enterococcus faecium, Escherichia coli and Clostridium butyricum are excluded from the QPS assessment. Between October 2019 and September 2022, 323 notifications of TUs were received, 217 related to feed additives, 54 to food enzymes, food additives and flavourings, 14 to plant protection products and 38 to novel foods. The list of QPS-recommended TUs is reviewed every 6 months following an extensive literature search strategy. Only sporadic infections with a few QPS status TUs in immunosuppressed individuals were identified and the assessment did not change the QPS status of these TUs. The QPS list has been updated in relation to the most recent taxonomic insights and the qualifications were revised and streamlined. The qualification 'absence of aminoglycoside production ability' was withdrawn for Bacillus velezensis. Six new TUs received the QPS status: Bacillus paralicheniformis with the qualification 'absence of toxigenic activity' and 'absence of bacitracin production ability'; Bacillus circulans with the qualifications for 'production purposes only' and 'absence of cytotoxic activity'; Haematococcus lacustris (synonym Haematococcus pluvialis) and Ogataea polymorpha, both with the qualification 'for production purposes only'; Lactiplantibacillus argentoratensis; Geobacillus thermodenitrificans with the qualification 'absence of toxigenic activity'.

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Section: Assessmentmentioning

confidence: 99%

Update of the list of qualified presumption of safety (QPS) recommended microorganisms intentionally added to food or feed as notified to EFSA

Koutsoumanis¹,

Allende²,

Álvarez‐Ordóñez³

et al. 2023

EFS2

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“…The program and profile HMMs for the four phage families listed above is available for download (27). This work was extended to create ClassiPhage 2.0, an updated method that uses profile HMMs derived from phage sequences to train an Artificial Neural Network (ANN) to classify the phages into one of 12 different families (28). The generated models showed very high specificity for all families, but the observed sensitivity was very low, with only 3 out of the 12 families showing values above 50% and six families presenting sensitivity below 20%.…”

Section: Databases Of Viral Profile Hmmsmentioning

confidence: 99%

Rational Design of Profile Hidden Markov Models for Viral Classification and Discovery

Oliveira¹,

Gruber²

2021

Bioinformatics

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This chapter provides an overview of the theoretical concepts and practical applications of methods for the rational design and application of profile hidden Markov models (profile HMMs) in viral discovery and classification. Profile HMMs are probabilistic models that represent sequence diversity and constitute a very sensitive approach for detecting remote homologs. One of the most relevant and challenging applications of profile HMMs is the discovery of viruses in metagenomic samples, a fundamental task for epidemiological surveillance. In this chapter, publicly available resources of viral profile HMMs are presented and the methods involved in their construction are discussed. Several aspects to be considered for the generation of profile HMMs are presented, including technical pitfalls that should be avoided, and the potential applications of such models for detecting specific viral sequences. This chapter also introduces a bioinformatics application that implements methods to select informative regions of a multiple sequence alignment and build profile HMMs with different taxonomic specificities. Additional programs using profile HMMs for targeted sequence assembly and detection of multigene entities are also presented. Such programs, integrated into a common framework for viral research,

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“…Our goal was also to create a bioinformatic tool for predicting the host from the whole genome sequence, but we chose the machine-learning-algorithm approach. The use of machine learning algorithms has proved to be suitable for phage biology, as evidenced by their use in the search for phage virions [ 37 ], and improved phage genome annotation [ 38 ] as well as phage classification [ 39 , 40 ]. Our pipeline, PHERI, re-annotates phage genomes, and uses TRIBE-MCL for rapid and accurate clustering of annotated protein sequences [ 41 , 42 ] and a binary decision tree classifier to predict the phage host.…”

Section: Introductionmentioning

confidence: 99%

PHERI—Phage Host ExploRation Pipeline

et al. 2023

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Antibiotic resistance is becoming a common problem in medicine, food, and industry, with multidrug-resistant bacterial strains occurring in all regions. One of the possible future solutions is the use of bacteriophages. Phages are the most abundant form of life in the biosphere, so we can highly likely purify a specific phage against each target bacterium. The identification and consistent characterization of individual phages was a common form of phage work and included determining bacteriophages’ host-specificity. With the advent of new modern sequencing methods, there was a problem with the detailed characterization of phages in the environment identified by metagenome analysis. The solution to this problem may be to use a bioinformatic approach in the form of prediction software capable of determining a bacterial host based on the phage whole-genome sequence. The result of our research is the machine learning algorithm-based tool called PHERI. PHERI predicts the suitable bacterial host genus for the purification of individual viruses from different samples. In addition, it can identify and highlight protein sequences that are important for host selection.

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ClassiPhages 2.0: Sequence-based classification of phages using Artificial Neural Networks

Abstract: 11Background/ Motivation:

Cited by 7 publications

References 30 publications

Update of the list of qualified presumption of safety (QPS) recommended microorganisms intentionally added to food or feed as notified to EFSA

Update of the list of qualified presumption of safety (QPS) recommended microorganisms intentionally added to food or feed as notified to EFSA

Rational Design of Profile Hidden Markov Models for Viral Classification and Discovery

PHERI—Phage Host ExploRation Pipeline

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