2022
Peng, Xue; Ru, Jinlong; Mirzaei, Mohammadali Khan; Deng, Li
Replidec - Use naive Bayes classifier to identify virus lifecycle from metagenomics data Journal Article
In: bioRxiv, 2022.
Abstract | Links | BibTeX | Tags: Project 05, WP 1.2 Host prediction
@article{nokey,
title = {Replidec - Use naive Bayes classifier to identify virus lifecycle from metagenomics data},
author = {Xue Peng and Jinlong Ru and Mohammadali Khan Mirzaei and Li Deng
},
doi = {10.1101/2022.07.18.500415},
year = {2022},
date = {2022-07-19},
journal = {bioRxiv},
abstract = {Motivation: Viruses are the most abundant biological entities on earth. The majority of these entities are bacterial viruses or phages which specifically infect bacteria. Phages can use different replication strategies to invade their hosts including lytic, lysogenic, chronic cycle and pseudolysogeny. While the determination of the replication strategy used by phages is important to explore the phage-bacteria relationships in different ecosystems there are not many tools that can predict this in metagenomic data. In addition, most of the tools available can only predict lytic and lysogenic cycles. To address this issue, we have developed a new software called Replidec to identify three most common phage replication cycles (virulent, temperate, chronic) in viral sequences.
Results: Replidec uses Naive Bayes classifier combined with alignment-based methods to improve the prediction accuracy in metagenomic data. We test Replidec on viral genomes with known replication cycle and simulated metagenomic sequences. Replidec perform relatively good both in isolated genomes (F1 score: 92.29% ± 0.81; mcc: 89.14% ± 1.22) and simulated metagenomic sequences(F1 score: 87.55% ± 2.12; mcc: 88.23% ± 2.55). Moreover, Replidec can also accurately predict the replication cycle in small viral fragments(∼3000bp). In conclusion, Replidec can achieve the best performance in simulated metagenomic data compared to most prediction softwares including BACPHLIP.},
keywords = {Project 05, WP 1.2 Host prediction},
pubstate = {published},
tppubtype = {article}
}
Motivation: Viruses are the most abundant biological entities on earth. The majority of these entities are bacterial viruses or phages which specifically infect bacteria. Phages can use different replication strategies to invade their hosts including lytic, lysogenic, chronic cycle and pseudolysogeny. While the determination of the replication strategy used by phages is important to explore the phage-bacteria relationships in different ecosystems there are not many tools that can predict this in metagenomic data. In addition, most of the tools available can only predict lytic and lysogenic cycles. To address this issue, we have developed a new software called Replidec to identify three most common phage replication cycles (virulent, temperate, chronic) in viral sequences.
Results: Replidec uses Naive Bayes classifier combined with alignment-based methods to improve the prediction accuracy in metagenomic data. We test Replidec on viral genomes with known replication cycle and simulated metagenomic sequences. Replidec perform relatively good both in isolated genomes (F1 score: 92.29% ± 0.81; mcc: 89.14% ± 1.22) and simulated metagenomic sequences(F1 score: 87.55% ± 2.12; mcc: 88.23% ± 2.55). Moreover, Replidec can also accurately predict the replication cycle in small viral fragments(∼3000bp). In conclusion, Replidec can achieve the best performance in simulated metagenomic data compared to most prediction softwares including BACPHLIP.
Results: Replidec uses Naive Bayes classifier combined with alignment-based methods to improve the prediction accuracy in metagenomic data. We test Replidec on viral genomes with known replication cycle and simulated metagenomic sequences. Replidec perform relatively good both in isolated genomes (F1 score: 92.29% ± 0.81; mcc: 89.14% ± 1.22) and simulated metagenomic sequences(F1 score: 87.55% ± 2.12; mcc: 88.23% ± 2.55). Moreover, Replidec can also accurately predict the replication cycle in small viral fragments(∼3000bp). In conclusion, Replidec can achieve the best performance in simulated metagenomic data compared to most prediction softwares including BACPHLIP.
2021
Goettsch, Winfried; Beerenwinkel, Niko; Deng, Li; Dölken, Lars; Dutilh, Bas E.; Erhard, Florian; Kaderali, Lars; von Kleist, Max; Marquet, Roland; Matthijnssens, Jelle; McCallin, Shawna; McMahon, Dino; Rattei, Thomas; van Rij, Ronald P.; Robertson, David L.; Schwemmle, Martin; Stern-Ginossar, Noam; Marz, Manja
ITN -- VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics Journal Article
In: Viruses, vol. 13, no. 5, pp. 766, 2021.
Abstract | Links | BibTeX | Tags: Project 01, Project 02, Project 03, Project 04, Project 05, Project 06, Project 07, Project 08, Project 09, Project 10, Project 11, Project 12, Project 13, Project 14, Project 15, WP 1.1 Virus identification, WP 1.2 Host prediction, WP 1.3 Virus-host interactions, WP 1.4 Virus regulation, WP 1.5 Virus products, WP 2.1 Microevolution: Virus quasispecies, WP 2.2 Macroevolution: Natural selection of viruses
@article{nokey,
title = {ITN -- VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics},
author = {Winfried Goettsch and Niko Beerenwinkel and Li Deng and Lars Dölken and Bas E. Dutilh and Florian Erhard and Lars Kaderali and Max von Kleist and Roland Marquet and Jelle Matthijnssens and Shawna McCallin and Dino McMahon and Thomas Rattei and Ronald P. {van Rij} and David L. Robertson and Martin Schwemmle and Noam Stern-Ginossar and Manja Marz},
doi = {10.3390/v13050766},
year = {2021},
date = {2021-04-27},
urldate = {2021-04-27},
journal = {Viruses},
volume = {13},
number = {5},
pages = {766},
abstract = {Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.},
keywords = {Project 01, Project 02, Project 03, Project 04, Project 05, Project 06, Project 07, Project 08, Project 09, Project 10, Project 11, Project 12, Project 13, Project 14, Project 15, WP 1.1 Virus identification, WP 1.2 Host prediction, WP 1.3 Virus-host interactions, WP 1.4 Virus regulation, WP 1.5 Virus products, WP 2.1 Microevolution: Virus quasispecies, WP 2.2 Macroevolution: Natural selection of viruses},
pubstate = {published},
tppubtype = {article}
}
Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.