Research » WP 1.2 Host prediction

@article{nokey,

title = {Helicobacter pylori infection alters gut virome by expanding temperate phages linked to increased risk of colorectal cancer},

author = {Shiqi Luo and Jinlong Ru and Mohammadali Khan Mirzaei and Jinling Xue and Xue Peng and Anna Ralser and Joshua Lemuel Hadi and Raquel Mejías-Luque and Markus Gerhard and Li Deng},

doi = {10.1136/gutjnl-2023-330362},

year  = {2023},

date = {2023-11-02},

journal = {Gut},

pages = {gutjnl-2023-330362},

abstract = {No abstract available.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Gut virome profiling identifies an association between temperate phages and colorectal cancer promoted by Helicobacter pylori infection},

author = {Shiqi Luo and Jinlong Ru and Mohammadali Khan Mirzaei and Jinling Xue and Xue Peng and Anna Ralser and Raquel Mejías Luque and Markus Gerhard and Li Deng},

doi = {10.1080/19490976.2023.2257291},

year  = {2023},

date = {2023-09-25},

journal = {Gut Microbes},

volume = {15},

issue = {2},

pages = {2257291},

abstract = {Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. While a close correlation between chronic Helicobacter pylori infection and CRC has been reported, the role of the virome has been overlooked. Here, we infected Apc-mutant mouse models and C57BL/6 mice with H. pylori and conducted a comprehensive metagenomics analysis of H. pylori-induced changes in lower gastrointestinal tract bacterial and viral communities. We observed an expansion of temperate phages in H. pylori infected Apc+/1638N mice at the early stage of carcinogenesis. Some of the temperate phages were predicted to infect bacteria associated with CRC, including Enterococcus faecalis. We also observed a high prevalence of virulent genes, such as flgJ, cwlJ, and sleB, encoded by temperate phages. In addition, we identified phages associated with pre-onset and onset of H. pylori-promoted carcinogenesis. Through co-occurrence network analysis, we found strong associations between the viral and bacterial communities in infected mice before the onset of carcinogenesis. These findings suggest that the expansion of temperate phages, possibly caused by prophage induction triggered by H. pylori infection, may have contributed to the development of CRC in mice by interacting with the bacterial community.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Benchmarking Bioinformatic Virus Identification Tools Using Real-World Metagenomic Data across Biomes},

author = {Ling-Yi Wu and Nikolaos Pappas and Yasas Wijesekara and Gonçalo J. Piedade and Corina P.D. Brussaard and Bas E. Dutilh},

doi = {10.1101/2023.04.26.538077},

year  = {2023},

date = {2023-04-28},

journal = {bioRxiv},

abstract = {As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training/reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compared the performance of ten state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools had highly variable true positive rates (0 – 68%) and false positive rates (0 – 15%). PPR-Meta best distinguished viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identified different subsets of the benchmarking data and all tools, except for Sourmash, found unique viral contigs. Tools performance could be improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking provides guidance on choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments for viromics researchers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Prediction of virus-host association using protein language models and multiple instance learning},

author = {Dan Liu and Francesca Young and David L. Robertson and Ke Yuan},

doi = {10.1101/2023.04.07.536023},

year  = {2023},

date = {2023-04-08},

urldate = {2023-04-08},

journal = {bioRxiv},

abstract = {Predicting virus-host association is essential to understand how viruses interact with host species, and discovering new therapeutics for viral diseases across humans and animals. Currently, the host of the majority of viruses is unknown. Here, we introduce EvoMIL, a deep learning method that predicts virus-host association at the species level from viral sequence only. The method combines a pre-trained large protein language model and attention-based multiple instance learning to allow protein-orientated predictions. Our results show that protein embeddings capture stronger predictive signals than traditional handcrafted features, including amino acids and DNA k-mers, and physio-chemical properties. EvoMIL binary classifiers achieve AUC values of over 0.95 for all prokaryotic and nearly 0.8 for almost all eukaryotic hosts. In multi-host prediction tasks, EvoMIL achieved median performance improvements of 8.6% in prokaryotic hosts and 1.8% in eukaryotic hosts. Furthermore, EvoMIL estimates the importance of single proteins in the prediction and maps them to an embedding landscape of all viral proteins, where proteins with similar functions are distinctly clustered together.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {ViroProfiler: a containerized bioinformatics pipeline for viral metagenomic data analysis},

author = {Jinlong Ru and Mohammadali Khan Mirzaei and Jinling Xue and Xue Peng and Li Deng

},

doi = {10.1080/19490976.2023.2192522},

year  = {2023},

date = {2023-03-30},

journal = {Gut Microbes},

volume = {15},

issue = {1},

pages = {2192522},

abstract = {Bacteriophages play central roles in the maintenance and function of most ecosystems by regulating bacterial communities. Yet, our understanding of their diversity remains limited due to the lack of robust bioinformatics standards. Here we present ViroProfiler, an in-silico workflow for analyzing shotgun viral metagenomic data. ViroProfiler can be executed on a local Linux computer or cloud computing environments. It uses the containerization technique to ensure computational reproducibility and facilitate collaborative research. ViroProfiler is freely available at https://github.com/deng-lab/viroprofiler.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}