2025
Botero, Juliana; Basler, Nikolas; Cnockaert, Margo; Peeters, Charlotte; Schreiber, Maria; Marz, Manja; de Graaf, Dirk C.; Matthijnssens, Jelle; Vandamme, Peter
Identification and functional genomic analyses of Bartonella isolates from honey bees, and reassessment of the taxonomy of the genus Bartonella Journal Article
In: Systematic and Applied Microbiology, vol. 48, 2025, ISBN: 0723-2020.
Abstract | Links | BibTeX | Tags: bacteria, classification, DNA / genomics, phylogenetics
@article{nokey_78,
title = {Identification and functional genomic analyses of Bartonella isolates from honey bees, and reassessment of the taxonomy of the genus Bartonella},
author = {Juliana Botero and Nikolas Basler and Margo Cnockaert and Charlotte Peeters and Maria Schreiber and Manja Marz and Dirk C. {de Graaf} and Jelle Matthijnssens and Peter Vandamme},
doi = {10.1016/j.syapm.2025.126625},
isbn = {0723-2020},
year = {2025},
date = {2025-06-06},
journal = {Systematic and Applied Microbiology},
volume = {48},
abstract = {We used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and whole-genome sequence analyses to identify 90 Bartonella isolates from honey bee gut samples in Belgium. While the identification of 62 isolates as Bartonella apihabitans and three as Bartonella choladocola was straightforward, the identification of 25 Bartonella apis-like isolates was challenging. A taxonomic and functional analysis of four B. apis-like genomes and of publicly available B. apis genomes demonstrated that neither OrthoANIu and digital DNA-DNA hybridization analyses, nor functional annotation supported a clear separation of B. apis and B. apis-like genomes. Different phylogenomic analyses showed that B. apis and B. apis-like strains formed a monophyletic clade with an inconsistent internal structure. We therefore considered the remaining 25 isolates identified as B. apis. We subsequently re-addressed an earlier phylogenetic and functional divergence between three major clades of Bartonella species which differed not only in phylogenomic position and ecology, but also in genome size and genomic percentage G + C content, and in many metabolic capabilities. We propose to reclassify the single species of the Bartonella tamiae clade into the novel genus Attibartonella gen. nov., with Attibartonella tamiae comb. nov. as the type species. Similarly, we propose to reclassify species of the honey bee-associated Bartonella clade into the novel genus Ditibartonella gen. nov., with Ditibartonella apis comb. nov. as the type species. The phylogenomic analyses of publicly available genome and metagenome sequences revealed additional Ditibartonella species in honey bee samples, highlighted an evolutionary adaptation of Ditibartonella bacteria to bee hosts and suggested shared transmission routes.},
keywords = {bacteria, classification, DNA / genomics, phylogenetics},
pubstate = {published},
tppubtype = {article}
}
We used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and whole-genome sequence analyses to identify 90 Bartonella isolates from honey bee gut samples in Belgium. While the identification of 62 isolates as Bartonella apihabitans and three as Bartonella choladocola was straightforward, the identification of 25 Bartonella apis-like isolates was challenging. A taxonomic and functional analysis of four B. apis-like genomes and of publicly available B. apis genomes demonstrated that neither OrthoANIu and digital DNA-DNA hybridization analyses, nor functional annotation supported a clear separation of B. apis and B. apis-like genomes. Different phylogenomic analyses showed that B. apis and B. apis-like strains formed a monophyletic clade with an inconsistent internal structure. We therefore considered the remaining 25 isolates identified as B. apis. We subsequently re-addressed an earlier phylogenetic and functional divergence between three major clades of Bartonella species which differed not only in phylogenomic position and ecology, but also in genome size and genomic percentage G + C content, and in many metabolic capabilities. We propose to reclassify the single species of the Bartonella tamiae clade into the novel genus Attibartonella gen. nov., with Attibartonella tamiae comb. nov. as the type species. Similarly, we propose to reclassify species of the honey bee-associated Bartonella clade into the novel genus Ditibartonella gen. nov., with Ditibartonella apis comb. nov. as the type species. The phylogenomic analyses of publicly available genome and metagenome sequences revealed additional Ditibartonella species in honey bee samples, highlighted an evolutionary adaptation of Ditibartonella bacteria to bee hosts and suggested shared transmission routes.
2024
Osadare, Ibukun Elizabeth; Monecke, Stefan; Abdilahi, Abdinasir; Müller, Elke; Collatz, Maximilian; Braun, Sascha; Reissig, Annett; Schneider-Brachert, Wulf; Kieninger, Bärbel; Eichner, Anja; Rath, Anca; Fritsch, Jürgen; Gary, Dominik; Frankenfeld, Katrin; Wellhöfer, Thomas; Ehricht, Ralf
In: Sensors, vol. 24, 2024.
Abstract | Links | BibTeX | Tags: bacteria, classification, DNA / genomics
@article{nokey_94,
title = {Fast and Economic Microarray-Based Detection of Species-, Resistance-, and Virulence-Associated Genes in Clinical Strains of Vancomycin-Resistant Enterococci (VRE)},
author = {Ibukun Elizabeth Osadare and Stefan Monecke and Abdinasir Abdilahi and Elke Müller and Maximilian Collatz and Sascha Braun and Annett Reissig and Wulf Schneider-Brachert and Bärbel Kieninger and Anja Eichner and Anca Rath and Jürgen Fritsch and Dominik Gary and Katrin Frankenfeld and Thomas Wellhöfer and Ralf Ehricht},
doi = {10.3390/s24196476},
year = {2024},
date = {2024-10-08},
journal = {Sensors},
volume = {24},
abstract = {Today, there is a continuous worldwide battle against antimicrobial resistance (AMR) and that includes vancomycin-resistant enterococci (VRE). Methods that can adequately and quickly detect transmission chains in outbreaks are needed to trace and manage this problem fast and cost-effectively. In this study, DNA-microarray-based technology was developed for this purpose. It commenced with the bioinformatic design of specific oligonucleotide sequences to obtain amplification primers and hybridization probes. Microarrays were manufactured using these synthesized oligonucleotides. A highly parallel and stringent labeling and hybridization protocol was developed and employed using isolated genomic DNA from previously sequenced (referenced) clinical VRE strains for optimal sensitivity and specificity. Microarray results showed the detection of virulence, resistance, and species-specific genes in the VRE strains. Theoretical predictions of the microarray results were also derived from the sequences of the same VRE strain and were compared to array results while optimizing protocols until the microarray result and theoretical predictions were a match. The study concludes that DNA microarray technology can be used to quickly, accurately, and economically detect specifically and massively parallel target genes in enterococci.},
keywords = {bacteria, classification, DNA / genomics},
pubstate = {published},
tppubtype = {article}
}
Today, there is a continuous worldwide battle against antimicrobial resistance (AMR) and that includes vancomycin-resistant enterococci (VRE). Methods that can adequately and quickly detect transmission chains in outbreaks are needed to trace and manage this problem fast and cost-effectively. In this study, DNA-microarray-based technology was developed for this purpose. It commenced with the bioinformatic design of specific oligonucleotide sequences to obtain amplification primers and hybridization probes. Microarrays were manufactured using these synthesized oligonucleotides. A highly parallel and stringent labeling and hybridization protocol was developed and employed using isolated genomic DNA from previously sequenced (referenced) clinical VRE strains for optimal sensitivity and specificity. Microarray results showed the detection of virulence, resistance, and species-specific genes in the VRE strains. Theoretical predictions of the microarray results were also derived from the sequences of the same VRE strain and were compared to array results while optimizing protocols until the microarray result and theoretical predictions were a match. The study concludes that DNA microarray technology can be used to quickly, accurately, and economically detect specifically and massively parallel target genes in enterococci.
2023
Rangel-Pineros, Guillermo; Almeida, Alexandre; Beracochea, Martin; Sakharova, Ekaterina; Marz, Manja; Muñoz, Alejandro Reyes; Hölzer, Martin; Finn, Robert D.
VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models Journal Article
In: PLOS Comput Biol, vol. 19, iss. 8, pp. e1011422, 2023.
Abstract | Links | BibTeX | Tags: annotation, classification, metagenomics, phylogenetics, software, viruses
@article{nokey,
title = {VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models},
author = {Guillermo Rangel-Pineros and Alexandre Almeida and Martin Beracochea and Ekaterina Sakharova and Manja Marz and Alejandro Reyes Muñoz and Martin Hölzer and Robert D. Finn },
doi = {10.1371/journal.pcbi.1011422},
year = {2023},
date = {2023-08-28},
journal = {PLOS Comput Biol},
volume = {19},
issue = {8},
pages = {e1011422},
abstract = {The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterisation of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterisation of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.},
keywords = {annotation, classification, metagenomics, phylogenetics, software, viruses},
pubstate = {published},
tppubtype = {article}
}
The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterisation of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterisation of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.
2022
Mock, Florian
Context sensitive neural networks for the classification of DNA, RNA and protein sequences PhD Thesis
2022.
Links | BibTeX | Tags: classification, machine learning
@phdthesis{nokey_37,
title = {Context sensitive neural networks for the classification of DNA, RNA and protein sequences},
author = {Florian Mock},
url = {https://suche.thulb.uni-jena.de/Record/1820176673},
year = {2022},
date = {2022-09-05},
howpublished = {Friedrich-Schiller-Universität Jena},
keywords = {classification, machine learning},
pubstate = {published},
tppubtype = {phdthesis}
}
Mock, Florian; Kretschmer, Fleming; Kriese, Anton; Böcker, Sebastian; Marz, Manja
Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks Journal Article
In: Proc Natl Acad Sci, vol. 119, iss. 35, pp. e2122636119, 2022.
Abstract | Links | BibTeX | Tags: classification, DNA / genomics, machine learning
@article{Mock2022,
title = {Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks},
author = {Florian Mock and Fleming Kretschmer and Anton Kriese and Sebastian Böcker and Manja Marz
},
doi = {10.1073/pnas.2122636119},
year = {2022},
date = {2022-08-30},
journal = {Proc Natl Acad Sci},
volume = {119},
issue = {35},
pages = {e2122636119},
abstract = {Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the database, since representative relatives must be present. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax, a deep neural network program based on natural language processing to precisely classify the superkingdom and phylum of DNA sequences taxonomically without the need for a known representative relative from a database. We show BERTax to be at least on par with the state-of-the-art approaches when taxonomically similar species are part of the training data. For novel organisms, however, BERTax clearly outperforms any existing approach. Finally, we show that BERTax can also be combined with database approaches to further increase the prediction quality in almost all cases. Since BERTax is not based on similar entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences, thus increasing the overall information gain.},
keywords = {classification, DNA / genomics, machine learning},
pubstate = {published},
tppubtype = {article}
}
Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the database, since representative relatives must be present. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax, a deep neural network program based on natural language processing to precisely classify the superkingdom and phylum of DNA sequences taxonomically without the need for a known representative relative from a database. We show BERTax to be at least on par with the state-of-the-art approaches when taxonomically similar species are part of the training data. For novel organisms, however, BERTax clearly outperforms any existing approach. Finally, we show that BERTax can also be combined with database approaches to further increase the prediction quality in almost all cases. Since BERTax is not based on similar entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences, thus increasing the overall information gain.
2021
Damme, Renaud Van; Hölzer, Martin; Viehweger, Adrian; Müller, Bettina; Bongcam-Rudloff, Erik; Brandt, Christian
Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN) Journal Article
In: PLOS Comput Biol, vol. 17, no. 2, pp. e1008716, 2021.
Abstract | Links | BibTeX | Tags: annotation, assembly, classification, DNA / genomics, metagenomics, RNA / transcriptomics, software
@article{VanDamme:21,
title = {Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN)},
author = {Renaud Van Damme and Martin Hölzer and Adrian Viehweger and Bettina Müller and Erik Bongcam-Rudloff and Christian Brandt},
editor = {Mihaela Pertea},
url = {https://github.com/RVanDamme/MUFFIN},
doi = {10.1371/journal.pcbi.1008716},
year = {2021},
date = {2021-02-09},
urldate = {2021-02-09},
journal = {PLOS Comput Biol},
volume = {17},
number = {2},
pages = {e1008716},
publisher = {Public Library of Science (PLoS)},
abstract = {Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: https://github.com/RVanDamme/MUFFIN.},
keywords = {annotation, assembly, classification, DNA / genomics, metagenomics, RNA / transcriptomics, software},
pubstate = {published},
tppubtype = {article}
}
Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: https://github.com/RVanDamme/MUFFIN.
Mock, Florian; Marz, Manja
Sequence Classification with Machine Learning at the Example of Viral Host Prediction Book Section
In: Frishman, Dmitrij; Marz, Manja (Ed.): Virus Bioinformatics, CRC Press, 2021.
Abstract | Links | BibTeX | Tags: classification, machine learning, virus host interaction, viruses
@incollection{Mock:21,
title = {Sequence Classification with Machine Learning at the Example of Viral Host Prediction},
author = {Florian Mock and Manja Marz},
editor = {Dmitrij Frishman and Manja Marz},
doi = {10.1201/9781003097679-10},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Virus Bioinformatics},
publisher = {CRC Press},
abstract = {Sequence classification is a common task in modern virus bioinformatics research. DNA, RNA, or protein sequences are either filtered for certain properties or the properties of a sequence are to be determined. This task is a very diverse problem. The previous knowledge about the data and also the amount of usable data differ for each project. Also the classification task itself is highly diverse. An additional difficulty is that even today for most biological questions, especially in virology, we lack some set of measurable properties (features) that always explain our observations. Here, we introduce machine learning for viral sequence classification. Together with the reader, we build a deep neural network (DNN) pipeline to classify the host of an influenza A virus from its genome sequence with great accuracy. This result may be somewhat surprising since, despite years of research, we lack a set of properties that lead to highly accurate predictions, and currently, more exceptions are often found than new features. Deep learning can automatically identify a trainable set of features and their dependencies with higher predictive power than previous approaches. This work may serve as a starting point to encourage researchers in virology to use machine learning. Using viral host prediction as an example, we will be discussing classical pitfalls such as data quantity and quality.},
keywords = {classification, machine learning, virus host interaction, viruses},
pubstate = {published},
tppubtype = {incollection}
}
Sequence classification is a common task in modern virus bioinformatics research. DNA, RNA, or protein sequences are either filtered for certain properties or the properties of a sequence are to be determined. This task is a very diverse problem. The previous knowledge about the data and also the amount of usable data differ for each project. Also the classification task itself is highly diverse. An additional difficulty is that even today for most biological questions, especially in virology, we lack some set of measurable properties (features) that always explain our observations. Here, we introduce machine learning for viral sequence classification. Together with the reader, we build a deep neural network (DNN) pipeline to classify the host of an influenza A virus from its genome sequence with great accuracy. This result may be somewhat surprising since, despite years of research, we lack a set of properties that lead to highly accurate predictions, and currently, more exceptions are often found than new features. Deep learning can automatically identify a trainable set of features and their dependencies with higher predictive power than previous approaches. This work may serve as a starting point to encourage researchers in virology to use machine learning. Using viral host prediction as an example, we will be discussing classical pitfalls such as data quantity and quality.
2020
Hufsky, Franziska; Beerenwinkel, Niko; Meyer, Irmtraud M.; Roux, Simon; Cook, Georgia May; Kinsella, Cormac M.; Lamkiewicz, Kevin; Marquet, Mike; Nieuwenhuijse, David F.; Olendraite, Ingrida; Paraskevopoulou, Sofia; Young, Francesca; Dijkman, Ronald; Ibrahim, Bashar; Kelly, Jenna; Mercier, Philippe Le; Marz, Manja; Ramette, Alban; Thiel, Volker
The International Virus Bioinformatics Meeting 2020 Journal Article
In: Viruses, vol. 12, no. 12, pp. 1398, 2020.
Abstract | Links | BibTeX | Tags: classification, conference report, evolution, metagenomics, software, viruses
@article{Hufsky:20b,
title = {The International Virus Bioinformatics Meeting 2020},
author = {Franziska Hufsky and Niko Beerenwinkel and Irmtraud M. Meyer and Simon Roux and Georgia May Cook and Cormac M. Kinsella and Kevin Lamkiewicz and Mike Marquet and David F. Nieuwenhuijse and Ingrida Olendraite and Sofia Paraskevopoulou and Francesca Young and Ronald Dijkman and Bashar Ibrahim and Jenna Kelly and Philippe Le Mercier and Manja Marz and Alban Ramette and Volker Thiel},
doi = {10.3390/v12121398},
year = {2020},
date = {2020-12-06},
urldate = {2020-01-01},
journal = {Viruses},
volume = {12},
number = {12},
pages = {1398},
publisher = {MDPI AG},
abstract = {The International Virus Bioinformatics Meeting 2020 was originally planned to take place in Bern, Switzerland, in March 2020. However, the COVID-19 pandemic put a spoke in the wheel of almost all conferences to be held in 2020. After moving the conference to 8–9 October 2020, we got hit by the second wave and finally decided at short notice to go fully online. On the other hand, the pandemic has made us even more aware of the importance of accelerating research in viral bioinformatics. Advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks. The International Virus Bioinformatics Meeting 2020 has attracted approximately 120 experts in virology and bioinformatics from all over the world to join the two-day virtual meeting. Despite concerns being raised that virtual meetings lack possibilities for face-to-face discussion, the participants from this small community created a highly interactive scientific environment, engaging in lively and inspiring discussions and suggesting new research directions and questions. The meeting featured five invited and twelve contributed talks, on the four main topics: (1) proteome and RNAome of RNA viruses, (2) viral metagenomics and ecology, (3) virus evolution and classification and (4) viral infections and immunology. Further, the meeting featured 20 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.},
keywords = {classification, conference report, evolution, metagenomics, software, viruses},
pubstate = {published},
tppubtype = {article}
}
The International Virus Bioinformatics Meeting 2020 was originally planned to take place in Bern, Switzerland, in March 2020. However, the COVID-19 pandemic put a spoke in the wheel of almost all conferences to be held in 2020. After moving the conference to 8–9 October 2020, we got hit by the second wave and finally decided at short notice to go fully online. On the other hand, the pandemic has made us even more aware of the importance of accelerating research in viral bioinformatics. Advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks. The International Virus Bioinformatics Meeting 2020 has attracted approximately 120 experts in virology and bioinformatics from all over the world to join the two-day virtual meeting. Despite concerns being raised that virtual meetings lack possibilities for face-to-face discussion, the participants from this small community created a highly interactive scientific environment, engaging in lively and inspiring discussions and suggesting new research directions and questions. The meeting featured five invited and twelve contributed talks, on the four main topics: (1) proteome and RNAome of RNA viruses, (2) viral metagenomics and ecology, (3) virus evolution and classification and (4) viral infections and immunology. Further, the meeting featured 20 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.
2019
Hufsky, Franziska; Ibrahim, Bashar; Modha, Sejal; Clokie, Martha R. J.; Deinhardt-Emmer, Stefanie; Dutilh, Bas E.; Lycett, Samantha; Simmonds, Peter; Thiel, Volker; Abroi, Aare; Adriaenssens, Evelien M.; Escalera-Zamudio, Marina; Kelly, Jenna Nicole; Lamkiewicz, Kevin; Lu, Lu; Susat, Julian; Sicheritz, Thomas; Robertson, David L.; Marz, Manja
The Third Annual Meeting of the European Virus Bioinformatics Center Journal Article
In: Viruses, vol. 11, no. 5, pp. 420, 2019.
Abstract | Links | BibTeX | Tags: classification, conference report, evolution, metagenomics, software, virus host interaction, viruses
@article{Hufsky:19,
title = {The Third Annual Meeting of the European Virus Bioinformatics Center},
author = {Franziska Hufsky and Bashar Ibrahim and Sejal Modha and Martha R. J. Clokie and Stefanie Deinhardt-Emmer and Bas E. Dutilh and Samantha Lycett and Peter Simmonds and Volker Thiel and Aare Abroi and Evelien M. Adriaenssens and Marina Escalera-Zamudio and Jenna Nicole Kelly and Kevin Lamkiewicz and Lu Lu and Julian Susat and Thomas Sicheritz and David L. Robertson and Manja Marz},
doi = {10.3390/v11050420},
year = {2019},
date = {2019-05-05},
urldate = {2019-05-05},
journal = {Viruses},
volume = {11},
number = {5},
pages = {420},
publisher = {MDPI AG},
abstract = {The Third Annual Meeting of the European Virus Bioinformatics Center (EVBC) took place in Glasgow, United Kingdom, 28–29 March 2019. Virus bioinformatics has become central to virology research, and advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks, being successfully used to detect, control, and treat infections of humans and animals. This active field of research has attracted approximately 110 experts in virology and bioinformatics/computational biology from Europe and other parts of the world to attend the two-day meeting in Glasgow to increase scientific exchange between laboratory- and computer-based researchers. The meeting was held at the McIntyre Building of the University of Glasgow; a perfect location, as it was originally built to be a place for “rubbing your brains with those of other people”, as Rector Stanley Baldwin described it. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The meeting featured eight invited and twelve contributed talks, on the four main topics: (1) systems virology, (2) virus-host interactions and the virome, (3) virus classification and evolution and (4) epidemiology, surveillance and evolution. Further, the meeting featured 34 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting. },
keywords = {classification, conference report, evolution, metagenomics, software, virus host interaction, viruses},
pubstate = {published},
tppubtype = {article}
}
The Third Annual Meeting of the European Virus Bioinformatics Center (EVBC) took place in Glasgow, United Kingdom, 28–29 March 2019. Virus bioinformatics has become central to virology research, and advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks, being successfully used to detect, control, and treat infections of humans and animals. This active field of research has attracted approximately 110 experts in virology and bioinformatics/computational biology from Europe and other parts of the world to attend the two-day meeting in Glasgow to increase scientific exchange between laboratory- and computer-based researchers. The meeting was held at the McIntyre Building of the University of Glasgow; a perfect location, as it was originally built to be a place for “rubbing your brains with those of other people”, as Rector Stanley Baldwin described it. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The meeting featured eight invited and twelve contributed talks, on the four main topics: (1) systems virology, (2) virus-host interactions and the virome, (3) virus classification and evolution and (4) epidemiology, surveillance and evolution. Further, the meeting featured 34 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.
2014
Madhugiri, Ramakanth; Fricke, Markus; Marz, Manja; Ziebuhr, John
RNA structure analysis of alphacoronavirus terminal genome regions Journal Article
In: Virus Res, vol. 194, pp. 76–89, 2014.
Abstract | Links | BibTeX | Tags: classification, coronavirus, evolution, RNA / transcriptomics, RNA structure, viruses
@article{Madhugiri:14,
title = {RNA structure analysis of alphacoronavirus terminal genome regions},
author = {Ramakanth Madhugiri and Markus Fricke and Manja Marz and John Ziebuhr},
doi = {10.1016/j.virusres.2014.10.001},
year = {2014},
date = {2014-10-13},
urldate = {2014-10-13},
journal = {Virus Res},
volume = {194},
pages = {76--89},
abstract = {Coronavirus genome replication is mediated by a multi-subunit protein complex that is comprised of more than a dozen virally encoded and several cellular proteins. Interactions of the viral replicase complex with cis-acting RNA elements located in the 5' and 3'-terminal genome regions ensure the specific replication of viral RNA. Over the past years, boundaries and structures of cis-acting RNA elements required for coronavirus genome replication have been extensively characterized in betacoronaviruses and, to a lesser extent, other coronavirus genera. Here, we review our current understanding of coronavirus cis-acting elements located in the terminal genome regions and use a combination of bioinformatic and RNA structure probing studies to identify and characterize putative cis-acting RNA elements in alphacoronaviruses. The study suggests significant RNA structure conservation among members of the genus Alphacoronavirus but also across genus boundaries. Overall, the conservation pattern identified for 5' and 3'-terminal RNA structural elements in the genomes of alpha- and betacoronaviruses is in agreement with the widely used replicase polyprotein-based classification of the Coronavirinae, suggesting co-evolution of the coronavirus replication machinery with cognate cis-acting RNA elements.},
keywords = {classification, coronavirus, evolution, RNA / transcriptomics, RNA structure, viruses},
pubstate = {published},
tppubtype = {article}
}
Coronavirus genome replication is mediated by a multi-subunit protein complex that is comprised of more than a dozen virally encoded and several cellular proteins. Interactions of the viral replicase complex with cis-acting RNA elements located in the 5' and 3'-terminal genome regions ensure the specific replication of viral RNA. Over the past years, boundaries and structures of cis-acting RNA elements required for coronavirus genome replication have been extensively characterized in betacoronaviruses and, to a lesser extent, other coronavirus genera. Here, we review our current understanding of coronavirus cis-acting elements located in the terminal genome regions and use a combination of bioinformatic and RNA structure probing studies to identify and characterize putative cis-acting RNA elements in alphacoronaviruses. The study suggests significant RNA structure conservation among members of the genus Alphacoronavirus but also across genus boundaries. Overall, the conservation pattern identified for 5' and 3'-terminal RNA structural elements in the genomes of alpha- and betacoronaviruses is in agreement with the widely used replicase polyprotein-based classification of the Coronavirinae, suggesting co-evolution of the coronavirus replication machinery with cognate cis-acting RNA elements.
Lechner, Marcus; Nickel, Astrid I.; Wehner, Stefanie; Riege, Konstantin; Wieseke, Nicolas; Beckmann, Benedikt M.; Hartmann, Roland K.; Marz, Manja
Genomewide comparison and novel ncRNAs of Aquificales Journal Article
In: BMC Genomics, vol. 15, pp. 522, 2014.
Abstract | Links | BibTeX | Tags: alignment, annotation, assembly, bacteria, classification, ncRNAs, phylogenetics
@article{Lechner:14,
title = {Genomewide comparison and novel ncRNAs of Aquificales},
author = {Marcus Lechner and Astrid I. Nickel and Stefanie Wehner and Konstantin Riege and Nicolas Wieseke and Benedikt M. Beckmann and Roland K. Hartmann and Manja Marz},
doi = {10.1186/1471-2164-15-522},
year = {2014},
date = {2014-06-25},
urldate = {2014-06-25},
journal = {BMC Genomics},
volume = {15},
pages = {522},
abstract = {The Aquificales are a diverse group of thermophilic bacteria that thrive in terrestrial and marine hydrothermal environments. They can be divided into the families Aquificaceae, Desulfurobacteriaceae and Hydrogenothermaceae. Although eleven fully sequenced and assembled genomes are available, only little is known about this taxonomic order in terms of RNA metabolism. In this work, we compare the available genomes, extend their protein annotation, identify regulatory sequences, annotate non-coding RNAs (ncRNAs) of known function, predict novel ncRNA candidates, show idiosyncrasies of the genetic decoding machinery, present two different types of transfer-messenger RNAs and variations of the CRISPR systems. Furthermore, we performed a phylogenetic analysis of the Aquificales based on entire genome sequences, and extended this by a classification among all bacteria using 16S rRNA sequences and a set of orthologous proteins.Combining several in silico features (e.g. conserved and stable secondary structures, GC-content, comparison based on multiple genome alignments) with an in vivo dRNA-seq transcriptome analysis of Aquifex aeolicus, we predict roughly 100 novel ncRNA candidates in this bacterium. We have here re-analyzed the Aquificales, a group of bacteria thriving in extreme environments, sharing the feature of a small, compact genome with a reduced number of protein and ncRNA genes. We present several classical ncRNAs and riboswitch candidates. By combining in silico analysis with dRNA-seq data of A. aeolicus we predict nearly 100 novel ncRNA candidates.},
keywords = {alignment, annotation, assembly, bacteria, classification, ncRNAs, phylogenetics},
pubstate = {published},
tppubtype = {article}
}
The Aquificales are a diverse group of thermophilic bacteria that thrive in terrestrial and marine hydrothermal environments. They can be divided into the families Aquificaceae, Desulfurobacteriaceae and Hydrogenothermaceae. Although eleven fully sequenced and assembled genomes are available, only little is known about this taxonomic order in terms of RNA metabolism. In this work, we compare the available genomes, extend their protein annotation, identify regulatory sequences, annotate non-coding RNAs (ncRNAs) of known function, predict novel ncRNA candidates, show idiosyncrasies of the genetic decoding machinery, present two different types of transfer-messenger RNAs and variations of the CRISPR systems. Furthermore, we performed a phylogenetic analysis of the Aquificales based on entire genome sequences, and extended this by a classification among all bacteria using 16S rRNA sequences and a set of orthologous proteins.Combining several in silico features (e.g. conserved and stable secondary structures, GC-content, comparison based on multiple genome alignments) with an in vivo dRNA-seq transcriptome analysis of Aquifex aeolicus, we predict roughly 100 novel ncRNA candidates in this bacterium. We have here re-analyzed the Aquificales, a group of bacteria thriving in extreme environments, sharing the feature of a small, compact genome with a reduced number of protein and ncRNA genes. We present several classical ncRNAs and riboswitch candidates. By combining in silico analysis with dRNA-seq data of A. aeolicus we predict nearly 100 novel ncRNA candidates.
Marz, Manja; Beerenwinkel, Niko; Drosten, Christian; Fricke, Markus; Frishman, Dmitrij; Hofacker, Ivo L; Hoffmann, Dieter; Middendorf, Martin; Rattei, Thomas; Stadler, Peter F; Töfer, Armin
Challenges in RNA virus bioinformatics Journal Article
In: Bioinformatics, vol. 30, no. 13, pp. 1793–1799, 2014.
Abstract | Links | BibTeX | Tags: classification, evolution, review, RNA / transcriptomics, viruses
@article{Marz:14,
title = {Challenges in RNA virus bioinformatics},
author = {Manja Marz and Niko Beerenwinkel and Christian Drosten and Markus Fricke and Dmitrij Frishman and Ivo L Hofacker and Dieter Hoffmann and Martin Middendorf and Thomas Rattei and Peter F Stadler and Armin Töfer},
doi = {10.1093/bioinformatics/btu105},
year = {2014},
date = {2014-03-03},
urldate = {2014-03-03},
journal = {Bioinformatics},
volume = {30},
number = {13},
pages = {1793--1799},
abstract = {Computer-assisted studies of structure, function and evolution of viruses remains a neglected area of research. The attention of bioinformaticians to this interesting and challenging field is far from commensurate with its medical and biotechnological importance. It is telling that out of >200 talks held at ISMB 2013, the largest international bioinformatics conference, only one presentation explicitly dealt with viruses. In contrast to many broad, established and well-organized bioinformatics communities (e.g. structural genomics, ontologies, next-generation sequencing, expression analysis), research groups focusing on viruses can probably be counted on the fingers of two hands. The purpose of this review is to increase awareness among bioinformatics researchers about the pressing needs and unsolved problems of computational virology. We focus primarily on RNA viruses that pose problems to many standard bioinformatics analyses owing to their compact genome organization, fast mutation rate and low evolutionary conservation. We provide an overview of tools and algorithms for handling viral sequencing data, detecting functionally important RNA structures, classifying viral proteins into families and investigating the origin and evolution of viruses.},
keywords = {classification, evolution, review, RNA / transcriptomics, viruses},
pubstate = {published},
tppubtype = {article}
}
Computer-assisted studies of structure, function and evolution of viruses remains a neglected area of research. The attention of bioinformaticians to this interesting and challenging field is far from commensurate with its medical and biotechnological importance. It is telling that out of >200 talks held at ISMB 2013, the largest international bioinformatics conference, only one presentation explicitly dealt with viruses. In contrast to many broad, established and well-organized bioinformatics communities (e.g. structural genomics, ontologies, next-generation sequencing, expression analysis), research groups focusing on viruses can probably be counted on the fingers of two hands. The purpose of this review is to increase awareness among bioinformatics researchers about the pressing needs and unsolved problems of computational virology. We focus primarily on RNA viruses that pose problems to many standard bioinformatics analyses owing to their compact genome organization, fast mutation rate and low evolutionary conservation. We provide an overview of tools and algorithms for handling viral sequencing data, detecting functionally important RNA structures, classifying viral proteins into families and investigating the origin and evolution of viruses.
Sachse, Konrad; Laroucau, Karine; Riege, Konstantin; Wehner, Stefanie; Dilcher, Meik; Creasy, Heather Huot; Weidmann, Manfred; Myers, Garry; Vorimore, Fabien; Vicari, Nadia; Magnino, Simone; Liebler-Tenorio, Elisabeth; Ruettger, Anke; Bavoil, Patrik M.; Hufert, Frank T.; Rosselló-Móra, Ramon; Marz, Manja
In: Syst Appl Microbiol, vol. 37, pp. 79–88, 2014.
Abstract | Links | BibTeX | Tags: bacteria, classification, phylogenetics
@article{Sachse:14,
title = {Evidence for the existence of two new members of the family Chlamydiaceae and proposal of \textit{Chlamydia avium} sp. nov. and \textit{Chlamydia gallinacea} sp. nov.},
author = {Konrad Sachse and Karine Laroucau and Konstantin Riege and Stefanie Wehner and Meik Dilcher and Heather Huot Creasy and Manfred Weidmann and Garry Myers and Fabien Vorimore and Nadia Vicari and Simone Magnino and Elisabeth Liebler-Tenorio and Anke Ruettger and Patrik M. Bavoil and Frank T. Hufert and Ramon Rosselló-Móra and Manja Marz},
doi = {10.1016/j.syapm.2013.12.004},
year = {2014},
date = {2014-01-22},
urldate = {2014-01-22},
journal = {Syst Appl Microbiol},
volume = {37},
pages = {79--88},
abstract = {The family Chlamydiaceae with the recombined single genus Chlamydia currently comprises nine species, all of which are obligate intracellular organisms distinguished by a unique biphasic developmental cycle. Anecdotal evidence from epidemiological surveys in flocks of poultry, pigeons and psittacine birds have indicated the presence of non-classified chlamydial strains, some of which may act as pathogens. In the present study, phylogenetic analysis of ribosomal RNA and ompA genes, as well as multi-locus sequence analysis of 11 field isolates were conducted. All independent analyses assigned the strains into two different clades of monophyletic origin corresponding to pigeon and psittacine strains or poultry isolates, respectively. Comparative genome analysis involving the type strains of currently accepted Chlamydiaceae species and the designated type strains representing the two new clades confirmed that the latter could be classified into two different species as their average nucleotide identity (ANI) values were always below 94%, both with the closest relative species and between themselves. In view of the evidence obtained from the analyses, we propose the addition of two new species to the current classification: Chlamydia avium sp. nov. comprising strains from pigeons and psittacine birds (type strain 10DC88(T); DSMZ: DSM27005(T), CSUR: P3508(T)) and Chlamydia gallinacea sp. nov. comprising strains from poultry (type strain 08-1274/3(T); DSMZ: DSM27451(T), CSUR: P3509(T)).},
keywords = {bacteria, classification, phylogenetics},
pubstate = {published},
tppubtype = {article}
}
The family Chlamydiaceae with the recombined single genus Chlamydia currently comprises nine species, all of which are obligate intracellular organisms distinguished by a unique biphasic developmental cycle. Anecdotal evidence from epidemiological surveys in flocks of poultry, pigeons and psittacine birds have indicated the presence of non-classified chlamydial strains, some of which may act as pathogens. In the present study, phylogenetic analysis of ribosomal RNA and ompA genes, as well as multi-locus sequence analysis of 11 field isolates were conducted. All independent analyses assigned the strains into two different clades of monophyletic origin corresponding to pigeon and psittacine strains or poultry isolates, respectively. Comparative genome analysis involving the type strains of currently accepted Chlamydiaceae species and the designated type strains representing the two new clades confirmed that the latter could be classified into two different species as their average nucleotide identity (ANI) values were always below 94%, both with the closest relative species and between themselves. In view of the evidence obtained from the analyses, we propose the addition of two new species to the current classification: Chlamydia avium sp. nov. comprising strains from pigeons and psittacine birds (type strain 10DC88(T); DSMZ: DSM27005(T), CSUR: P3508(T)) and Chlamydia gallinacea sp. nov. comprising strains from poultry (type strain 08-1274/3(T); DSMZ: DSM27451(T), CSUR: P3509(T)).
2011
Dilcher, Meik; Hasib, Lekbira; Lechner, Marcus; Wieseke, Nicolas; Middendorf, Martin; Marz, Manja; Koch, Andrea; Spiegel, Martin; Dobler, Gerhard; Hufert, Frank T; Weidmann, Manfred
Genetic characterization of Tribeč virus and Kemerovo virus, two tick-transmitted human-pathogenic Orbiviruses Journal Article
In: Virology, vol. 423, pp. 68–76, 2011.
Abstract | Links | BibTeX | Tags: classification, evolution, insects, phylogenetics, virus host interaction, viruses
@article{Dilcher:12,
title = {Genetic characterization of Tribeč virus and Kemerovo virus, two tick-transmitted human-pathogenic Orbiviruses},
author = {Meik Dilcher and Lekbira Hasib and Marcus Lechner and Nicolas Wieseke and Martin Middendorf and Manja Marz and Andrea Koch and Martin Spiegel and Gerhard Dobler and Frank T Hufert and Manfred Weidmann},
doi = {10.1016/j.virol.2011.11.020},
year = {2011},
date = {2011-12-20},
urldate = {2011-12-20},
journal = {Virology},
volume = {423},
pages = {68--76},
abstract = {We determined the complete genome sequences of Tribeč virus (TRBV) and Kemerovo virus (KEMV), two tick-transmitted Orbiviruses that can cause diseases of the central nervous system and that are currently classified into the Great Island virus serogroup. VP2 proteins of TRBV and KEMV show very low sequence similarity to the homologous VP4 protein of tick-transmitted Great Island virus (GIV). The new sequence data support previous serological classification of these Orbiviruses into the Kemerovo serogroup, which is different from the Great Island virus serogroup. Genome segment 9 of TRBV and KEMV encodes several overlapping ORF's in the +1 reading frame relative to VP6(Hel). A co-phylogenetic analysis indicates a host switch from insect-borne Orbiviruses toward Ixodes species, which is in disagreement with previously published data.},
keywords = {classification, evolution, insects, phylogenetics, virus host interaction, viruses},
pubstate = {published},
tppubtype = {article}
}
We determined the complete genome sequences of Tribeč virus (TRBV) and Kemerovo virus (KEMV), two tick-transmitted Orbiviruses that can cause diseases of the central nervous system and that are currently classified into the Great Island virus serogroup. VP2 proteins of TRBV and KEMV show very low sequence similarity to the homologous VP4 protein of tick-transmitted Great Island virus (GIV). The new sequence data support previous serological classification of these Orbiviruses into the Kemerovo serogroup, which is different from the Great Island virus serogroup. Genome segment 9 of TRBV and KEMV encodes several overlapping ORF's in the +1 reading frame relative to VP6(Hel). A co-phylogenetic analysis indicates a host switch from insect-borne Orbiviruses toward Ixodes species, which is in disagreement with previously published data.
2010
Yusuf, Dilmurat; Marz, Manja; Stadler, Peter F; Hofacker, Ivo L
Bcheck: a wrapper tool for detecting RNase P RNA genes Journal Article
In: BMC Genomics, vol. 11, pp. 432, 2010.
Abstract | Links | BibTeX | Tags: annotation, bacteria, classification, fungi, ncRNAs, RNA / transcriptomics, software
@article{Yusuf:10,
title = {Bcheck: a wrapper tool for detecting RNase P RNA genes},
author = {Dilmurat Yusuf and Manja Marz and Peter F Stadler and Ivo L Hofacker},
url = {http://rna.tbi.univie.ac.at/bcheck},
doi = {10.1186/1471-2164-11-432},
year = {2010},
date = {2010-07-13},
urldate = {2010-07-13},
journal = {BMC Genomics},
volume = {11},
pages = {432},
abstract = {Effective bioinformatics solutions are needed to tackle challenges posed by industrial-scale genome annotation. We present Bcheck, a wrapper tool which predicts RNase P RNA genes by combining the speed of pattern matching and sensitivity of covariance models. The core of Bcheck is a library of subfamily specific descriptor models and covariance models. Scanning all microbial genomes in GenBank identifies RNase P RNA genes in 98% of 1024 microbial chromosomal sequences within just 4 hours on single CPU. Comparing to existing annotations found in 387 of the GenBank files, Bcheck predictions have more intact structure and are automatically classified by subfamily membership. For eukaryotic chromosomes Bcheck could identify the known RNase P RNA genes in 84 out of 85 metazoan genomes and 19 out of 21 fungi genomes. Bcheck predicted 37 novel eukaryotic RNase P RNA genes, 32 of which are from fungi. Gene duplication events are observed in at least 20 metazoan organisms. Scanning of meta-genomic data from the Global Ocean Sampling Expedition, comprising over 10 million sample sequences (18 Gigabases), predicted 2909 unique genes, 98% of which fall into ancestral bacteria A type of RNase P RNA and 66% of which have no close homolog to known prokaryotic RNase P RNA. The combination of efficient filtering by means of a descriptor-based search and subsequent construction of a high-quality gene model by means of a covariance model provides an efficient method for the detection of RNase P RNA genes in large-scale sequencing data. Bcheck is implemented as webserver and can also be downloaded for local use from http://rna.tbi.univie.ac.at/bcheck.},
keywords = {annotation, bacteria, classification, fungi, ncRNAs, RNA / transcriptomics, software},
pubstate = {published},
tppubtype = {article}
}
Effective bioinformatics solutions are needed to tackle challenges posed by industrial-scale genome annotation. We present Bcheck, a wrapper tool which predicts RNase P RNA genes by combining the speed of pattern matching and sensitivity of covariance models. The core of Bcheck is a library of subfamily specific descriptor models and covariance models. Scanning all microbial genomes in GenBank identifies RNase P RNA genes in 98% of 1024 microbial chromosomal sequences within just 4 hours on single CPU. Comparing to existing annotations found in 387 of the GenBank files, Bcheck predictions have more intact structure and are automatically classified by subfamily membership. For eukaryotic chromosomes Bcheck could identify the known RNase P RNA genes in 84 out of 85 metazoan genomes and 19 out of 21 fungi genomes. Bcheck predicted 37 novel eukaryotic RNase P RNA genes, 32 of which are from fungi. Gene duplication events are observed in at least 20 metazoan organisms. Scanning of meta-genomic data from the Global Ocean Sampling Expedition, comprising over 10 million sample sequences (18 Gigabases), predicted 2909 unique genes, 98% of which fall into ancestral bacteria A type of RNase P RNA and 66% of which have no close homolog to known prokaryotic RNase P RNA. The combination of efficient filtering by means of a descriptor-based search and subsequent construction of a high-quality gene model by means of a covariance model provides an efficient method for the detection of RNase P RNA genes in large-scale sequencing data. Bcheck is implemented as webserver and can also be downloaded for local use from http://rna.tbi.univie.ac.at/bcheck.