ORCID Profile
0000-0003-4783-8823
Current Organisation
Friedrich-Schiller-Universität Jena
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Publisher: Elsevier BV
Date: 03-2014
DOI: 10.1016/J.SYAPM.2013.12.004
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)).
Publisher: Public Library of Science (PLoS)
Date: 07-09-2010
Publisher: Cold Spring Harbor Laboratory
Date: 22-09-2011
DOI: 10.1261/RNA.2750811
Abstract: During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor.
Publisher: Elsevier BV
Date: 09-2020
Publisher: MDPI AG
Date: 05-05-2022
DOI: 10.3390/V14050973
Abstract: The International Virus Bioinformatics Meeting 2022 took place online, on 23–25 March 2022, and has attracted about 380 participants from all over the world. 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 participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus–host interactions, viral sequence analysis, virus identification and annotation, phages, and viral ersity. This report summarizes the main research findings and highlights presented at the meeting.
Publisher: Springer Science and Business Media LLC
Date: 26-05-2021
DOI: 10.1038/S41586-021-03583-3
Abstract: Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine 1,2 . Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes 3 . However, there is an increasing ide between what is technically possible and what is allowed, because of privacy legislation 4,5 . Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at in idual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.
Publisher: Oxford University Press (OUP)
Date: 28-06-2021
DOI: 10.1093/CID/CIAB588
Abstract: Tracing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission chains is still a major challenge for public health authorities, when incidental contacts are not recalled or are not perceived as potential risk contacts. Viral sequencing can address key questions about SARS-CoV-2 evolution and may support reconstruction of viral transmission networks by integration of molecular epidemiology into classical contact tracing. In collaboration with local public health authorities, we set up an integrated system of genomic surveillance in an urban setting, combining a) viral surveillance sequencing, b) genetically based identification of infection clusters in the population, c) integration of public health authority contact tracing data, and d) a user-friendly dashboard application as a central data analysis platform. Application of the integrated system from August to December 2020 enabled a characterization of viral population structure, analysis of 4 outbreaks at a maximum care hospital, and genetically based identification of 5 putative population infection clusters, all of which were confirmed by contact tracing. The system contributed to the development of improved hospital infection control and prevention measures and enabled the identification of previously unrecognized transmission chains, involving a martial arts gym and establishing a link between the hospital to the local population. Integrated systems of genomic surveillance could contribute to the monitoring and, potentially, improved management of SARS-CoV-2 transmission in the population.
Publisher: MDPI AG
Date: 12-07-2022
DOI: 10.3390/V14071522
Abstract: Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing “Big Data” offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics.
Publisher: Elsevier BV
Date: 11-2021
No related grants have been discovered for Manja Marz.