PhD Projects » ESR 14: Computational methods for the analysis of metagenomic datasets to extract viral sequences within the context of commercial data-mining

@article{nokey,

title = {Screening great ape museum specimens for DNA viruses},

author = {Michelle Hämmerle and Meriam Guellil and Olivia Cheronet and Susanna Sawyer and Irune Ruiz-Gartzia and Esther Lizano and Aigerim Rymbekova and Pere Gelabert and Paolo Bernardi and Sojung Han and Lovro Trgovec-Greif and Thomas Rattei and Verena J. Schuenemann and Tomas Marques-Bonet and Katerina Guschanski and Sebastien Calvignac-Spencer and Ron Pinhasi and Martin Kuhlwilm},

doi = {10.1101/2024.04.25.591107v3},

year  = {2024},

date = {2024-11-01},

journal = {bioRxiv},

abstract = {Natural history museum collections harbour a record of wild species from the past centuries, providing a unique opportunity to study animals as well as their infectious agents. Thousands of great ape specimens are kept in these collections, and could become an important resource for studying the evolution of DNA viruses. Their genetic material is likely to be preserved in dry museum specimens, as reported previously for monkeypox virus genomes from historical orangutan specimens. Here, we screened 209 great ape museum specimens for 99 different DNA viruses, using hybridization capture coupled with short-read high-throughput sequencing. We determined the presence of multiple viruses within this dataset from historical specimens and obtained several near-complete viral genomes. In particular, we report high-coverage (>18-fold) hepatitis B virus genomes from one gorilla and two chimpanzee individuals, which are phylogenetically placed within clades infecting the respective host species.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Trgovec-Greif2024,

title = {VOGDB—Database of Virus Orthologous Groups},

author = {Lovro Trgovec-Greif and Hans-Jörg Hellinger and Jean Mainguy and Alexander Pfundner and Dmitrij Frishman and Michael Kiening and Nicole Suzanne Webster and Patrick William Laffy and Michael Feichtinger and Thomas Rattei},

doi = {10.3390/v16081191},

year  = {2024},

date = {2024-07-25},

urldate = {2024-07-25},

journal = {Viruses},

volume = {16},

issue = {8},

pages = {1191},

abstract = {Computational models of homologous protein groups are essential in sequence bioinformatics. Due to the diversity and rapid evolution of viruses, the grouping of protein sequences from virus genomes is particularly challenging. The low sequence similarities of homologous genes in viruses require specific approaches for sequence- and structure-based clustering. Furthermore, the annotation of virus genomes in public databases is not as consistent and up to date as for many cellular genomes. To tackle these problems, we have developed VOGDB, which is a database of virus orthologous groups. VOGDB is a multi-layer database that progressively groups viral genes into groups connected by increasingly remote similarity. The first layer is based on pair-wise sequence similarities, the second layer is based on the sequence profile alignments, and the third layer uses predicted protein structures to find the most remote similarity. VOGDB groups allow for more sensitive homology searches of novel genes and increase the chance of predicting annotations or inferring phylogeny. VOGD B uses all virus genomes from RefSeq and partially reannotates them. VOGDB is updated with every RefSeq release. The unique feature of VOGDB is the inclusion of both prokaryotic and eukaryotic viruses in the same clustering process, which makes it possible to explore old evolutionary relationships of the two groups. VOGDB is freely available at vogdb.org under the CC BY 4.0 license.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}