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.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

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, a database of Virus Orthologous Groups. VOGDB is a multi-layer database that progressively groups viral genes into groups connected by increasingly remote homology. 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 homology. VOGDB groups allow for more sensitive homology searches of novel genes and increase the chance of predicting annotations or inferring phylogeny. VOGDB uses all virus genomes from RefSeq and partially re-annotates them. VOGDB is updated with every RefSeq release. The unique feature of VOGDB is 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 https://vogdb.org under the CC BY 4.0 license.

Microbial community profiling relies on comprehensive reference databases, yet full-length 16S rRNA amplicons remain sparse for many bacterial taxa. We present SGenerator, a neural network-based data augmentation method that generates biologically informative, full-length (1500 bp) 16S rRNA sequences for underrepresented genera. Combining time series forecasting and natural language processing, SGenerator uses a long short-term memory (LSTM) architecture with a sliding-window approach and n-gram segmentation to generate full-length amplicons. Trained on a subset of 2289 sequences from 50 different unbalanced genera of a total of 184 732 high-quality sequences from the RiboGrove database, it produced 500 synthetic sequences per genus across 50 genera. BLASTn validation showed that an average of 300 sequences per genus closely matched native entries, and R2DT analysis confirmed that an average of 244 per genus folded into canonical 16S rRNA secondary structures, indicating strong biological fidelity. Classifiers trained on the augmented datasets achieved F1 and MCC scores of 0.90 on ITGDB and 0.75 on the more specialized MiDAS dataset, with k-mer embeddings slightly outperforming transformer-based representations. These results demonstrate that LSTM-driven sequence generation can effectively fill taxonomic gaps in full-length amplicon databases, overcome hypervariable region biases in short-read data, and has the potential to enhance microbial profiling accuracy in ecological studies.