Chrome Extension
WeChat Mini Program
Use on ChatGLM

VOGDB - Database of Virus Orthologous Groups

Viruses(2024)

Univ Vienna | Genoscope | Tech Univ Munich | Australian Inst Marine Sci

Cited 0|Views3
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.
More
Translated text
Key words
virus genomes,protein families,comparative Genomics,orthologous groups,genome annotation,genome analysis
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
2011

被引用6429 | 浏览

2016

被引用27 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文介绍了VOGDB,这是一个病毒同源基因组的数据库,通过多层次的相似性分析,包括序列相似性、序列轮廓比对以及预测的蛋白质结构,为病毒基因提供了更敏感的同源性搜索,提高了注释预测和系统发育推断的可能性。

方法】:作者采用了一种逐步将病毒基因分组的方法,通过三个层次的分析,分别是基于成对序列相似性的第一层,基于序列轮廓对齐的第二层,以及使用预测蛋白质结构来发现最远相似性的第三层。

实验】:作者利用了所有来自RefSeq的病毒基因组,并对它们进行了部分重新注释,VOGDB会随着RefSeq的每次更新而更新。该数据库的独特之处在于将原核和真核病毒纳入同一个聚类过程,便于探索两者之间的古老进化关系。