Complete Genome Sequence of Pseudomonas Sp. HT11 Isolated from Broad Bean (vicia Faba L.)
Current Genetics（2025）
Chongqing Academy of Agricultural Sciences
Abstract
The bacterial strain HT11 isolated from broad bean (Vicia faba L.) exhibited strong antifungal activity against Botrytis fabiopsis, the causative agent of red spot disease in broad bean. To gain insights into the secondary metabolites produced by HT11,its entire genome was sequenced and subjected to comprehensive analysis. The genome comprised a single circular chromosome of 6,335,588 base pairs (bp) in length. Comparative analysis of the 16 S rRNA gene and the average nucleotide identity (ANI) confirmed the HT11 strain as a new Pseudomonas strain. The complete genome encoded 5,366 predicted open reading frames (ORFs), 66 tRNA genes and 16 rRNA genes. The total length of the annotated genes accounted for 82.93
MoreTranslated text
Key words
Vicia faba,Botrytis fabiopsis,Pseudomonas sp.,Complete genome sequence,Secondary metabolites
求助PDF
上传PDF
View via Publisher
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
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
Summary is being generated by the instructions you defined