A Practical Guide and Galaxy Workflow to Avoid Inter-Plasmidic Repeat Collapse and False Gene Loss in Unicycler's Hybrid Assemblies.
MICROBIAL GENOMICS(2024)
Julius Kuhn Inst JKI
Abstract
Generating complete, high-quality genome assemblies is key for any downstream analysis, such as comparative genomics. For bacterial genome assembly, various algorithms and fully automated pipelines exist, which are free-of-charge and easily accessible. However, these assembly tools often cannot unambiguously resolve a bacterial genome, for example due to the presence of sequence repeat structures on the chromosome or on plasmids. Then, a more sophisticated approach and/or manual curation is needed. Such modifications can be challenging, especially for non-bioinformaticians, because they are generally not considered as a straightforward process. In this study, we propose a standardized approach for manual genome completion focusing on the popular hybrid assembly pipeline Unicycler. The provided Galaxy workflow addresses two weaknesses in Unicycler's hybrid assemblies: (i) collapse of inter-plasmidic repeats and (ii) false loss of single-copy sequences. To demonstrate and validate how to detect and resolve these assembly errors, we use two genomes from the Bacillus cereus group. By applying the proposed pipeline following an automated assembly, the genome sequence quality can be significantly improved.
MoreTranslated text
Key words
gene duplication,hybrid assembly,Nanopore,plasmids,repeat,whole- genome sequencing
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