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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2406.12064 |
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| _version_ | 1866913394015076352 |
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| author | Zhang, Xiaolei Brian Oualline, Grace Shaw, Jim Yu, Yun William |
| author_facet | Zhang, Xiaolei Brian Oualline, Grace Shaw, Jim Yu, Yun William |
| contents | Mobile genetic elements (MGEs) are as ubiquitous in nature as they are varied in type, ranging from viral insertions to transposons to incorporated plasmids. Horizontal transfer of MGEs across bacterial species may also pose a significant threat to global health due to their capability to harbour antibiotic resistance genes. However, despite cheap and rapid whole genome sequencing, the varied nature of MGEs makes it difficult to fully characterize them, and existing methods for detecting MGEs often don't agree on what should count. In this manuscript, we first define and argue in favor of a divergence-based characterization of mobile-genetic elements. Using that paradigm, we present skandiver, a tool designed to efficiently detect MGEs from whole genome assemblies without the need for gene annotation or markers. skandiver determines mobile elements via genome fragmentation, average nucleotide identity (ANI), and divergence time. By building on the scalable skani software for ANI computation, skandiver can query hundreds of complete assemblies against $>$65,000 representative genomes in a few minutes and 19 GB memory, providing scalable and efficient method for elucidating mobile element profiles in incomplete, uncharacterized genomic sequences. For isolated and integrated large plasmids (>10kbp), skandiver's recall was 48\% and 47\%, MobileElementFinder was 59\% and 17\%, and geNomad was 86\% and 32\%, respectively. For isolated large plasmids, skandiver's recall (48\%) is lower than state-of-the-art reference-based methods geNomad (86\%) and MobileElementFinder (59\%). However, skandiver achieves higher recall on integrated plasmids and, unlike other methods, without comparing against a curated database, making skandiver suitable for discovery of novel MGEs.
Availability: https://github.com/YoukaiFromAccounting/skandiver |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12064 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | skandiver: a divergence-based analysis tool for identifying intercellular mobile genetic elements Zhang, Xiaolei Brian Oualline, Grace Shaw, Jim Yu, Yun William Genomics Mobile genetic elements (MGEs) are as ubiquitous in nature as they are varied in type, ranging from viral insertions to transposons to incorporated plasmids. Horizontal transfer of MGEs across bacterial species may also pose a significant threat to global health due to their capability to harbour antibiotic resistance genes. However, despite cheap and rapid whole genome sequencing, the varied nature of MGEs makes it difficult to fully characterize them, and existing methods for detecting MGEs often don't agree on what should count. In this manuscript, we first define and argue in favor of a divergence-based characterization of mobile-genetic elements. Using that paradigm, we present skandiver, a tool designed to efficiently detect MGEs from whole genome assemblies without the need for gene annotation or markers. skandiver determines mobile elements via genome fragmentation, average nucleotide identity (ANI), and divergence time. By building on the scalable skani software for ANI computation, skandiver can query hundreds of complete assemblies against $>$65,000 representative genomes in a few minutes and 19 GB memory, providing scalable and efficient method for elucidating mobile element profiles in incomplete, uncharacterized genomic sequences. For isolated and integrated large plasmids (>10kbp), skandiver's recall was 48\% and 47\%, MobileElementFinder was 59\% and 17\%, and geNomad was 86\% and 32\%, respectively. For isolated large plasmids, skandiver's recall (48\%) is lower than state-of-the-art reference-based methods geNomad (86\%) and MobileElementFinder (59\%). However, skandiver achieves higher recall on integrated plasmids and, unlike other methods, without comparing against a curated database, making skandiver suitable for discovery of novel MGEs. Availability: https://github.com/YoukaiFromAccounting/skandiver |
| title | skandiver: a divergence-based analysis tool for identifying intercellular mobile genetic elements |
| topic | Genomics |
| url | https://arxiv.org/abs/2406.12064 |