인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Biodiversity monitoring using metabarcoding is now widely used as a routine environmental management tool. However, despite the rapid advancement of third-generation high-throughput sequencing platforms, there are limited studies assessing the most suitable tools and approaches for environmental metabarcoding studies. We tested the utility of Oxford Nanopore Technologies MinION sequencing for short-read amplicon sequencing of mitochondrial <i>COI</i> mini-barcodes from a known composition of arthropod species and compared its performance with more commonly used Illumina NovaSeq sequencing. The mock arthropod species assemblage allowed us to optimise a bioinformatic filtering pipeline to identify arthropod species using MinION long reads. Using this pipeline, we identified host species and diet composition by sequencing droppings collected from five individual Irish brown long-eared bats (<i>Plecotus auritus</i>) roosts. We showed that MinION data provided a similar taxonomic assignment to <i>NovaSeq</i> but only if the reference species barcode database was accurate and comprehensive. The <i>P. auritus</i> diet inferred was as expected based on previous morphological and Illumina metabarcoding studies. We showed that less sequencing depth, but a higher number of biological samples were necessary for complete species composition detection by MinION. A relatively simple bioinformatic filtering tool such as NanoPipe could adequately retrieve both host species and diet composition. The biggest standing challenge was the reference database format transferability and comprehensiveness. This pipeline can be used to guide future metabarcoding studies using nanopore sequencing to minimise the cost and effort while optimising results.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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