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
In metabarcoding studies, Linnaean taxonomy assignments of Operational Taxonomic Units (OTUs) or Amplicon Sequence Variants (ASVs) underpin many downstream bioinformatics analyses and ecological interpretations of environmental DNA (eDNA) datasets. However, public molecular databases (i.e., SILVA, EUKARYOME, BOLD) for most microbial metazoan phyla (nematodes, tardigrades, kinorhynchs, etc.) are sparsely populated, negatively impacting our ability to assign ecologically meaningful taxonomy to these understudied groups. Additionally, the choice of bioinformatics parameters and computational algorithms can further impact the accuracy of eDNA taxonomy assignments. Here, we use two <i>in-silico</i> datasets to show that taxonomy assignments using the 18S rRNA gene can be dramatically improved by curating Linnaean taxonomy strings associated with each reference sequence and closing phylogenetic gaps by improving taxon sampling. Using free-living nematodes as a case study, we applied two commonly used taxonomy assignment algorithms (BLAST+ and the QIIME2 Naïve Bayes classifier) across six iterations of the SILVA 138 reference database to evaluate the precision and accuracy of taxonomy assignments. The BLAST+ top hit with a 90% sequence similarity cutoff often returned the highest percentage of correctly assigned taxonomy at the genus level, and the QIIME2 Naïve Bayes classifier performed similarly well when paired with a reference database containing corrected taxonomy strings. Our results highlight the urgent need for phylogenetically-informed expansions of public reference databases (encompassing both genomes and common gene markers), focused on poorly sampled lineages which are now robustly recovered via eDNA metabarcoding approaches. Additional taxonomy curation efforts should be applied to popular reference databases such as SILVA, and taxon sampling could be rapidly improved by more frequent incorporation of newly published GenBank sequences linked to genus and/or species level identifications.
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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