Lee J Kerkhof, Pierce A Roth, Samir V Deshpande, R Cory Bernhards, Alvin T Liem, Jessica M Hill, Max M Häggblom, Nicole S Webster, Olufunmilola Ibironke, Seda Mirzoyan, James J Polashock, Raymond F Sullivan
{"title":"核糖体操纵子数据库和微生物组菌株水平分辨率的MegaBLAST设置。","authors":"Lee J Kerkhof, Pierce A Roth, Samir V Deshpande, R Cory Bernhards, Alvin T Liem, Jessica M Hill, Max M Häggblom, Nicole S Webster, Olufunmilola Ibironke, Seda Mirzoyan, James J Polashock, Raymond F Sullivan","doi":"10.1093/femsmc/xtac002","DOIUrl":null,"url":null,"abstract":"<p><p>Current methods to characterize microbial communities generally employ sequencing of the 16S rRNA gene (<500 bp) with high accuracy (∼99%) but limited phylogenetic resolution. However, long-read sequencing now allows for the profiling of near-full-length ribosomal operons (16S-ITS-23S rRNA genes) on platforms such as the Oxford Nanopore MinION. Here, we describe an rRNA operon database with >300 ,000 entries, representing >10 ,000 prokaryotic species and ∼ 150, 000 strains. Additionally, BLAST parameters were identified for strain-level resolution using <i>in silico</i> mutated, mock rRNA operon sequences (70-95% identity) from four bacterial phyla and two members of the Euryarchaeota, mimicking MinION reads. MegaBLAST settings were determined that required <3 s per read on a Mac Mini with strain-level resolution for sequences with >84% identity. These settings were tested on rRNA operon libraries from the human respiratory tract, farm/forest soils and marine sponges ( <i>n</i> = 1, 322, 818 reads for all sample sets). Most rRNA operon reads in this data set yielded best BLAST hits (95 ± 8%). However, only 38-82% of library reads were compatible with strain-level resolution, reflecting the dominance of human/biomedical-associated prokaryotic entries in the database. Since the MinION and the Mac Mini are both portable, this study demonstrates the possibility of rapid strain-level microbiome analysis in the field.</p>","PeriodicalId":73024,"journal":{"name":"FEMS microbes","volume":"3 ","pages":"xtac002"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117742/pdf/xtac002.pdf","citationCount":"1","resultStr":"{\"title\":\"A ribosomal operon database and MegaBLAST settings for strain-level resolution of microbiomes.\",\"authors\":\"Lee J Kerkhof, Pierce A Roth, Samir V Deshpande, R Cory Bernhards, Alvin T Liem, Jessica M Hill, Max M Häggblom, Nicole S Webster, Olufunmilola Ibironke, Seda Mirzoyan, James J Polashock, Raymond F Sullivan\",\"doi\":\"10.1093/femsmc/xtac002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Current methods to characterize microbial communities generally employ sequencing of the 16S rRNA gene (<500 bp) with high accuracy (∼99%) but limited phylogenetic resolution. However, long-read sequencing now allows for the profiling of near-full-length ribosomal operons (16S-ITS-23S rRNA genes) on platforms such as the Oxford Nanopore MinION. Here, we describe an rRNA operon database with >300 ,000 entries, representing >10 ,000 prokaryotic species and ∼ 150, 000 strains. Additionally, BLAST parameters were identified for strain-level resolution using <i>in silico</i> mutated, mock rRNA operon sequences (70-95% identity) from four bacterial phyla and two members of the Euryarchaeota, mimicking MinION reads. MegaBLAST settings were determined that required <3 s per read on a Mac Mini with strain-level resolution for sequences with >84% identity. These settings were tested on rRNA operon libraries from the human respiratory tract, farm/forest soils and marine sponges ( <i>n</i> = 1, 322, 818 reads for all sample sets). Most rRNA operon reads in this data set yielded best BLAST hits (95 ± 8%). However, only 38-82% of library reads were compatible with strain-level resolution, reflecting the dominance of human/biomedical-associated prokaryotic entries in the database. Since the MinION and the Mac Mini are both portable, this study demonstrates the possibility of rapid strain-level microbiome analysis in the field.</p>\",\"PeriodicalId\":73024,\"journal\":{\"name\":\"FEMS microbes\",\"volume\":\"3 \",\"pages\":\"xtac002\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117742/pdf/xtac002.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"FEMS microbes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/femsmc/xtac002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"FEMS microbes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/femsmc/xtac002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A ribosomal operon database and MegaBLAST settings for strain-level resolution of microbiomes.
Current methods to characterize microbial communities generally employ sequencing of the 16S rRNA gene (<500 bp) with high accuracy (∼99%) but limited phylogenetic resolution. However, long-read sequencing now allows for the profiling of near-full-length ribosomal operons (16S-ITS-23S rRNA genes) on platforms such as the Oxford Nanopore MinION. Here, we describe an rRNA operon database with >300 ,000 entries, representing >10 ,000 prokaryotic species and ∼ 150, 000 strains. Additionally, BLAST parameters were identified for strain-level resolution using in silico mutated, mock rRNA operon sequences (70-95% identity) from four bacterial phyla and two members of the Euryarchaeota, mimicking MinION reads. MegaBLAST settings were determined that required <3 s per read on a Mac Mini with strain-level resolution for sequences with >84% identity. These settings were tested on rRNA operon libraries from the human respiratory tract, farm/forest soils and marine sponges ( n = 1, 322, 818 reads for all sample sets). Most rRNA operon reads in this data set yielded best BLAST hits (95 ± 8%). However, only 38-82% of library reads were compatible with strain-level resolution, reflecting the dominance of human/biomedical-associated prokaryotic entries in the database. Since the MinION and the Mac Mini are both portable, this study demonstrates the possibility of rapid strain-level microbiome analysis in the field.