16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences

Hsieh, Yu-Peng; Hung, Yuan-Mao; Tsai, Mong‐Hsun; Lai, Liang‐Chuan; Chuang, Eric Y.

doi:10.3389/fbinf.2022.905489

Cited by 7 publications

(11 citation statements)

References 54 publications

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“…Reads under 250 bp or exceeding a total expected error threshold of 0.5 were excluded using USEARCH. Next, Mothur’s (v1.48.0) commands ‘classify.seqs’ and ‘remove.lineage’ were used in combination with the 16S ITGDB database, a database for taxonomic classification of 16S rRNA sequences integrating sequences from RDP (version NO.18 trainset), SILVA (version 138) and Greengenes (version 13_8) [ 41 ], to eliminate potential mitochondrial, chloroplast and other non-target sequences. The remaining bacterial sequences were clustered into zero-radius operational taxonomic units (zOTUs [ 42 ], also known as amplicon sequence variants (ASVs) [ 43 ]) using the UNOISE3 algorithm as implemented in USEARCH [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Microbial dynamics and vertical transmission of Escherichia coli across consecutive life stages of the black soldier fly (Hermetia illucens)

Van Looveren,

IJdema,

van der Heijden

et al. 2024

anim microbiome

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Background The black soldier fly (BSF, Hermetia illucens L.) is one of the most promising insects for bioconversion of organic waste, which often carry a high microbial load with potential foodborne pathogens. Although horizontal transmission (from rearing substrate to larvae) has been extensively studied, less is known about vertical transmission of microorganisms, and particularly of foodborne pathogens, across different BSF life stages. Results This study investigated the microbial dynamics and vertical transmission of Escherichia coli across different life stages (larvae, prepupae, pupae and adults) of one BSF life cycle and its associated substrate (chicken feed) and frass, based on a combination of general microbial counts (based on culture-dependent techniques) and the bacterial community composition (based on 16S rRNA gene sequencing). Multiple interactions between the microbiota of the substrate, frass and BSF larvae were affirmed. The larvae showed relative consistency among both the microbial counts and bacterial community composition. Diversification of the bacterial communities started during the pupal stage, while most notable changes of the microbial counts and bacterial community compositions occurred during metamorphosis to adults. Furthermore, vertical transmission of E. coli was investigated after substrate inoculation with approximately 7.0 log cfu/g of kanamycin-resistant E. coli, and monitoring E. coli counts from larval to adult stage. Although the frass still contained substantial levels of E. coli (> 4.5 log cfu/g) and E. coli was taken up by the larvae, limited vertical transmission of E. coli was observed with a decreasing trend until the prepupal stage. E. coli counts were below the detection limit (1.0 log cfu/g) for all BSF samples from the end of the pupal stage and the adult stage. Additionally, substrate inoculation of E. coli did not have a substantial impact on the bacterial community composition of the substrate, frass or different BSF life stages. Conclusions The fluctuating microbial counts and bacterial community composition underscored the dynamic character of the microbiota of BSF life stages. Additionally, vertical transmission throughout one BSF life cycle was not observed for E. coli. Hence, these findings paved the way for future case studies on vertical transmission of foodborne pathogens across consecutive BSF life stages or other insect species.

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Section: Methodsmentioning

confidence: 99%

Microbial dynamics and vertical transmission of Escherichia coli across consecutive life stages of the black soldier fly (Hermetia illucens)

Van Looveren,

IJdema,

van der Heijden

et al. 2024

anim microbiome

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“…The algorithm used to merge the Greengenes, SILVA, RDP, and vaginal processed databases was based on the integration algorithms proposed by Hsieh et al ( 10 ). The algorithm took two databases as inputs and integrated them as follows ( Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The taxonomic nomenclature of the GSR database has been unified to guarantee the coherence of annotations. Its performance has been compared with Greengenes, Greengenes2 ( 9 ), GTDB, SILVA, and RDP databases and other existing integrated databases, including ITGDB ( 10 ) and MetaSquare ( 11 ). The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1–V3, V3–V4, and V3–V5.…”

Section: Introductionmentioning

confidence: 99%

GSR-DB: a manually curated and optimized taxonomical database for 16S rRNA amplicon analysis

Molano,

Vega-Abellaneda,

Manichanh

2024

mSystems

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Amplicon-based 16S ribosomal RNA sequencing remains a widely used method to profile microbial communities, especially in low biomass samples, due to its cost-effectiveness and low-complexity approach. Reference databases are a mainstay for taxonomic assignments, which typically rely on popular databases such as SILVA, Greengenes, Genome Taxonomy Database (GTDB), or Ribosomal Database Project (RDP). However, the inconsistency of the nomenclature across databases and the presence of shortcomings in the annotation of these databases are limiting the resolution of the analysis. To overcome these limitations, we created the GSR database (Greengenes, SILVA, and RDP database), an integrated and manually curated database for bacterial and archaeal 16S amplicon taxonomy analysis. Unlike previous integration approaches, this database creation pipeline includes a taxonomy unification step to ensure consistency in taxonomical annotations. The database was validated with three mock communities, two real data sets, and a 10-fold cross-validation method and compared with existing 16S databases such as Greengenes, Greengenes 2, GTDB, ITGDB, SILVA, RDP, and MetaSquare. Results showed that the GSR database enhances taxonomical annotations of 16S sequences, outperforming current 16S databases at the species level, based on the evaluation of the mock communities. This was confirmed by the 10-fold cross-validation, except for Greengenes 2. The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1–V3, V3–V4, and V3–V5. IMPORTANCE Taxonomic assignments of microorganisms have long been hindered by inconsistent nomenclature and annotation issues in existing databases like SILVA, Greengenes, Greengenes2, Genome Taxonomy Database, or Ribosomal Database Project. To overcome these issues, we created Greengenes-SILVA-RDP database (GSR-DB), accurate and comprehensive taxonomic annotations of 16S amplicon data. Unlike previous approaches, our innovative pipeline includes a unique taxonomy unification step, ensuring consistent and reliable annotations. Our evaluation analyses showed that GSR-DB outperforms existing databases in providing species-level resolution, especially based on mock-community analysis, making it a game-changer for microbiome studies. Moreover, GSR-DB is designed to be accessible to researchers with limited computational resources, making it a powerful tool for scientists across the board. Available for full-length 16S sequences and commonly used hypervariable regions, including V4, V1–V3, V3–V4, and V3–V5, GSR-DB is a go-to database for robust and accurate microbial taxonomy analysis.

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“…Merging algorithm. The algorithm used to merge the Greengenes, SILVA, RDP, and vaginal processed databases was based on the integration algorithms proposed by Hsieh et al (9).…”

Section: Creation Of Gsr Databasementioning

confidence: 99%

“…The taxonomic nomenclature of the GSR database has been unified to guarantee the coherence of annotations. Its performance has been compared with Greengenes, GTDB, SILVA, and RDP databases and other existing integrated databases, including ITGDB (9) and MetaSquare (10). The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1-V3, V3-V4, and V3-V5.…”

Section: Introductionmentioning

confidence: 99%

GSR-DB: a manually curated and optimised taxonomical database for 16S rRNA amplicon analysis

Molano

Vega-Abellaneda

Manichanh

2023

Preprint

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Amplicon-based 16S ribosomal RNA sequencing remains the most widely used method to profile microbial communities, as a low-cost and low-complexity approach. Reference databases are a mainstay for taxonomic assignments, which typically rely on popular databases such as SILVA, Greengenes, GTDB, or RDP. However, the inconsistency of the nomenclature across databases, and the presence of shortcomings in the annotation of these databases are limiting the resolution of the analysis. To overcome these limitations, we created the GSR database (Greengenes, SILVA, and RDP database), an integrated and manually curated database for bacterial and archaeal 16S amplicon taxonomy analysis. Unlike previous integration approaches, this database creation pipeline includes a taxonomy unification step to ensure consistency in taxonomical annotations. The database was validated with three mock communities and two real datasets and compared with existing 16S databases such as Greengenes, GTDB, ITGDB, SILVA, RDP, and MetaSquare. Results showed that the GSR database enhances taxonomical annotations of 16S sequences, outperforming current 16S databases at the species level. The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1-V3, V3-V4, and V3-V5.IMPORTANCETaxonomic assignments of microorganisms have long been hindered by inconsistent nomenclature and annotation issues in existing databases like SILVA, Greengenes, GTDB, or RDP. To overcome these issues, we created GSR-DB, accurate and comprehensive taxonomic annotations of 16S amplicon data. Unlike previous approaches, our innovative pipeline includes a unique taxonomy unification step, ensuring consistent and reliable annotations. Validated with mock communities and real datasets, GSR-DB outperforms existing databases in providing species-level resolution, making it a game-changer for microbiome studies. Moreover, GSR-DB is designed to be accessible to researchers with limited computational resources, making it a powerful tool for scientists across the board. Available for full-length 16S sequences and commonly used hypervariable regions, including V4, V1-V3, V3-V4, and V3-V5, GSR-DB is a go-to database for robust and accurate microbial taxonomy analysis.

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16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences

Cited by 7 publications

References 54 publications

Microbial dynamics and vertical transmission of Escherichia coli across consecutive life stages of the black soldier fly (Hermetia illucens)

Microbial dynamics and vertical transmission of Escherichia coli across consecutive life stages of the black soldier fly (Hermetia illucens)

GSR-DB: a manually curated and optimized taxonomical database for 16S rRNA amplicon analysis

GSR-DB: a manually curated and optimised taxonomical database for 16S rRNA amplicon analysis

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