The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks

By Pelin Yilmaz, Laura Wegener Parfrey, Pablo Yarza, Jan Gerken, Elmar Pruesse, Christian Quast, Timmy Schweer, Jörg Peplies, Wolfgang Ludwig, Frank Oliver Glöckner. Nucleic Acids Res., 2013

3,399 citations recorded by the source corpus.

SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive resource for up-to-date quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. SILVA provides a manually curated taxonomy for all three domains of life, based on representative phylogenetic trees for the small- and large-subunit rRNA genes. This article describes the improvements the SILVA taxonomy has undergone in the last 3 years. Specifically we are focusing on the curation process, the various resources used for curation and the comparison of the SILVA taxonomy with Greengenes and RDP-II taxonomies. Our comparisons not only revealed a reasonable overlap between the taxa names, but also points to significant differences in both names and numbers of taxa between the three resources.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6ef7a8f0d335db7a2d0ed60d54791dc074f5deb2

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Citations in the corpus

GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy (2021, methodology): “Establishing the provenance of type material remains an ongoing challenge and this decision is made by considering a number of factors including the classification of 16S rRNA genes against the Living Tree Project database (13), the type status of genomes at NCBI, previous GTDB classifications, cons”
SILVA, RDP, Greengenes, NCBI and OTT — how do these taxonomies compare? (2017, methodology): “The SILVA database [4] contains taxonomic information for the domains of Bacteria, Archaea and Eukarya.”
Synthesis of phylogeny and taxonomy into a comprehensive tree of life (2014, background): “We therefore constructed the Open Tree Taxonomy (OTT), by merging Index Fungorum (35), SILVA (36, 37), NCBI (38), GBIF (39), IRMNG (40), and two clade-specific resources (41) (42) using a fully-documented, repeatable process that includes both generalized merge steps and user-defined patches (See SO”
EzBioCloud: a genome-driven database and platform for microbiome identification and discovery (2024, neutral): “This method facilitates further analysis with ANI and is particularly valuable for genome sequences that do not have or have only a partial 16S rRNA sequence, where a complete 16S rRNA sequence is often necessary for accurate taxonomic assignment [5, 6].”
The European Nucleotide Archive in 2019 (2019, background): “ee/) (5-7) that consume ENA, add value through such curation processes and make their data available to tool and service providers.”
Coastal marine habitats harbor novel early-diverging fungal diversity (2017, methodology): “Using the USEARCH qualityfiltering pipeline (Edgar, 2010) as implemented in QIIME, noisy sequenceswere filtered at 99% similarity before de novo and referencebased chimera checks were performed, the latter using the SILVA LSU 119 release as a reference (Quast et al., 2013; Yilmaz et al., 2014).”
High-level classification of the Fungi and a tool for evolutionary ecological analyses (2018, background): “For better comparability across fungi and preferably across all organisms, taxonomic ranks should be monophyletic and exhibit at least roughly similar age (Hennig 1966; Avise and John 1999; Yilmaz et al. 2014; Samarakoon et al. 2016; Hyde et al. 2017; Tedersoo 2017a).”
MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. (2016, methodology): “We benchmarked 64-bit M EGA 7 performance using 16S ribosomal RNA sequence alignments obtained from the SILVA rRNA database project (Quast et al. 2013; Yilmaz et al. 2014) with thousands of sites and increasingly greater number of sequences (as many as 10,000).”