Bahram, Mohammad
- Department of Ecology, Swedish University of Agricultural Sciences
- University of Tartu
Research article2024Peer reviewedOpen access
EUKARYOME: the rRNA gene reference database for identification of all eukaryotes
Tedersoo, Leho; Moghaddam, Mahdieh S. Hosseyni; Mikryukov, Vladimir; Hakimzadeh, Ali; Bahram, Mohammad; Nilsson, R. Henrik; Yatsiuk, Iryna; Geisen, Stefan; Schwelm, Arne; Piwosz, Kasia; Prous, Marko; Sildever, Sirje; Chmolowska, Dominika; Rueckert, Sonja; Skaloud, Pavel; Laas, Peeter; Tines, Marco; Jung, Jae-Ho; Choi, Ji Hye; Alkahtani, Saad;
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Abstract
Molecular identification of micro- and macroorganisms based on nuclear markers has revolutionized our understanding of their taxonomy, phylogeny and ecology. Today, research on the diversity of eukaryotes in global ecosystems heavily relies on nuclear ribosomal RNA (rRNA) markers. Here, we present the research community-curated reference database EUKARYOME for nuclear ribosomal 18S rRNA, internal transcribed spacer (ITS) and 28S rRNA markers for all eukaryotes, including metazoans (animals), protists, fungi and plants. It is particularly useful for the identification of arbuscular mycorrhizal fungi as it bridges the four commonly used molecular markers-ITS1, ITS2, 18S V4-V5 and 28S D1-D2 subregions. The key benefits of this database over other annotated reference sequence databases are that it is not restricted to certain taxonomic groups and it includes all rRNA markers. EUKARYOME also offers a number of reference long-read sequences that are derived from (meta)genomic and (meta)barcoding-a unique feature that can be used for taxonomic identification and chimera control of third-generation, long-read, high-throughput sequencing data. Taxonomic assignments of rRNA genes in the database are verified based on phylogenetic approaches. The reference datasets are available in multiple formats from the project homepage, http://www.eukaryome.org.
Published in
Database
2024, volume: 2024, article number: baae043
Publisher: OXFORD UNIV PRESS
SLU Authors
UKÄ Subject classification
Bioinformatics (Computational Biology)
Publication identifier
- DOI: https://doi.org/10.1093/database/baae043
Permanent link to this page (URI)
https://res.slu.se/id/publ/130891