2005
DOI: 10.1111/j.1469-8137.2005.01376.x
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UNITE: a database providing web‐based methods for the molecular identification of ectomycorrhizal fungi

Abstract: Summary• Identification of ectomycorrhizal (ECM) fungi is often achieved through comparisons of ribosomal DNA internal transcribed spacer (ITS) sequences with accessioned sequences deposited in public databases. A major problem encountered is that annotation of the sequences in these databases is not always complete or trustworthy. In order to overcome this deficiency, we report on UNITE, an open-access database.• UNITE comprises well annotated fungal ITS sequences from well defined herbarium specimens that in… Show more

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Cited by 929 publications
(632 citation statements)
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“…Forward and reverse DNA sequences of the ITS1 region were edited and aligned using Bioedit v7.1.11 (Hall 1999) and compared with all fungal sequences available through the BLAST Network Service (National Center for Biotechnology Service, NCBI; http://www.ncbi.nlm.nih.gov/ BLAST) and the UNITE database (Kõljalg et al 2005) using the BLASTn algorithm (Altschul et al 1997). All generated DNA sequence data have been deposited with the EMBL/GenBank Data Libraries and are available under accession nos.…”
Section: Resultsmentioning
confidence: 99%
“…Forward and reverse DNA sequences of the ITS1 region were edited and aligned using Bioedit v7.1.11 (Hall 1999) and compared with all fungal sequences available through the BLAST Network Service (National Center for Biotechnology Service, NCBI; http://www.ncbi.nlm.nih.gov/ BLAST) and the UNITE database (Kõljalg et al 2005) using the BLASTn algorithm (Altschul et al 1997). All generated DNA sequence data have been deposited with the EMBL/GenBank Data Libraries and are available under accession nos.…”
Section: Resultsmentioning
confidence: 99%
“…Database at both GenBank (Benson et al, 2005) and the UNITE (Kõljalg et al, 2005) (updated August 2014.) were used to determine the identity of sequences.…”
Section: Effect Of Temperature Ph Carbon and Nitrogen Sources On Mymentioning
confidence: 99%
“…Demultiplexed reads were used to construct a de novo database by removing sequences with an expected error rate of <0.5-bp per sequence, dereplicating them, removing unique sequences (i.e., singletons), and clustering the remaining sequences into operational taxonomic units (OTUs) at the 97 % similarity threshold. An additional quality control measure was taken by removing sequences from this dataset <75 % similar to any sequence in the UNITE database [54,55]. Raw demultiplexed sequences were then mapped to this filtered database at the 97 % similarity threshold to calculate sequence counts per OTU per sample.…”
Section: Fungal Sequencingmentioning
confidence: 99%