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Cited by 319

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences

Open access
Edgar, Robert C.·2018·PeerJ
DOI 10.7717/peerj.4652
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Abstract

Prediction of taxonomy for marker gene sequences such as 16S ribosomal RNA (rRNA) is a fundamental task in microbiology. Most experimentally observed sequences are diverged from reference sequences of authoritatively named organisms, creating a challenge for prediction methods. I assessed the accuracy of several algorithms using cross-validation by identity, a new benchmark strategy which explicitly models the variation in distances between query sequences and the closest entry in a reference database. When the accuracy of genus predictions was averaged over a representative range of identities with the reference database (100%, 99%, 97%, 95% and 90%), all tested methods had ≤50% accuracy on the currently-popular V4 region of 16S rRNA. Accuracy was found to fall rapidly with identity; for example, better methods were found to have V4 genus prediction accuracy of ∼100% at 100% identity but ∼50% at 97% identity. The relationship between identity and taxonomy was quantified as the probability that a rank is the lowest shared by a pair of sequences with a given pair-wise identity. With the V4 region, 95% identity was found to be a twilight zone where taxonomy is highly ambiguous because the probabilities that the lowest shared rank between pairs of sequences is genus, family, order or class are approximately equal.

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Metadata

Authors
Edgar, R. C.
Venue
PeerJ
Publisher
PeerJ
Published
2018-04-18
Volume
6
Pages
e4652
ISSN
2167-8359 (ISSN-L)
DOI
10.7717/peerj.4652
OpenAlex
W2802266517
Cited by
319
References
55
FWCI
11.22
Type
journal article
Language
en
Open access
gold

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