Dataset Information

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

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.

SUBMITTER: Edgar RC

PROVIDER: S-EPMC5910792 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Edgar Robert C RC

PeerJ 20180418

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 da ...[more]

PMID: 29682424

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Project description:BackgroundSequencing of 16S rRNA genes has become a powerful technique to study microbial communities and their responses towards changing environmental conditions in various ecosystems. Several tools have been developed for the prediction of functional profiles from 16S rRNA gene sequencing data, because numerous questions in ecosystem ecology require knowledge of community functions in addition to taxonomic composition. However, the accuracy of these tools relies on functional information derived from genomes available in public databases, which are often not representative of the microorganisms present in the studied ecosystem. In addition, there is also a lack of tools to predict functional gene redundancy in microbial communities.ResultsTo address these challenges, we developed Tax4Fun2, an R package for the prediction of functional profiles and functional gene redundancies of prokaryotic communities from 16S rRNA gene sequences. We demonstrate that functional profiles predicted by Tax4Fun2 are highly correlated to functional profiles derived from metagenomes of the same samples. We further show that Tax4Fun2 has higher accuracies than PICRUSt and Tax4Fun. By incorporating user-defined, habitat-specific genomic information, the accuracy and robustness of predicted functional profiles is substantially enhanced. In addition, functional gene redundancies predicted with Tax4Fun2 are highly correlated to functional gene redundancies determined for simulated microbial communities.ConclusionsTax4Fun2 provides researchers with a unique tool to predict and investigate functional profiles of prokaryotic communities based on 16S rRNA gene sequencing data. It is easy-to-use, platform-independent and highly memory-efficient, thus enabling researchers without extensive bioinformatics knowledge or access to high-performance clusters to predict functional profiles. Another unique feature of Tax4Fun2 is that it allows researchers to calculate the redundancy of specific functions, which is a potentially important measure of how resilient a community will be to environmental perturbation. Tax4Fun2 is implemented in R and freely available at https://github.com/bwemheu/Tax4Fun2.

Project description:BackgroundThe phylogeny of the genus Methanobrevibacter was established almost 25 years ago on the basis of the similarities of the 16S rRNA oligonucleotide catalogs. Since then, many 16S rRNA gene sequences of newly isolated strains or clones representing the genus Methanobrevibacter have been deposited. We tried to reorganize the 16S rRNA gene sequences of this genus and revise the taxonomic affiliation of the isolates and clones representing the genus Methanobrevibacter.ResultsThe phylogenetic analysis of the genus based on 786 bp aligned region from fifty-four representative sequences of the 120 available sequences for the genus revealed seven multi-member groups namely, Ruminantium, Smithii, Woesei, Curvatus, Arboriphilicus, Filiformis, and the Termite gut symbionts along with three separate lineages represented by Mbr. wolinii, Mbr. acididurans, and termite gut flagellate symbiont LHD12. The cophenetic correlation coefficient, a test for the ultrametric properties of the 16S rRNA gene sequences used for the tree was found to be 0.913 indicating the high degree of goodness of fit of the tree topology. A significant relationship was found between the 16S rRNA sequence similarity (S) and the extent of DNA hybridization (D) for the genus with the correlation coefficient (r) for logD and logS, and for [ln(-lnD) and ln(-lnS)] being 0.73 and 0.796 respectively. Our analysis revealed that for this genus, when S = 0.984, D would be <70% at least 99% of the times, and with 70% D as the species "cutoff", any 16S rRNA gene sequence showing <98% sequence similarity can be considered as a separate species. In addition, we deduced group specific signature positions that have remained conserved in evolution of the genus.ConclusionsA very significant relationship between D and S was found to exist for the genus Methanobrevibacter, implying that it is possible to predict D from S with a known precision for the genus. We propose to include the termite gut flagellate symbiont LHD12, the methanogenic endosymbionts of the ciliate Nyctotherus ovalis, and rat feces isolate RT reported earlier, as separate species of the genus Methanobrevibacter.

Dataset Information

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

Publications

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

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