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TUIT, a BLAST-based tool for taxonomic classification of nucleotide sequences.

ABSTRACT: Pyrosequencing of 16S ribosomal RNA (rRNA) genes has become the gold standard in human microbiome studies. The routine task of taxonomic classification using 16S rRNA reads is commonly performed by the Ribosomal Database Project (RDP) II Classifier, a robust tool that relies on a set of well-characterized reference sequences. However, the RDP II Classifier may be unable to classify a significant part of the data set due to the absence of proper reference sequences. The taxonomic classification for some unclassified sequences might still be performed using BLAST searches against large and frequently updated nucleotide databases. Here we introduce TUIT (Taxonomic Unit Identification Tool)-an efficient open source and platform-independent application that can perform taxonomic classification on its own or can be used in combination with the RDP II Classifier to maximize the taxonomic identification rate. Using a set of simulated DNA sequences, we demonstrate that the algorithm performs taxonomic classification with high specificity for sequences as short as 125 base pairs. TUIT is applicable for 16S rRNA gene sequence classification; however, it is not restricted to 16S rRNA sequences. In addition, TUIT may be used as a complementary tool for effective taxonomic classification of nucleotide sequences generated by many current platforms, such as Roche 454 and Illumina. Stand-alone TUIT is available online at http://sourceforge.net/projects/tuit/.

SUBMITTER: Tuzhikov A

PROVIDER: S-EPMC4186660 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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TUIT, a BLAST-based tool for taxonomic classification of nucleotide sequences.

Tuzhikov Alexander A Panchin Alexander A Shestopalov Valery I VI

BioTechniques 20140201 2

Pyrosequencing of 16S ribosomal RNA (rRNA) genes has become the gold standard in human microbiome studies. The routine task of taxonomic classification using 16S rRNA reads is commonly performed by the Ribosomal Database Project (RDP) II Classifier, a robust tool that relies on a set of well-characterized reference sequences. However, the RDP II Classifier may be unable to classify a significant part of the data set due to the absence of proper reference sequences. The taxonomic classification f ...[more]

PMID: 24502797

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Project description:BackgroundMicrobiome studies often involve sequencing a marker gene to identify the microorganisms in samples of interest. Sequence classification is a critical component of this process, whereby sequences are assigned to a reference taxonomy containing known sequence representatives of many microbial groups. Previous studies have shown that existing classification programs often assign sequences to reference groups even if they belong to novel taxonomic groups that are absent from the reference taxonomy. This high rate of "over classification" is particularly detrimental in microbiome studies because reference taxonomies are far from comprehensive.ResultsHere, we introduce IDTAXA, a novel approach to taxonomic classification that employs principles from machine learning to reduce over classification errors. Using multiple reference taxonomies, we demonstrate that IDTAXA has higher accuracy than popular classifiers such as BLAST, MAPSeq, QIIME, SINTAX, SPINGO, and the RDP Classifier. Similarly, IDTAXA yields far fewer over classifications on Illumina mock microbial community data when the expected taxa are absent from the training set. Furthermore, IDTAXA offers many practical advantages over other classifiers, such as maintaining low error rates across varying input sequence lengths and withholding classifications from input sequences composed of random nucleotides or repeats.ConclusionsIDTAXA's classifications may lead to different conclusions in microbiome studies because of the substantially reduced number of taxa that are incorrectly identified through over classification. Although misclassification error is relatively minor, we believe that many remaining misclassifications are likely caused by errors in the reference taxonomy. We describe how IDTAXA is able to identify many putative mislabeling errors in reference taxonomies, enabling training sets to be automatically corrected by eliminating spurious sequences. IDTAXA is part of the DECIPHER package for the R programming language, available through the Bioconductor repository or accessible online ( http://DECIPHER.codes ).

Dataset Information

TUIT, a BLAST-based tool for taxonomic classification of nucleotide sequences.

Publications

TUIT, a BLAST-based tool for taxonomic classification of nucleotide sequences.

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