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Species abundance information improves sequence taxonomy classification accuracy.

ABSTRACT: Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.

SUBMITTER: Kaehler BD

PROVIDER: S-EPMC6789115 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Species abundance information improves sequence taxonomy classification accuracy.

Kaehler Benjamin D BD Bokulich Nicholas A NA McDonald Daniel D Knight Rob R Caporaso J Gregory JG Huttley Gavin A GA

Nature communications 20191011 1

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the specie ...[more]

PMID: 31604942

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Project description:We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for sequence discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcriptlevel profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings. The well-characterized reference RNA samples A (pooled cell lines) and B (human brain) from the MAQC consortium, adding spike-ins of synthetic RNA from the External RNA Control Consortium (ERCC). Samples C and D were then constructed by combining A and B in known mixing ratios, 3:1 and 1:3, respectively. All samples were distributed to several independent sites for RNA-Seq library construction and profiling by Illumina HiSeq 2000 and LifeTech SOLiD 5500 platforms. Also, vendors created their own cDNA libraries that were then distributed to each test site, in order to examine the degree of a M-bM-^@M-^\site effectM-bM-^@M-^] that was independent of the library preparation process. To support an assessment of gene models, samples A and B were also sequenced at independent sites by the Roche 454 platform, providing longer reads. For comparison to other technologies, these data were also compared to the MAQC-I Affymetrix U133 Plus2 microarray, several current microarray platforms, and also assessed by 20,801 PrimePCR reactions.

Dataset Information

Species abundance information improves sequence taxonomy classification accuracy.

Publications

Species abundance information improves sequence taxonomy classification accuracy.

Similar Datasets

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