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BMC Genet. 2018 Oct 29;19(1):98. doi: 10.1186/s12863-018-0680-1.

An improved statistical model for taxonomic assignment of metagenomics.

Author information

1
Department of Biostatistics, Columbia University, New York, NY, USA.
2
Sergievsky Center, Taub Institute, and Departments of Epidemiology and Neurology, Columbia University, New York, NY, USA. JHL2@cumc.columbia.edu.
3
Sergievsky Center, Columbia University, 630 West 168th Street, P&S Unit 16, New York, NY, 10032, USA. JHL2@cumc.columbia.edu.

Abstract

BACKGROUND:

With the advances in the next-generation sequencing technologies, researchers can now rapidly examine the composition of samples from humans and their surroundings. To enhance the accuracy of taxonomy assignments in metagenomic samples, we developed a method that allows multiple mismatch probabilities from different genomes.

RESULTS:

We extended the algorithm of taxonomic assignment of metagenomic sequence reads (TAMER) by developing an improved method that can set a different mismatch probability for each genome rather than imposing a single parameter for all genomes, thereby obtaining a greater degree of accuracy. This method, which we call TADIP (Taxonomic Assignment of metagenomics based on DIfferent Probabilities), was comprehensively tested in simulated and real datasets. The results support that TADIP improved the performance of TAMER especially in large sample size datasets with high complexity.

CONCLUSIONS:

TADIP was developed as a statistical model to improve the estimate accuracy of taxonomy assignments. Based on its varying mismatch probability setting and correlated variance matrix setting, its performance was enhanced for high complexity samples when compared with TAMER.

KEYWORDS:

EM algorithm; Metagenomics; Taxonomic assignment

PMID:
30373533
PMCID:
PMC6206629
DOI:
10.1186/s12863-018-0680-1
[Indexed for MEDLINE]
Free PMC Article

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