An improved statistical model for taxonomic assignment of metagenomics.
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.
['Yao|Yujing|Y|', 'Jin|Zhezhen|Z|', 'Lee|Joseph H|JH|0000-0002-2000-4821']
D000465:Algorithms D001419:Bacteria D000069196:Gastrointestinal Microbiome D006801:Humans D016013:Likelihood Functions D016015:Logistic Models D056186:Metagenomics D015233:Models, Statistical D009055:Mouth
Taxonomic assignment Metagenomics EM algorithm