Controlling critical mistag-associated false discoveries in metagenetic data

Abstract

O_LIMetagenetic methods are commonplace within ecological and environmental research. One concern with these methods is the phenomenon of critical mistagging, where sequences from one sample are erroneously inferred to have originated from another sample due to errors in the attachment, PCR replication or sequencing of sample-specific dual-index tags. For studies using PCR-based library preparation on large sample sizes, the most cost-effective approach to limiting mistag-associated false detections involves using an unsaturated Latin square dual-indexing design. This allows researchers to estimate mistagging rates during sequencing but the statistical procedures for filtering out detections using this mistag rate have received little attention. C_LIO_LIWe propose a straightforward method to limit mistag-associated false discoveries during metabarcoding applications. We analyzed two Illumina metabarcoding datasets produced using unsaturated Latin square designs to explore the distribution of mistagged sequences across dual-index combinations on a per taxon basis. We tested these data for conformity to the assumptions that 1) mistagging follows a binomial distribution [i.e., X ~ B(n, p)] where p, the probability of a sequence being mistagged, varies minimally across taxa and 2) mistags are distributed uniformly across dual-index combinations. We provide R functions that estimate the 95th percentile of expected mistags per dual-index combination for each taxon under these assumptions. C_LIO_LIWe show that mistagging rates were consistent across taxa within the datasets analyzed and that modelling mistagging as a binomial process with uniform distribution across dual-index combinations enabled robust control of mistag-associated false discoveries. C_LIO_LIWe propose that this method of taxon-specific filtering of detections based on the maximum mistags expected per dual-index combination should be broadly accepted during metagenetic analysis, provided that experimental and control sequence abundances per taxon are strongly correlated. When this assumption is violated, data may be better fit by assuming that the distribution of mistags across combinations follows Poisson characteristics [i.e., X ~ Pois({lambda})], with{lambda} empirically estimated from the abundance distribution of mistags among control samples. We provide a second R function for this case, though we have yet to observe such a dataset. Both functions and demonstrations associated with this work are freely available at https://github.com/RTRichar/ModellingCriticalMistags. C_LI