Mahdi Belcaid
ABSTRACT
The cytochrome c oxidase 1 (COI) gene is among the most popular markers for molecular biodiversity estimation. In essence, COI-based approaches for taxonomic identification rely on comprehensive reference databases to assign unknown sequences to known species and/or to enhance the identification of new species. As such, for COI-based methods to be effective, the accuracy and integrity of reference databases are critical. However, as COI repositories grow, it becomes difficult to manually curate and validate user-contributed data. This, in turn, propagates prediction errors, therefore reinforcing the cycle. Here, we propose a new computationally efficient approach for identifying anomalies which are either due to systematic biases (indels and chimeras) or to user error (mistranslation and misclassification). Our approach uses COI reference alignments to model substitutions across the marker. The resulting model is subsequently used to screen and identify sequences with incongruous fit to the model. Analysis of the complete set of curated Insecta COI reference sequences identify the presence of numerous anomalous sequences, which makes a strong case for the importance of new strategies to screen publicly available COI references.
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Index Terms
- A profile-based probabilistic approach for the detection of anomalies in the cytochrome C oxidase I amplicon sequences
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