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- W2785803357 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 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 challenging to curate and validate user-contributed data manually. This, in turn, propagates prediction errors, leading to future erroneous taxonomic assignments. 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 multiple sequence alignments to model insertions, deletions, and substitutions and flag sequences with incongruous fit to the model. Our analysis of a complete set of curated Insecta COI reference sequences identifies the presence of numerous anomalous sequences, which makes a strong case for the validity of our approach and for the importance of new strategies to screen publicly available COI references." @default.
- W2785803357 created "2018-02-23" @default.
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- W2785803357 date "2018-01-29" @default.
- W2785803357 modified "2023-10-01" @default.
- W2785803357 title "Detecting anomalies in the Cytochrome C Oxidase I amplicon sequences using minimum scoring segments" @default.
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- W2785803357 doi "https://doi.org/10.1145/3183628.3183629" @default.
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