Detecting and correcting misclassified sequences in the large-scale public databases.

ABSTRACT:

Motivation

As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the NR database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation

SUBMITTER: Bagheri H 

PROVIDER: S-EPMC7821992 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature