Lerna: transformer architectures for configuring error correction tools for short- and long-read genome sequencing.

ABSTRACT:

Background

Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- and dataset-dependent. Moreover, evaluating the performance (i.e., Alignment-rate or Gain) of a given tool usually relies on a reference genome, but quality reference genomes are not always available. We introduce Lerna for the automated configuration of k-mer-based EC tools. Lerna first creates a language model (LM) of the uncorrected genomic reads, and then, based on this LM, calculates a metric called the perplexity metric to evaluate the corrected reads for different parameter choices. Next, it finds the one that produces t

SUBMITTER: Sharma A 

PROVIDER: S-EPMC8734100 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature