CARE 2.0: reducing false-positive sequencing error corrections using machine learning.

ABSTRACT:

Background

Next-generation sequencing pipelines often perform error correction as a preprocessing step to obtain cleaned input data. State-of-the-art error correction programs are able to reliably detect and correct the majority of sequencing errors. However, they also introduce new errors by making false-positive corrections. These correction mistakes can have negative impact on downstream analysis, such as k-mer statistics, de-novo assembly, and variant calling. This motivates the need for more precise error correction tools.

Results

We present CARE 2.0, a context-aware read error correction tool based on multiple sequence alignment targeting Illumina datasets. In addition to a number of newly introduced optimizations its most significant change is the replacement of CARE

SUBMITTER: Kallenborn F 

PROVIDER: S-EPMC9195321 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature