DeepCorr: a novel error correction method for 3GS long reads based on deep learning.

ABSTRACT: Long reads generated by third-generation sequencing (3GS) technologies are involved in many biological analyses and play a vital role due to their ultra-long read length. However, the high error rate affects the downstream process. DeepCorr, a novel error correction algorithm for data from both PacBio and ONT platforms based on deep learning is proposed. The core algorithm adopts a recurrent neural network to capture the long-term dependencies in the long reads to convert the problem of long-read error correction to a multi-classification task. It first aligns the high-precision short reads to long reads to generate the corresponding feature vectors and labels, then feeds these vectors to the neural network, and finally trains the model for prediction and error correction. DeepCorr produce

SUBMITTER: Wang R 

PROVIDER: S-EPMC11639150 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature