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NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer

Rongshu Wang¹, Jianhua Chen²

¹Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China.

|June 7, 2024

Summary

This study introduces NmTHC, a novel hybrid error correction method using neural machine translation to improve long-read sequencing accuracy. NmTHC enhances alignment identity without losing read length, offering more precise genetic data.

Keywords:

Hybrid error correction Long read Natural language processing Neural machine translation

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Third-generation sequencing produces long reads with high error rates.
Next-generation sequencing (NGS) offers high-precision, low-cost short reads.
Hybrid approaches are needed to correct long-read errors using short reads.

Purpose of the Study:

To develop a hybrid error correction method for long reads.
To leverage neural machine translation for improved accuracy.
To create a sequencing-technology-independent solution.

Main Methods:

A hybrid error correction method (NmTHC) based on a generative neural machine translation model.
Utilizing a sequence-to-sequence framework with Recurrent Neural Networks (RNNs).
Training the model on a corpus derived from aligned long and short reads.

Main Results:

NmTHC outperforms existing hybrid error correction methods on PacBio and Nanopore datasets.
Achieves higher alignment identity with reference genomes without read segmentation.
Preserves the length advantage of long reads while enhancing accuracy.

Conclusions:

NmTHC effectively transforms hybrid error correction into a machine translation problem.
Offers a novel Natural Language Processing (NLP) perspective for long-read error correction.
Provides sequencing-technology-independent, more precise reads.