Deep Neural Network: An Efficient and Optimized Machine Learning Paradigm for Reducing Genome Sequencing Error

TL;DR

This paper presents a deep learning method to correct insertion and deletion errors in genome sequencing data, improving accuracy in genomic analysis.

Contribution

It introduces a novel deep neural network approach specifically designed to correct indel errors in genome sequencing datasets.

Findings

Deep learning effectively reduces indel errors in sequencing data.

The proposed method outperforms traditional error correction techniques.

Improved data quality enhances downstream genomic analysis accuracy.

Abstract

Genomic data I used in many fields but, it has become known that most of the platforms used in the sequencing process produce significant errors. This means that the analysis and inferences generated from these data may have some errors that need to be corrected. On the two main types of genome errors - substitution and indels - our work is focused on correcting indels. A deep learning approach was used to correct the errors in sequencing the chosen dataset