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Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

De novo Nanopore read quality improvement using deep learning.

Nathan LaPierre¹, Rob Egan², Wei Wang³

¹Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

BMC Bioinformatics

|November 8, 2019

Summary

MiniScrub, a novel Convolutional Neural Network (CNN) method, effectively identifies and removes low-quality segments from Oxford Nanopore sequencing reads. This improves de novo genome assembly accuracy by reducing mis-assemblies and indel errors, particularly in metagenomic datasets.

Keywords:

Deep learning Long sequence reads Oxford Nanopore de novo assembly

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Last Updated: Jan 4, 2026

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Published on: October 31, 2013

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long-read sequencing technologies like Oxford Nanopore simplify genome assembly and structural variation identification.
Oxford Nanopore reads exhibit high error rates, often clustered in low-quality segments.
Existing error correction methods struggle with sensitivity, leading to mis-assemblies.

Purpose of the Study:

To develop a robust method for identifying and removing low-quality segments from Nanopore reads.
To minimize the impact of read errors on downstream genome assembly processes.
To enhance the accuracy of de novo genome assembly and structural variation analysis.

Main Methods:

Developed MiniScrub, a Convolutional Neural Network (CNN) based approach.
Generated read-to-read overlaps using MiniMap2.
Encoded overlaps into images for CNN model training to predict low-quality segments.

Main Results:

MiniScrub effectively identifies and removes low-quality Nanopore read segments.
Demonstrated robust improvement in read quality and error correction, especially for metagenomic data.
De novo genome assembly using scrubbed reads resulted in significantly fewer mis-assemblies and large indel errors.

Conclusions:

MiniScrub robustly enhances Oxford Nanopore read quality, benefiting downstream applications like de novo assembly.
The tool is particularly effective in metagenomic settings.
MiniScrub is proposed as a preprocessing tool for Nanopore reads and is available as open-source software.