De novo Nanopore read quality improvement using deep learning
Abstract
BACKGROUND: Long read sequencing technologies such as Oxford Nanopore can greatly decrease the complexity of de novo genome assembly and large structural variation identification. Currently Nanopore reads have high error rates, and the errors often cluster into low-quality segments within the reads. The limited sensitivity of existing read-based error correction methods can cause large-scale mis-assemblies in the assembled genomes, motivating further innovation in this area. RESULTS: Here we developed a Convolutional Neural Network (CNN) based method, called MiniScrub, for identification and subsequent "scrubbing" (removal) of low-quality Nanopore read segments to minimize their interference in downstream assembly process. MiniScrub first generates read-to-read overlaps via MiniMap2, then encodes the overlaps into images, and finally builds CNN models to predict low-quality segments. Applying MiniScrub to real world control datasets under several different parameters, we show that it robustly improves read quality, and improves read error correction in the metagenome setting. Compared to raw reads, de novo genome assembly with scrubbed reads produces many fewer mis-assemblies and large indel errors. CONCLUSIONS: MiniScrub is able to robustly improve read quality of Oxford Nanopore reads, especially in the metagenome setting, making it useful for downstream applications such as de novo assembly. We propose MiniScrub as amore »
- Authors:
-
[3]
- Univ. of California, Los Angeles, CA (United States)
- USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)
- USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Merced, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Science Foundation (NSF); National Institutes of Health (NIH)
- OSTI Identifier:
- 1581387
- Grant/Contract Number:
- AC02-05CH11231; DGE-1829071; T3EB016640
- Resource Type:
- Accepted Manuscript
- Journal Name:
- BMC Bioinformatics
- Additional Journal Information:
- Journal Volume: 20; Journal Issue: 1; Journal ID: ISSN 1471-2105
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; deep learning; long sequence reads; Oxford Nanopore; de novo assembly
Citation Formats
LaPierre, Nathan, Egan, Rob, Wang, Wei, and Wang, Zhong. De novo Nanopore read quality improvement using deep learning. United States: N. p., 2019.
Web. doi:10.1186/s12859-019-3103-z.
LaPierre, Nathan, Egan, Rob, Wang, Wei, & Wang, Zhong. De novo Nanopore read quality improvement using deep learning. United States. https://doi.org/10.1186/s12859-019-3103-z
LaPierre, Nathan, Egan, Rob, Wang, Wei, and Wang, Zhong. Wed .
"De novo Nanopore read quality improvement using deep learning". United States. https://doi.org/10.1186/s12859-019-3103-z. https://www.osti.gov/servlets/purl/1581387.
@article{osti_1581387,
title = {De novo Nanopore read quality improvement using deep learning},
author = {LaPierre, Nathan and Egan, Rob and Wang, Wei and Wang, Zhong},
abstractNote = {BACKGROUND: Long read sequencing technologies such as Oxford Nanopore can greatly decrease the complexity of de novo genome assembly and large structural variation identification. Currently Nanopore reads have high error rates, and the errors often cluster into low-quality segments within the reads. The limited sensitivity of existing read-based error correction methods can cause large-scale mis-assemblies in the assembled genomes, motivating further innovation in this area. RESULTS: Here we developed a Convolutional Neural Network (CNN) based method, called MiniScrub, for identification and subsequent "scrubbing" (removal) of low-quality Nanopore read segments to minimize their interference in downstream assembly process. MiniScrub first generates read-to-read overlaps via MiniMap2, then encodes the overlaps into images, and finally builds CNN models to predict low-quality segments. Applying MiniScrub to real world control datasets under several different parameters, we show that it robustly improves read quality, and improves read error correction in the metagenome setting. Compared to raw reads, de novo genome assembly with scrubbed reads produces many fewer mis-assemblies and large indel errors. CONCLUSIONS: MiniScrub is able to robustly improve read quality of Oxford Nanopore reads, especially in the metagenome setting, making it useful for downstream applications such as de novo assembly. We propose MiniScrub as a tool for preprocessing Nanopore reads for downstream analyses. MiniScrub is open-source software and is available at https://bitbucket.org/berkeleylab/jgi-miniscrub .},
doi = {10.1186/s12859-019-3103-z},
journal = {BMC Bioinformatics},
number = 1,
volume = 20,
place = {United States},
year = {Wed Nov 06 00:00:00 EST 2019},
month = {Wed Nov 06 00:00:00 EST 2019}
}
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