Oxford Nanopore Sequencing, Hybrid Error Correction, and de novo Assembly of a Eukaryotic Genome

Monitoring the progress of DNA molecules through a membrane pore has been postulated as a method for sequencing DNA for several decades. Recently, a nanopore-based sequencing instrument, the Oxford Nanopore MinION, has become available that we used for sequencing the S. cerevisiae genome. To make use of these data, we developed a novel open-source hybrid error correction algorithm Nanocorr (https://github.com/jgurtowski/nanocorr) specifically for Oxford Nanopore reads, as existing packages were incapable of assembling the long read lengths (5-50kbp) at such high error rate (between ~5 and 40% error). With this new method we were able to perform a hybrid error correction of the nanopore reads using complementary MiSeq data and produce a de novo assembly that is highly contiguous and accurate: the contig N50 length is more than ten-times greater than an Illumina-only assembly (678kb versus 59.9kbp), and has greater than 99.88% consensus identity when compared to the reference. Furthermore, the assembly with the long nanopore reads presents a much more complete representation of the features of the genome and correctly assembles gene cassettes, rRNAs, transposable elements, and other genomic features that were almost entirely absent in the Illumina-only assembly.

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A hybrid and scalable error correction algorithm for indel and substitution errors of long reads

BMC Genomics ◽

10.1186/s12864-019-6286-9 ◽

2019 ◽

Vol 20 (S11) ◽

Author(s):

Arghya Kusum Das ◽

Sayan Goswami ◽

Kisung Lee ◽

Seung-Jong Park

Keyword(s):

Error Correction ◽

Error Rates ◽

De Bruijn Graph ◽

Correction Algorithm ◽

Short Read ◽

Short Reads ◽

Long Reads ◽

Long Read ◽

De Bruijn ◽

Error Correction Algorithm

Abstract Background Long-read sequencing has shown the promises to overcome the short length limitations of second-generation sequencing by providing more complete assembly. However, the computation of the long sequencing reads is challenged by their higher error rates (e.g., 13% vs. 1%) and higher cost ($0.3 vs. $0.03 per Mbp) compared to the short reads. Methods In this paper, we present a new hybrid error correction tool, called ParLECH (Parallel Long-read Error Correction using Hybrid methodology). The error correction algorithm of ParLECH is distributed in nature and efficiently utilizes the k-mer coverage information of high throughput Illumina short-read sequences to rectify the PacBio long-read sequences.ParLECH first constructs a de Bruijn graph from the short reads, and then replaces the indel error regions of the long reads with their corresponding widest path (or maximum min-coverage path) in the short read-based de Bruijn graph. ParLECH then utilizes the k-mer coverage information of the short reads to divide each long read into a sequence of low and high coverage regions, followed by a majority voting to rectify each substituted error base. Results ParLECH outperforms latest state-of-the-art hybrid error correction methods on real PacBio datasets. Our experimental evaluation results demonstrate that ParLECH can correct large-scale real-world datasets in an accurate and scalable manner. ParLECH can correct the indel errors of human genome PacBio long reads (312 GB) with Illumina short reads (452 GB) in less than 29 h using 128 compute nodes. ParLECH can align more than 92% bases of an E. coli PacBio dataset with the reference genome, proving its accuracy. Conclusion ParLECH can scale to over terabytes of sequencing data using hundreds of computing nodes. The proposed hybrid error correction methodology is novel and rectifies both indel and substitution errors present in the original long reads or newly introduced by the short reads.

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A Long read hybrid error correction algorithm based on segmented pHMM

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) ◽

10.1109/icmcce51767.2020.00329 ◽

2020 ◽

Author(s):

Hu Lanyue ◽

Chen Jianhua ◽

Wang Rongshu ◽

Lu Zhiwen ◽

Hou Bin

Keyword(s):

Error Correction ◽

Correction Algorithm ◽

Long Read ◽

Error Correction Algorithm

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Hercules: a profile HMM-based hybrid error correction algorithm for long reads

10.1101/233080 ◽

2017 ◽

Author(s):

Can Firtina ◽

Ziv Bar-Joseph ◽

Can Alkan ◽

A. Ercument Cicek

Keyword(s):

Machine Learning ◽

Error Correction ◽

Machine Learning Algorithms ◽

Read Length ◽

Correction Algorithm ◽

Short Reads ◽

Error Profile ◽

Long Reads ◽

Long Read ◽

Error Correction Algorithm

AbstractMotivationChoosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time-efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies.ResultsWe designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platform’s error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads.AvailabilityHercules source code is available at https://github.com/BilkentCompGen/Hercules

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Oxford Nanopore sequencing, hybrid error correction, and de novo assembly of a eukaryotic genome

Genome Research ◽

10.1101/gr.191395.115 ◽

2015 ◽

Vol 25 (11) ◽

pp. 1750-1756 ◽

Cited By ~ 223

Author(s):

Sara Goodwin ◽

James Gurtowski ◽

Scott Ethe-Sayers ◽

Panchajanya Deshpande ◽

Michael C. Schatz ◽

...

Keyword(s):

Error Correction ◽

De Novo Assembly ◽

De Novo ◽

Eukaryotic Genome ◽

Nanopore Sequencing ◽

Oxford Nanopore

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lra: A long read aligner for sequences and contigs

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009078 ◽

2021 ◽

Vol 17 (6) ◽

pp. e1009078

Author(s):

Jingwen Ren ◽

Mark J. P. Chaisson

Keyword(s):

Dynamic Programming ◽

Single Molecule ◽

De Novo Assembly ◽

De Novo ◽

Concave Function ◽

Single Molecule Sequencing ◽

Link Type ◽

Oxford Nanopore ◽

Concave Cost ◽

Long Read

It is computationally challenging to detect variation by aligning single-molecule sequencing (SMS) reads, or contigs from SMS assemblies. One approach to efficiently align SMS reads is sparse dynamic programming (SDP), where optimal chains of exact matches are found between the sequence and the genome. While straightforward implementations of SDP penalize gaps with a cost that is a linear function of gap length, biological variation is more accurately represented when gap cost is a concave function of gap length. We have developed a method, lra, that uses SDP with a concave-cost gap penalty, and used lra to align long-read sequences from PacBio and Oxford Nanopore (ONT) instruments as well as de novo assembly contigs. This alignment approach increases sensitivity and specificity for SV discovery, particularly for variants above 1kb and when discovering variation from ONT reads, while having runtime that are comparable (1.05-3.76×) to current methods. When applied to calling variation from de novo assembly contigs, there is a 3.2% increase in Truvari F1 score compared to minimap2+htsbox. lra is available in bioconda (https://anaconda.org/bioconda/lra) and github (https://github.com/ChaissonLab/LRA).

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De novo Nanopore read quality improvement using deep learning

BMC Bioinformatics ◽

10.1186/s12859-019-3103-z ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 4

Author(s):

Nathan LaPierre ◽

Rob Egan ◽

Wei Wang ◽

Zhong Wang

Keyword(s):

Error Correction ◽

Genome Assembly ◽

Large Scale ◽

De Novo ◽

Error Rates ◽

De Novo Genome Assembly ◽

Sequencing Technologies ◽

Oxford Nanopore ◽

Long Read ◽

Read Error Correction

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 a tool for preprocessing Nanopore reads for downstream analyses. MiniScrub is open-source software and is available at https://bitbucket.org/berkeleylab/jgi-miniscrub.

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