Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

AbstractOxford Nanopore (ONT) is a leading long-read technology which has been revolutionizing transcriptome analysis through its capacity to sequence the majority of transcripts from end-to-end. This has greatly increased our ability to study the diversity of transcription mechanisms such as transcription initiation, termination, and alternative splicing. However, ONT still suffers from high error rates which have thus far limited its scope to reference-based analyses. When a reference is not available or is not a viable option due to reference-bias, error correction is a crucial step towards the reconstruction of the sequenced transcripts and downstream sequence analysis of transcripts. In this paper, we present a novel computational method to error correct ONT cDNA sequencing data, called isONcorrect. IsONcorrect is able to jointly use all isoforms from a gene during error correction, thereby allowing it to correct reads at low sequencing depths. We are able to obtain a median accuracy of 98.9–99.6%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.

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Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

10.1101/2020.01.07.897512 ◽

2020 ◽

Cited By ~ 3

Author(s):

Kristoffer Sahlin ◽

Botond Sipos ◽

Phillip L James ◽

Paul Medvedev

Keyword(s):

Error Correction ◽

Transcriptome Analysis ◽

Transcription Initiation ◽

Cost Effective ◽

Error Rates ◽

Computational Method ◽

Bias Error ◽

Sequencing Data ◽

Oxford Nanopore ◽

Long Read

AbstractOxford Nanopore (ONT) is a leading long-read technology which has been revolutionizing transcriptome analysis through its capacity to sequence the majority of transcripts from end-to-end. This has greatly increased our ability to study the diversity of transcription mechanisms such as transcription initiation, termination, and alternative splicing. However, ONT still suffers from high error rates which have thus far limited its scope to reference-based analyses. When a reference is not available or is not a viable option due to reference-bias, error correction is a crucial step towards the reconstruction of the sequenced transcripts and downstream sequence analysis of transcripts. In this paper, we present a novel computational method to error correct ONT cDNA sequencing data, called isONcorrect. IsONcorrect is able to jointly use all isoforms from a gene during error correction, thereby allowing it to correct reads at low sequencing depths. We are able to obtain a median accuracy of 98.9-99.6%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.

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Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbz058 ◽

2019 ◽

Vol 21 (4) ◽

pp. 1164-1181 ◽

Cited By ~ 9

Author(s):

Leandro Lima ◽

Camille Marchet ◽

Ségolène Caboche ◽

Corinne Da Silva ◽

Benjamin Istace ◽

...

Keyword(s):

Error Correction ◽

Rna Sequencing ◽

Gene Families ◽

Error Rates ◽

Open Reading Frames ◽

Sequencing Data ◽

Isoform Diversity ◽

Long Reads ◽

Long Read ◽

Read Error Correction

Abstract Motivation Nanopore long-read sequencing technology offers promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However this technology is currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames and creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error correction of Nanopore RNA-sequencing long reads remain limited. Results In this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error correction metrics but also the effect of correction on gene families, isoform diversity, bias toward the major isoform and splice site detection. We find that long read error correction tools that were originally developed for DNA are also suitable for the correction of Nanopore RNA-sequencing data, especially in terms of increasing base pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error correction tools should be used, depending on the application type. Benchmarking software https://gitlab.com/leoisl/LR_EC_analyser

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Comparative assessment of long-read error-correction software applied to RNA-sequencing data

10.1101/476622 ◽

2018 ◽

Cited By ~ 2

Author(s):

Leandro Lima ◽

Camille Marchet ◽

Ségolène Caboche ◽

Corinne Da Silva ◽

Benjamin Istace ◽

...

Keyword(s):

Error Correction ◽

Rna Sequencing ◽

Gene Families ◽

Error Rates ◽

Open Reading Frames ◽

Sequencing Data ◽

Sequencing Technologies ◽

Isoform Diversity ◽

Long Read ◽

Read Error Correction

AbstractMotivationLong-read sequencing technologies offer promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However these technologies are currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames, and the creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error-correction of RNA-sequencing long reads remain limited.ResultsIn this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error-correction metrics but also the effect of correction on gene families, isoform diversity, bias towards the major isoform, and splice site detection. We find that long read error-correction tools that were originally developed for DNA are also suitable for the correction of RNA-sequencing data, especially in terms of increasing base-pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error-correction tools should be used, depending on the application type.Benchmarking softwarehttps://gitlab.com/leoisl/LR_EC_analyser

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Detecting and phasing minor single-nucleotide variants from long-read sequencing data

Nature Communications ◽

10.1038/s41467-021-23289-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Zhixing Feng ◽

Jose C. Clemente ◽

Brandon Wong ◽

Eric E. Schadt

Keyword(s):

Genetic Heterogeneity ◽

Error Rates ◽

Metagenomic Data ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Sequencing Technologies ◽

Oxford Nanopore ◽

Long Read ◽

Technological Limitations

AbstractCellular genetic heterogeneity is common in many biological conditions including cancer, microbiome, and co-infection of multiple pathogens. Detecting and phasing minor variants play an instrumental role in deciphering cellular genetic heterogeneity, but they are still difficult tasks because of technological limitations. Recently, long-read sequencing technologies, including those by Pacific Biosciences and Oxford Nanopore, provide an opportunity to tackle these challenges. However, high error rates make it difficult to take full advantage of these technologies. To fill this gap, we introduce iGDA, an open-source tool that can accurately detect and phase minor single-nucleotide variants (SNVs), whose frequencies are as low as 0.2%, from raw long-read sequencing data. We also demonstrate that iGDA can accurately reconstruct haplotypes in closely related strains of the same species (divergence ≥0.011%) from long-read metagenomic data.

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Implications of error-prone long-read whole-genome shotgun sequencing on characterizing reference microbiomes

10.1101/2020.03.05.978866 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yu Hu ◽

Li Fang ◽

Christopher Nicholson ◽

Kai Wang

Keyword(s):

Human Microbiome ◽

Human Microbiome Project ◽

Error Rates ◽

Tool Development ◽

Sequencing Data ◽

Bacterial Strains ◽

Microbial Genomes ◽

Oxford Nanopore ◽

Long Read ◽

Adequate Coverage

SummaryLong-read sequencing techniques, such as the Oxford Nanopore Technology, can generate reads that are tens of kilobases in length, and are therefore particularly relevant for microbiome studies. However, due to the higher per-base error rates than typical short-read sequencing, the application of long-read sequencing on microbiomes remains largely unexplored. Here we deeply sequenced two human microbiota mock community samples (HM-276D and HM-277D) from the Human Microbiome Project. We showed that assembly programs consistently achieved high accuracy (~99%) and completeness (~99%) for bacterial strains with adequate coverage. We also found that long-read sequencing provides accurate estimates of species-level abundance (R=0.94 for 20 bacteria with abundance ranging from 0.005% to 64%). Our results demonstrate the feasibility to characterize complete microbial genomes and populations from error-prone Nanopore sequencing data, but also highlight necessary bioinformatics improvements for future metagenomics tool development.

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Detecting and phasing minor single-nucleotide variants from long-read sequencing data

10.1101/2020.09.25.314252 ◽

2020 ◽

Author(s):

Zhixing Feng ◽

Jose Clemente ◽

Brandon Wong ◽

Eric E. Schadt

Keyword(s):

Genetic Heterogeneity ◽

Error Rates ◽

Metagenomic Data ◽

Significant Advance ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Sequencing Technologies ◽

Oxford Nanopore ◽

Long Read

AbstractCellular genetic heterogeneity is common in many biological conditions including cancer, microbiome, co-infection of multiple pathogens. Detecting and phasing minor variants, which is to determine whether multiple variants are from the same haplotype, play an instrumental role in deciphering cellular genetic heterogeneity, but are still difficult because of technological limitations. Recently, long-read sequencing technologies, including those by Pacific Biosciences and Oxford Nanopore, have provided an unprecedented opportunity to tackle these challenges. However, high error rates make it difficult to take full advantage of these technologies. To fill this gap, we introduce iGDA, an open-source tool that can accurately detect and phase minor single-nucleotide variants (SNVs), whose frequencies are as low as 0.2%, from raw long-read sequencing data. We also demonstrated that iGDA can accurately reconstruct haplotypes in closely-related strains of the same species (divergence ≥ 0.011%) from long-read metagenomic data. Our approach, therefore, presents a significant advance towards the complete deciphering of cellular genetic heterogeneity.

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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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Comparison of long-read methods for sequencing and assembly of a plant genome

GigaScience ◽

10.1093/gigascience/giaa146 ◽

2020 ◽

Vol 9 (12) ◽

Author(s):

Valentine Murigneux ◽

Subash Kumar Rai ◽

Agnelo Furtado ◽

Timothy J C Bruxner ◽

Wei Tian ◽

...

Keyword(s):

De Novo ◽

Cost Effective ◽

Genome Project ◽

Plant Genome ◽

Sequencing Data ◽

Pacific Biosciences ◽

Sequencing Technologies ◽

Oxford Nanopore ◽

Long Read ◽

The Cost

Abstract Background Sequencing technologies have advanced to the point where it is possible to generate high-accuracy, haplotype-resolved, chromosome-scale assemblies. Several long-read sequencing technologies are available, and a growing number of algorithms have been developed to assemble the reads generated by those technologies. When starting a new genome project, it is therefore challenging to select the most cost-effective sequencing technology, as well as the most appropriate software for assembly and polishing. It is thus important to benchmark different approaches applied to the same sample. Results Here, we report a comparison of 3 long-read sequencing technologies applied to the de novo assembly of a plant genome, Macadamia jansenii. We have generated sequencing data using Pacific Biosciences (Sequel I), Oxford Nanopore Technologies (PromethION), and BGI (single-tube Long Fragment Read) technologies for the same sample. Several assemblers were benchmarked in the assembly of Pacific Biosciences and Nanopore reads. Results obtained from combining long-read technologies or short-read and long-read technologies are also presented. The assemblies were compared for contiguity, base accuracy, and completeness, as well as sequencing costs and DNA material requirements. Conclusions The 3 long-read technologies produced highly contiguous and complete genome assemblies of M. jansenii. At the time of sequencing, the cost associated with each method was significantly different, but continuous improvements in technologies have resulted in greater accuracy, increased throughput, and reduced costs. We propose updating this comparison regularly with reports on significant iterations of the sequencing technologies.

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A MinION-based pipeline for fast and cost-effective DNA barcoding

10.1101/253625 ◽

2018 ◽

Cited By ~ 4

Author(s):

Amrita Srivathsan ◽

Bilgenur Baloğlu ◽

Wendy Wang ◽

Wei Xin Tan ◽

Denis Bertrand ◽

...

Keyword(s):

Amino Acid ◽

Error Correction ◽

Dna Barcoding ◽

Cost Effective ◽

Error Rates ◽

Dna Barcodes ◽

Species Discovery ◽

Oxford Nanopore ◽

High Base ◽

The Cost

ABSTRACTDNA barcodes are useful for species discovery and species identification, but obtaining barcodes currently requires a well-equipped molecular laboratory, is time-consuming, and/or expensive. We here address these issues by developing a barcoding pipeline for Oxford Nanopore MinION™ and demonstrate that one flowcell can generate barcodes for ∼500 specimens despite high base-call error rates of MinION™. The pipeline overcomes the errors by first summarizing all reads for the same tagged amplicon as a consensus barcode. These barcodes are overall mismatch-free but retain indel errors that are concentrated in homopolymeric regions. We thus complement the barcode caller with an optional error correction pipeline that uses conserved amino-acid motifs from publicly available barcodes to correct the indel errors. The effectiveness of this pipeline is documented by analysing reads from three MinION™ runs that represent three different stages of MinION™ development. They generated data for (1) 511 specimens of a mixed Diptera sample, (2) 575 specimens of ants, and (3) 50 specimens of Chironomidae. The run based on the latest chemistry yielded MinION barcodes for 490 specimens which were assessed against reference Sanger barcodes (N=471). Overall, the MinION barcodes have an accuracy of 99.3%-100% and the number of ambiguities ranges from <0.01-1.5% depending on which correction pipeline is used. We demonstrate that it requires only 2 hours of sequencing to gather all information that is needed for obtaining reliable barcodes for most specimens (>90%). We estimate that up to 1000 barcodes can be generated in one flowcell and that the cost of a MinION barcode can be <USD 2.

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NGSpeciesID: DNA barcode and amplicon consensus generation from long-read sequencing data

10.22541/au.160262406.62842291/v2 ◽

2020 ◽

Author(s):

Kristoffer Sahlin ◽

Marisa Lim ◽

Stefan Prost

Keyword(s):

High Throughput Sequencing ◽

Dna Barcode ◽

Amplicon Sequencing ◽

Error Rates ◽

Sequencing Data ◽

Sequencing Platform ◽

Consensus Sequences ◽

Sequencing Technologies ◽

Oxford Nanopore ◽

Long Read

Third generation sequencing technologies, such as Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), have gained popularity over the last years. These platforms can generate millions of long read sequences. This is not only advantageous for genome sequencing projects, but also for amplicon-based high-throughput sequencing experiments, such as DNA barcoding. However, the relatively high error rates associated with these technologies still pose challenges for generating high quality consensus sequences. Here we present NGSpeciesID, a program which can generate highly accurate consensus sequences from long-read amplicon sequencing technologies, including ONT and PacBio. The tool includes clustering of the reads to help filter out contaminants or reads with high error rates and employs polishing strategies specific to the appropriate sequencing platform. We show that NGSpeciesID produces consensus sequences with improved usability by minimizing preprocessing and software installation and scalability by enabling rapid processing of hundreds to thousands of samples, while maintaining similar consensus accuracy as current pipelines

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