Oxford Nanopore sequencing: new opportunities for plant genomics?

Abstract DNA sequencing was dominated by Sanger’s chain termination method until the mid-2000s, when it was progressively supplanted by new sequencing technologies that can generate much larger quantities of data in a shorter time. At the forefront of these developments, long-read sequencing technologies (third-generation sequencing) can produce reads that are several kilobases in length. This greatly improves the accuracy of genome assemblies by spanning the highly repetitive segments that cause difficulty for second-generation short-read technologies. Third-generation sequencing is especially appealing for plant genomes, which can be extremely large with long stretches of highly repetitive DNA. Until recently, the low basecalling accuracy of third-generation technologies meant that accurate genome assembly required expensive, high-coverage sequencing followed by computational analysis to correct for errors. However, today’s long-read technologies are more accurate and less expensive, making them the method of choice for the assembly of complex genomes. Oxford Nanopore Technologies (ONT), a third-generation platform for the sequencing of native DNA strands, is particularly suitable for the generation of high-quality assemblies of highly repetitive plant genomes. Here we discuss the benefits of ONT, especially for the plant science community, and describe the issues that remain to be addressed when using ONT for plant genome sequencing.

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HASLR: Fast Hybrid Assembly of Long Reads

10.1101/2020.01.27.921817 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ehsan Haghshenas ◽

Hossein Asghari ◽

Jens Stoye ◽

Cedric Chauve ◽

Faraz Hach

Keyword(s):

Unmet Need ◽

Effective Length ◽

Third Generation ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

The One ◽

Generation Sequencing

AbstractThird generation sequencing technologies from platforms such as Oxford Nanopore Technologies and Pacific Biosciences have paved the way for building more contiguous assemblies and complete reconstruction of genomes. The larger effective length of the reads generated with these technologies has provided a mean to overcome the challenges of short to mid-range repeats. Currently, accurate long read assemblers are computationally expensive while faster methods are not as accurate. Therefore, there is still an unmet need for tools that are both fast and accurate for reconstructing small and large genomes. Despite the recent advances in third generation sequencing, researchers tend to generate second generation reads for many of the analysis tasks. Here, we present HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples.AvailabilityHASLR is an open source tool available at https://github.com/vpc-ccg/haslr.

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RNA Transcriptome Mapping with GraphMap

10.1101/160085 ◽

2017 ◽

Cited By ~ 1

Author(s):

Krešimir Križanović ◽

Ivan Sović ◽

Ivan Krpelnik ◽

Mile Šikić

Keyword(s):

Third Generation ◽

Sequencing Data ◽

Mapping Algorithm ◽

Gene Annotations ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Oxford Nanopore ◽

Rna Mapping ◽

Synthetic Datasets ◽

Generation Sequencing

AbstractNext generation sequencing technologies have made RNA sequencing widely accessible and applicable in many areas of research. In recent years, 3rd generation sequencing technologies have matured and are slowly replacing NGS for DNA sequencing. This paper presents a novel tool for RNA mapping guided by gene annotations. The tool is an adapted version of a previously developed DNA mapper – GraphMap, tailored for third generation sequencing data, such as those produced by Pacific Biosciences or Oxford Nanopore Technologies devices. It uses gene annotations to generate a transcriptome, uses a DNA mapping algorithm to map reads to the transcriptome, and finally transforms the mappings back to genome coordinates. Modified version of GraphMap is compared on several synthetic datasets to the state-of-the-art RNAseq mappers enabled to work with third generation sequencing data. The results show that our tool outperforms other tools in general mapping quality.

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LongQC: A Quality Control Tool for Third Generation Sequencing Long Read Data

G3 Genes|Genome|Genetics ◽

10.1534/g3.119.400864 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1193-1196

Author(s):

Yoshinori Fukasawa ◽

Luca Ermini ◽

Hai Wang ◽

Karen Carty ◽

Min-Sin Cheung

Keyword(s):

Quality Control ◽

Third Generation ◽

Third Generation Sequencing ◽

Oxford Nanopore ◽

Quality Control Tool ◽

Long Read ◽

Automated Quality Control ◽

Oxford Nanopore Technologies ◽

Generation Sequencing ◽

Control Tool

We propose LongQC as an easy and automated quality control tool for genomic datasets generated by third generation sequencing (TGS) technologies such as Oxford Nanopore technologies (ONT) and SMRT sequencing from Pacific Bioscience (PacBio). Key statistics were optimized for long read data, and LongQC covers all major TGS platforms. LongQC processes and visualizes those statistics automatically and quickly.

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Benchmarking of long-read correction methods

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa037 ◽

2020 ◽

Vol 2 (2) ◽

Cited By ~ 2

Author(s):

Juliane C Dohm ◽

Philipp Peters ◽

Nancy Stralis-Pavese ◽

Heinz Himmelbauer

Keyword(s):

Error Rate ◽

Total Error ◽

Error Rates ◽

High Rate ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Oxford Nanopore ◽

Long Read ◽

Oxford Nanopore Technologies ◽

Generation Sequencing

Abstract Third-generation sequencing technologies provided by Pacific Biosciences and Oxford Nanopore Technologies generate read lengths in the scale of kilobasepairs. However, these reads display high error rates, and correction steps are necessary to realize their great potential in genomics and transcriptomics. Here, we compare properties of PacBio and Nanopore data and assess correction methods by Canu, MARVEL and proovread in various combinations. We found total error rates of around 13% in the raw datasets. PacBio reads showed a high rate of insertions (around 8%) whereas Nanopore reads showed similar rates for substitutions, insertions and deletions of around 4% each. In data from both technologies the errors were uniformly distributed along reads apart from noisy 5′ ends, and homopolymers appeared among the most over-represented kmers relative to a reference. Consensus correction using read overlaps reduced error rates to about 1% when using Canu or MARVEL after patching. The lowest error rate in Nanopore data (0.45%) was achieved by applying proovread on MARVEL-patched data including Illumina short-reads, and the lowest error rate in PacBio data (0.42%) was the result of Canu correction with minimap2 alignment after patching. Our study provides valuable insights and benchmarks regarding long-read data and correction methods.

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Evaluating approaches to find exon chains based on long reads

10.1101/066241 ◽

2016 ◽

Author(s):

Anna Kuosmanen ◽

Veli Mäkinen

Keyword(s):

Second Generation ◽

Simulated Data ◽

Error Rates ◽

Third Generation ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Long Reads ◽

Long Read ◽

Second Generation Sequencing ◽

Generation Sequencing

AbstractMotivationTranscript prediction can be modelled as a graph problem where exons are modelled as nodes and reads spanning two or more exons are modelled as exon chains. PacBio third-generation sequencing technology produces significantly longer reads than earlier second-generation sequencing technologies, which gives valuable information about longer exon chains in a graph. However, with the high error rates of third-generation sequencing, aligning long reads correctly around the splice sites is a challenging task. Incorrect alignments lead to spurious nodes and arcs in the graph, which in turn lead to incorrect transcript predictions.ResultsWe survey several approaches to find the exon chains corresponding to long reads in a splicing graph, and experimentally study the performance of these methods using simulated data to allow for sensitivity / precision analysis. Our experiments show that short reads from second-generation sequencing can be used to significantly improve exon chain correctness either by error-correcting the long reads before splicing graph creation, or by using them to create a splicing graph on which the long read alignments are then projected. We also study the memory and time consumption of various modules, and show that accurate exon chains lead to significantly increased transcript prediction accuracy.AvailabilityThe simulated data and in-house scripts used for this article are available at http://cs.helsinki.fi/u/aekuosma/exon_chain_evaluation_publish.tar.gz.

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New insights on Pseudoalteromonas haloplanktis TAC125 genome organization and benchmarks of genome assembly applications using next and third generation sequencing technologies

Scientific Reports ◽

10.1038/s41598-019-52832-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Weihong Qi ◽

Andrea Colarusso ◽

Miriam Olombrada ◽

Ermenegilda Parrilli ◽

Andrea Patrignani ◽

...

Keyword(s):

Genome Assembly ◽

Model Organisms ◽

Nanopore Sequencing ◽

Third Generation ◽

Small Plasmid ◽

Pseudoalteromonas Haloplanktis ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Oxford Nanopore ◽

Generation Sequencing

Abstract Pseudoalteromonas haloplanktis TAC125 is among the most commonly studied bacteria adapted to cold environments. Aside from its ecological relevance, P. haloplanktis has a potential use for biotechnological applications. Due to its importance, we decided to take advantage of next generation sequencing (Illumina) and third generation sequencing (PacBio and Oxford Nanopore) technologies to resequence its genome. The availability of a reference genome, obtained using whole genome shotgun sequencing, allowed us to study and compare the results obtained by the different technologies and draw useful conclusions for future de novo genome assembly projects. We found that assembly polishing using Illumina reads is needed to achieve a consensus accuracy over 99.9% when using Oxford Nanopore sequencing, but not in PacBio sequencing. However, the dependency of consensus accuracy on coverage is lower in Oxford Nanopore than in PacBio, suggesting that a cost-effective solution might be the use of low coverage Oxford Nanopore sequencing together with Illumina reads. Despite the differences in consensus accuracy, all sequencing technologies revealed the presence of a large plasmid, pMEGA, which was undiscovered until now. Among the most interesting features of pMEGA is the presence of a putative error-prone polymerase regulated through the SOS response. Aside from the characterization of the newly discovered plasmid, we confirmed the sequence of the small plasmid pMtBL and uncovered the presence of a potential partitioning system. Crucially, this study shows that the combination of next and third generation sequencing technologies give us an unprecedented opportunity to characterize our bacterial model organisms at a very detailed level.

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Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm

Bioinformatics ◽

10.1093/bioinformatics/btaa179 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3669-3679 ◽

Cited By ~ 3

Author(s):

Can Firtina ◽

Jeremie S Kim ◽

Mohammed Alser ◽

Damla Senol Cali ◽

A Ercument Cicek ◽

...

Keyword(s):

Genome Analysis ◽

Supplementary Information ◽

Third Generation ◽

Sequencing Technology ◽

Base Pairs ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Long Reads ◽

Generation Sequencing ◽

Large Genomes

Abstract Motivation Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward–Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts. Availability and implementation Source code is available at https://github.com/CMU-SAFARI/Apollo. Supplementary information Supplementary data are available at Bioinformatics online.

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