Accurate detection of complex structural variations using single molecule sequencing

AbstractStructural variations (SVs) are the largest source of genetic variation, but remain poorly understood because of limited genomics technology. Single molecule long read sequencing from Pacific Biosciences and Oxford Nanopore has the potential to dramatically advance the field, although their high error rates challenge existing methods. Addressing this need, we introduce open-source methods for long read alignment (NGMLR, https://github.com/philres/ngmlr) and SV identification (Sniffles, https://github.com/fritzsedlazeck/Sniffles) that enable unprecedented SV sensitivity and precision, including within repeat-rich regions and of complex nested events that can have significant impact on human disorders. Examining several datasets, including healthy and cancerous human genomes, we discover thousands of novel variants using long reads and categorize systematic errors in short-read approaches. NGMLR and Sniffles are further able to automatically filter false events and operate on low amounts of coverage to address the cost factor that has hindered the application of long reads in clinical and research settings.

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lra: the Long Read Aligner for Sequences and Contigs

10.1101/2020.11.15.383273 ◽

2020 ◽

Author(s):

Jingwen Ren ◽

Mark JP Chaisson

Keyword(s):

Single Molecule ◽

De Novo ◽

Data Types ◽

Single Molecule Sequencing ◽

Detection Algorithms ◽

Link Type ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

Linear Cost

AbstractMotivationIt is computationally challenging to detect variation by aligning long reads from single-molecule sequencing (SMS) instruments, or megabase-scale contigs from SMS assemblies. One approach to efficiently align long sequences is sparse dynamic programming (SDP), where exact matches are found between the sequence and the genome, and optimal chains of matches are found representing a rough alignment. Sequence variation is more accurately modeled when alignments are scored with a gap penalty that is a convex function of the gap length. Because previous implementations of SDP used a linear-cost gap function that does not accurately model variation, and implementations of alignment that have a convex gap penalty are either inefficient or use heuristics, we developed a method, lra, that uses SDP with a convex-cost gap penalty. We use lra to align long-read sequences from PacBio and Oxford Nanopore (ONT) instruments as well as de novo assembly contigs.ResultsAcross all data types, the runtime of lra is between 52-168% of the state of the art aligner minimap2 when generating SAM alignment, and 9-15% of an alternative method, ngmlr. This alignment approach may be used to provide additional evidence of SV calls in PacBio datasets, and an increase in sensitivity and specificity on ONT data with current SV detection algorithms. The number of calls discovered using pbsv with lra alignments are within 98.3-98.6% of calls made from minimap2 alignments on the same data, and give a nominal 0.2-0.4% increase in F1 score by Truvari analysis. On ONT data with SV called using Sniffles, the number of calls made from lra alignments is 3% greater than minimap2-based calls, and 30% greater than ngmlr based calls, with a 4.6-5.5% increase in Truvari F1 score. When applied to calling variation from de novo assembly contigs, there is a 5.8% increase in SV calls compared to minimap2+paftools, with a 4.3% increase in Truvari F1 score.Availability and implementationAvailable in bioconda: https://anaconda.org/bioconda/lra and github: https://github.com/ChaissonLab/[email protected], [email protected]

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Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab034 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Jean-Marc Aury ◽

Benjamin Istace

Keyword(s):

Single Molecule ◽

Direct Consequence ◽

High Quality ◽

Sequencing Errors ◽

Coding Regions ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

Genome Assemblies

Abstract Single-molecule sequencing technologies have recently been commercialized by Pacific Biosciences and Oxford Nanopore with the promise of sequencing long DNA fragments (kilobases to megabases order) and then, using efficient algorithms, provide high quality assemblies in terms of contiguity and completeness of repetitive regions. However, the error rate of long-read technologies is higher than that of short-read technologies. This has a direct consequence on the base quality of genome assemblies, particularly in coding regions where sequencing errors can disrupt the coding frame of genes. In the case of diploid genomes, the consensus of a given gene can be a mixture between the two haplotypes and can lead to premature stop codons. Several methods have been developed to polish genome assemblies using short reads and generally, they inspect the nucleotide one by one, and provide a correction for each nucleotide of the input assembly. As a result, these algorithms are not able to properly process diploid genomes and they typically switch from one haplotype to another. Herein we proposed Hapo-G (Haplotype-Aware Polishing Of Genomes), a new algorithm capable of incorporating phasing information from high-quality reads (short or long-reads) to polish genome assemblies and in particular assemblies of diploid and heterozygous genomes.

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PacBio library preparation using blunt-end adapter ligation produces significant artefactual fusion DNA sequences

10.1101/245241 ◽

2018 ◽

Author(s):

Paul Griffith ◽

Castle Raley ◽

David Sun ◽

Yongmei Zhao ◽

Zhonghe Sun ◽

...

Keyword(s):

Single Molecule ◽

Dna Sequences ◽

Turnaround Time ◽

Library Preparation ◽

Adapter Ligation ◽

Sequence Production ◽

Long Reads ◽

Complex Structural Variation ◽

Long Read ◽

Complex Structural

AbstractPacific Biosciences’ (PacBio) RS II sequencer, utilizing Single-Molecule, Real-Time (SMRT) technology, has revolutionized next-generation sequencing by providing an accurate long-read platform. PacBio single-molecule long reads have been used to delineate complex spliceoforms, detect mutations in highly homologous sequences, identify mRNA chimeras and chromosomal translocations, accurately haplotype phasing over multiple kilobase distances and aid in assembly of genomes with complex structural variation. The PacBio protocol for preparation of sequencing templates employs blunt-end hairpin adapter ligation, which enables a short turnaround time for sequence production. However, we have found a significant portion of sequencing yield contains chimeric reads resulting from blunt-end ligation of multiple template molecules to each other prior to adapter ligation. These artefactual fusion DNA sequences pose a major challenge to analysis and can lead to false-positive detection of fusion events. We assessed the frequency of artefactual fusion when using blunt-end adapter ligation and compared it to an alternative method using A/T overhang adapter ligation. The A/T overhang adapter ligation method showed a vast improvement in limiting artefactual fusion events and is now our recommended procedure for adapter ligation during PacBio library preparation.

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LINKS: Scaffolding genome assemblies with kilobase-long nanopore reads

10.1101/016519 ◽

2015 ◽

Cited By ~ 4

Author(s):

Rene L Warren ◽

Benjamin P Vandervalk ◽

Steven JM Jones ◽

Inanc Birol

Keyword(s):

Genome Assembly ◽

Error Rates ◽

Great Promise ◽

Genomic Information ◽

E Coli ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

K 12 ◽

Genome Assemblies

Owing to the complexity of the assembly problem, we do not yet have complete genome sequences. The difficulty in assembling reads into finished genomes is exacerbated by sequence repeats and the inability of short reads to capture sufficient genomic information to resolve those problematic regions. Established and emerging long read technologies show great promise in this regard, but their current associated higher error rates typically require computational base correction and/or additional bioinformatics pre-processing before they could be of value. We present LINKS, the Long Interval Nucleotide K-mer Scaffolder algorithm, a solution that makes use of the information in error-rich long reads, without the need for read alignment or base correction. We show how the contiguity of an ABySS E. coli K-12 genome assembly could be increased over five-fold by the use of beta-released Oxford Nanopore Ltd. (ONT) long reads and how LINKS leverages long-range information in S. cerevisiae W303 ONT reads to yield an assembly with less than half the errors of competing applications. Re-scaffolding the colossal white spruce assembly draft (PG29, 20 Gbp) and how LINKS scales to larger genomes is also presented. We expect LINKS to have broad utility in harnessing the potential of long reads in connecting high-quality sequences of small and large genome assembly drafts. Availability: http://www.bcgsc.ca/bioinfo/software/links

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De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm

10.1101/463463 ◽

2018 ◽

Cited By ~ 8

Author(s):

Kristoffer Sahlin ◽

Paul Medvedev

Keyword(s):

Clustering Algorithm ◽

De Novo ◽

Substantial Improvement ◽

Error Rates ◽

Reconstruction Algorithms ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

Transcript Reconstruction ◽

Oxford Nanopore Technologies

AbstractLong-read sequencing of transcripts with PacBio Iso-Seq and Oxford Nanopore Technologies has proven to be central to the study of complex isoform landscapes in many organisms. However, current de novo transcript reconstruction algorithms from long-read data are limited, leaving the potential of these technologies unfulfilled. A common bottleneck is the dearth of scalable and accurate algorithms for clustering long reads according to their gene family of origin. To address this challenge, we develop isONclust, a clustering algorithm that is greedy (in order to scale) and makes use of quality values (in order to handle variable error rates). We test isONclust on three simulated and five biological datasets, across a breadth of organisms, technologies, and read depths. Our results demonstrate that isONclust is a substantial improvement over previous approaches, both in terms of overall accuracy and/or scalability to large datasets. Our tool is available at https://github.com/ksahlin/isONclust.

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SVJedi: Genotyping structural variations with long reads

10.1101/849208 ◽

2019 ◽

Author(s):

Lolita Lecompte ◽

Pierre Peterlongo ◽

Dominique Lavenier ◽

Claire Lemaitre

Keyword(s):

Sequencing Data ◽

Structural Variations ◽

Short Read ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Clinical Diagnoses ◽

Long Read ◽

Reference Sequences ◽

The One

AbstractMotivationStudies on structural variants (SV) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, the number of discovered SVs is increasing, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it is important to genotype newly sequenced individuals on well defined and characterized SVs. Whereas several SV genotypers have been developed for short read data, there is a lack of such dedicated tool to assess whether known SVs are present or not in a new long read sequenced sample, such as the one produced by Pacific Biosciences or Oxford Nanopore Technologies.ResultsWe present a novel method to genotype known SVs from long read sequencing data. The method is based on the generation of a set of reference sequences that represent the two alleles of each structural variant. Long reads are aligned to these reference sequences. Alignments are then analyzed and filtered out to keep only informative ones, to quantify and estimate the presence of each SV allele and the allele frequencies. We provide an implementation of the method, SVJedi, to genotype insertions and deletions with long reads. The tool has been applied to both simulated and real human datasets and achieves high genotyping accuracy. We also demonstrate that SV genotyping is considerably improved with SVJedi compared to other approaches, namely SV discovery and short read SV genotyping approaches.Availabilityhttps://github.com/llecompte/[email protected]

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Quantifying the Benefit Offered by Transcript Assembly on Single-Molecule Long Reads

10.1101/632703 ◽

2019 ◽

Cited By ~ 1

Author(s):

Laura H. Tung ◽

Mingfu Shao ◽

Carl Kingsford

Keyword(s):

Single Molecule ◽

Error Rates ◽

Human Transcriptome ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Long Reads ◽

Long Read ◽

Transcript Assembly ◽

Novel Isoforms ◽

Generation Sequencing

AbstractThird-generation sequencing technologies benefit transcriptome analysis by generating longer sequencing reads. However, not all single-molecule long reads represent full transcripts due to incomplete cDNA synthesis and the sequencing length limit of the platform. This drives a need for long read transcript assembly. We quantify the benefit that can be achieved by using a transcript assembler on long reads. Adding long-read-specific algorithms, we evolved Scallop to make Scallop-LR, a long-read transcript assembler, to handle the computational challenges arising from long read lengths and high error rates. Analyzing 26 SRA PacBio datasets using Scallop-LR, Iso-Seq Analysis, and StringTie, we quantified the amount by which assembly improved Iso-Seq results. Through combined evaluation methods, we found that Scallop-LR identifies 2100–4000 more (for 18 human datasets) or 1100–2200 more (for eight mouse datasets) known transcripts than Iso-Seq Analysis, which does not do assembly. Further, Scallop-LR finds 2.4–4.4 times more potentially novel isoforms than Iso-Seq Analysis for the human and mouse datasets. StringTie also identifies more transcripts than Iso-Seq Analysis. Adding long-read-specific optimizations in Scallop-LR increases the numbers of predicted known transcripts and potentially novel isoforms for the human transcriptome compared to several recent short-read assemblers (e.g. StringTie). Our findings indicate that transcript assembly by Scallop-LR can reveal a more complete human transcriptome.

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Rapid multi-locus sequence typing direct from uncorrected long reads using Krocus

10.1101/259150 ◽

2018 ◽

Author(s):

Andrew J. Page ◽

Jacqueline A. Keane

Keyword(s):

Error Rates ◽

Multi Locus Sequence Typing ◽

Short Read Sequencing ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Standard Tool ◽

Long Read ◽

Sequence Types ◽

Very High

AbstractGenome sequencing is rapidly being adopted in reference labs and hospitals for bacterial outbreak investigation and diagnostics where time is critical. Seven gene multi-locus sequence typing is a standard tool for broadly classifying samples into sequence types, allowing, in many cases, to rule a sample in or out of an outbreak, or allowing for general characteristics about a bacterial strain to be inferred. Long read sequencing technologies, such as from PacBio or Oxford Nanopore, can produce read data within minutes of an experiment starting, unlike short read sequencing technologies which require many hours/days. However, the error rates of raw uncorrected long read data are very high. We present Krocus which can predict a sequence type directly from uncorrected long reads, and which was designed to consume read data as it is produced, providing results in minutes. It is the only tool which can do this from uncorrected long reads. We tested Krocus on over 600 samples sequenced with using long read sequencing technologies from PacBio and Oxford Nanopore. It provides sequence types on average within 90 seconds, with a sensitivity of 94% and specificity of 97%, directly from uncorrected raw sequence reads. The software is written in Python and is available under the open source license GNU GPL version 3.

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LongTron: Automated Analysis of Long Read Spliced Alignment Accuracy

10.1101/2020.11.10.376871 ◽

2020 ◽

Author(s):

Christopher Wilks ◽

Michael C. Schatz

Keyword(s):

Random Forest ◽

Cancer Cell Line ◽

Automated Analysis ◽

Error Rates ◽

Supplementary Information ◽

Splice Sites ◽

Link Type ◽

Spliced Alignment ◽

Oxford Nanopore ◽

Long Read

AbstractMotivationLong read sequencing has increased the accuracy and completeness of assemblies of various organisms’ genomes in recent months. Similarly, spliced alignments of long read RNA sequencing hold the promise of delivering much longer transcripts of existing and novel isoforms in known genes without the need for error-prone transcript assemblies from short reads. However, low coverage and high-error rates potentially hamper the widespread adoption of long-read spliced alignments in annotation updates and isoform-level expression quantifications.ResultsAddressing these issues, we first develop a simulation of error modes for both Oxford Nanopore and PacBio CCS spliced-alignments. Based on this we train a Random Forest classifier to assign new long-read alignments to one of two error categories, a novel category, or label them as non-error. We use this classifier to label reads from the spliced-alignments of the popular aligner minimap2, run on three long read sequencing datasets, including NA12878 from Oxford Nanopore and PacBio CCS, as well as a PacBio SKBR3 cancer cell line. Finally, we compare the intron chains of the three long read alignments against individual splice sites, short read assemblies, and the output from the FLAIR pipeline on the same samples.Our results demonstrate a substantial lack of precision in determining exact splice sites for long reads during alignment on both platforms while showing some benefit from postprocessing. This work motivates the need for both better aligners and additional post-alignment processing to adjust incorrectly called putative splice-sites and clarify novel transcripts support.Availability and implementationSource code for the random forest implemented in python is available at https://github.com/schatzlab/LongTron under the MIT license. The modified version of GffCompare used to construct Table 3 and related is here: https://github.com/ChristopherWilks/gffcompare/releases/tag/0.11.2LTSupplementary InformationSupplementary notes and figures are available online.

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Benchmarking Long-Read Assemblers for Genomic Analyses of Bacterial Pathogens Using Oxford Nanopore Sequencing

International Journal of Molecular Sciences ◽

10.3390/ijms21239161 ◽

2020 ◽

Vol 21 (23) ◽

pp. 9161

Author(s):

Zhao Chen ◽

David L. Erickson ◽

Jianghong Meng

Keyword(s):

Virulence Genes ◽

Bacterial Pathogens ◽

Error Rates ◽

Nanopore Sequencing ◽

Long Reads ◽

Oxford Nanopore ◽

Genomic Analyses ◽

Long Read ◽

Genome Analyses ◽

Assembly Algorithms

Oxford Nanopore sequencing can be used to achieve complete bacterial genomes. However, the error rates of Oxford Nanopore long reads are greater compared to Illumina short reads. Long-read assemblers using a variety of assembly algorithms have been developed to overcome this deficiency, which have not been benchmarked for genomic analyses of bacterial pathogens using Oxford Nanopore long reads. In this study, long-read assemblers, namely Canu, Flye, Miniasm/Racon, Raven, Redbean, and Shasta, were thus benchmarked using Oxford Nanopore long reads of bacterial pathogens. Ten species were tested for mediocre- and low-quality simulated reads, and 10 species were tested for real reads. Raven was the most robust assembler, obtaining complete and accurate genomes. All Miniasm/Racon and Raven assemblies of mediocre-quality reads provided accurate antimicrobial resistance (AMR) profiles, while the Raven assembly of Klebsiella variicola with low-quality reads was the only assembly with an accurate AMR profile among all assemblers and species. All assemblers functioned well for predicting virulence genes using mediocre-quality and real reads, whereas only the Raven assemblies of low-quality reads had accurate numbers of virulence genes. Regarding multilocus sequence typing (MLST), Miniasm/Racon was the most effective assembler for mediocre-quality reads, while only the Raven assemblies of Escherichia coli O157:H7 and K. variicola with low-quality reads showed positive MLST results. Miniasm/Racon and Raven were the best performers for MLST using real reads. The Miniasm/Racon and Raven assemblies showed accurate phylogenetic inference. For the pan-genome analyses, Raven was the strongest assembler for simulated reads, whereas Miniasm/Racon and Raven performed the best for real reads. Overall, the most robust and accurate assembler was Raven, closely followed by Miniasm/Racon.

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