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

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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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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Combined use of Oxford Nanopore and Illumina sequencing yields insights into soybean structural variation biology

10.1101/2021.08.26.457816 ◽

2021 ◽

Author(s):

Marc-André Lemay ◽

Jonas A. Sibbesen ◽

Davoud Torkamaneh ◽

Jérémie Hamel ◽

Roger C. Levesque ◽

...

Keyword(s):

Structural Variation ◽

Pfam Domain ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Short Read ◽

Sequencing Technologies ◽

Oxford Nanopore ◽

Population Structure Analysis ◽

Illumina Data ◽

Long Read

Background: Structural variant (SV) discovery based on short reads is challenging due to their complex signatures and tendency to occur in repeated regions. The increasing availability of long-read technologies has greatly facilitated SV discovery, however these technologies remain too costly to apply routinely to population-level studies. Here, we combined short-read and long-read sequencing technologies to provide a comprehensive population-scale assessment of structural variation in a panel of Canadian soybean cultivars. Results: We used Oxford Nanopore sequencing data (~12X mean coverage) for 17 samples to both benchmark SV calls made from the Illumina data and predict SVs that were subsequently genotyped in a population of 102 samples using Illumina data. Benchmarking results show that variants discovered using Oxford Nanopore can be accurately genotyped from the Illumina data. We first use the genotyped SVs for population structure analysis and show that results are comparable to those based on single-nucleotide variants. We observe that the population frequency and distribution within the genome of SVs are constrained by the location of genes. Gene Ontology and PFAM domain enrichment analyses also confirm previous reports that genes harboring high-frequency SVs are enriched for functions in defense response. Finally, we discover polymorphic transposable elements from the SVs and report evidence of the recent activity of a Stowaway MITE. Conclusions: Our results demonstrate that long-read and short-read sequencing technologies can be efficiently combined to enhance SV analysis in large populations, providing a reusable framework for their study in a wider range of samples and non-model species.

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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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1202. Multimodal Sequencing of a Clonal Case Cluster of Carbapenem-Resistant Citrobacter Reveals Unexpectedly Rapid Dynamics of KPC3-Containing Plasmids

Open Forum Infectious Diseases ◽

10.1093/ofid/ofy210.1035 ◽

2018 ◽

Vol 5 (suppl_1) ◽

pp. S364-S364

Author(s):

Roby Bhattacharyya ◽

Alejandro Pironti ◽

Bruce J Walker ◽

Abigail Manson ◽

Virginia Pierce ◽

...

Keyword(s):

Point Mutations ◽

Illumina Miseq ◽

Nucleotide Polymorphisms ◽

Sequencing Data ◽

Single Nucleotide ◽

Carbapenem Resistant ◽

Oxford Nanopore ◽

Close Relationship ◽

Long Read ◽

Carbapenem Resistant Enterobacteriaceae

Abstract Background Carbapenem-resistant Enterobacteriaceae (CRE) are a major public health threat. We report four clonally related Citrobacter freundii isolates harboring the blaKPC-3 carbapenemase in April–May 2017 that are nearly identical to a strain from 2014 at the same institution. Despite differing by ≤5 single nucleotide polymorphisms (SNPs), these isolates exhibited dramatic differences in carbapenemase plasmid architecture. Methods We sequenced four carbapenem-resistant C. freundii isolates from 2017 and compared them with an ongoing CRE surveillance project at our institution. SNPs were identified from Illumina MiSeq data aligned to a reference genome using the variant caller Pilon. Plasmids were assembled from Illumina and Oxford Nanopore sequencing data using Unicycler. Results The four 2017 isolates differed from one another by 0–5 chromosomal SNPs; two were identical. With one exception, these isolates differed by >38,000 SNPs from 25 C. freundii isolates sequenced from 2013 to 2017 at the same institution for CRE surveillance. The exception was a 2014 isolate that differed by 13–16 SNPs from each 2017 isolate, with 13 SNPs common to all four. Each C. freundii isolate harbored wild-type blaKPC-3. Despite the close relationship among the 2017 cluster, the plasmids harboring the blaKPC-3 genes differed dramatically: the carbapenemase occurred in one of the two different plasmids, with rearrangements between these plasmids across isolates. The related 2014 isolate harbored both plasmids, each with a separate copy of blaKPC-3. No transmission chains were found between any of the affected patients. Conclusion WGS confirmed clonality among four contemporaneous blaKPC-3-containing C. freundii isolates, and marked similarity with a 2014 isolate, within an institution. That only 13–16 SNPs varied between the 2014 and 2017 isolates suggests durable persistence of the blaKPC-3 gene within this lineage in a hospital ecosystem. The plasmids harboring these carbapenemase genes proved remarkably plastic, with plasmid loss and rearrangements occurring on the same time scale as two to three chromosomal point mutations. Combining short and long-read sequencing in a case cluster uniquely revealed unexpectedly rapid dynamics of carbapenemase plasmids, providing critical insight into their manner of spread. Disclosures M. J. Ferraro, SeLux Diagnostics: Scientific Advisor and Shareholder, Consulting fee. D. C. Hooper, SeLux Diagnostics: Scientific Advisor, Consulting fee.

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Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads

Nature Biotechnology ◽

10.1038/s41587-020-0719-5 ◽

2020 ◽

Author(s):

David Porubsky ◽

◽

Peter Ebert ◽

Peter A. Audano ◽

Mitchell R. Vollger ◽

...

Keyword(s):

Single Cell ◽

Genome Assembly ◽

De Novo ◽

Error Rates ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

De Novo Genome Assembly ◽

Parental Data ◽

Human Genome Assembly ◽

Long Read

AbstractHuman genomes are typically assembled as consensus sequences that lack information on parental haplotypes. Here we describe a reference-free workflow for diploid de novo genome assembly that combines the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing1,2 with continuous long-read or high-fidelity3 sequencing data. Employing this strategy, we produced a completely phased de novo genome assembly for each haplotype of an individual of Puerto Rican descent (HG00733) in the absence of parental data. The assemblies are accurate (quality value > 40) and highly contiguous (contig N50 > 23 Mbp) with low switch error rates (0.17%), providing fully phased single-nucleotide variants, indels and structural variants. A comparison of Oxford Nanopore Technologies and Pacific Biosciences phased assemblies identified 154 regions that are preferential sites of contig breaks, irrespective of sequencing technology or phasing algorithms.

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Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Frontiers in Genetics ◽

10.3389/fgene.2021.761791 ◽

2021 ◽

Vol 12 ◽

Author(s):

Davide Bolognini ◽

Alberto Magi

Keyword(s):

Variant Calling ◽

Research Report ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Factors Affecting ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Sequencing Studies ◽

Long Read

Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

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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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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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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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Near-complete Lokiarchaeota genomes from complex environmental samples using long and short read metagenomic analyses

10.1101/2019.12.17.879148 ◽

2019 ◽

Cited By ~ 3

Author(s):

Eva F. Caceres ◽

William H. Lewis ◽

Felix Homa ◽

Tom Martin ◽

Andreas Schramm ◽

...

Keyword(s):

Large Scale ◽

Phylogenetic Analyses ◽

Metagenomic Data ◽

Endosomal Sorting ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Complete Genomes ◽

Culture Independent ◽

Long Read

AbstractAsgard archaea is a recently proposed superphylum currently comprised of five recognised phyla: Lokiarchaeota, Thorarchaeota, Odinarchaeota, Heimdallarchaeota and Helarchaeota. Members of this group have been identified based on culture-independent approaches with several metagenome-assembled genomes (MAGs) reconstructed to date. However, most of these genomes consist of several relatively small contigs, and, until recently, no complete Asgard archaea genome is yet available. Large scale phylogenetic analyses suggest that Asgard archaea represent the closest archaeal relatives of eukaryotes. In addition, members of this superphylum encode proteins that were originally thought to be specific to eukaryotes, including components of the trafficking machinery, cytoskeleton and endosomal sorting complexes required for transport (ESCRT). Yet, these findings have been questioned on the basis that the genome sequences that underpin them were assembled from metagenomic data, and could have been subjected to contamination and other assembly artefacts. Even though several lines of evidence indicate that the previously reported findings were not affected by these issues, having access to high-quality and preferentially fully closed Asgard archaea genomes is needed to definitively close this debate. Current long-read sequencing technologies such as Oxford Nanopore allow the generation of long reads in a high-throughput manner making them suitable for their use in metagenomics. Although the use of long reads is still limited in this field, recent analyses have shown that it is feasible to obtain complete or near-complete genomes of abundant members of mock communities and metagenomes of various level of complexity. Here, we show that long read metagenomics can be successfully applied to obtain near-complete genomes of low-abundant members of complex communities from sediment samples. We were able to reconstruct six MAGs from different Lokiarchaeota lineages that show high completeness and low fragmentation, with one of them being a near-complete genome only consisting of three contigs. Our analyses confirm that the eukaryote-like features previously associated with Lokiarchaeota are not the result of contamination or assembly artefacts, and can indeed be found in the newly reconstructed genomes.

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