SVIM: structural variant identification using mapped long reads

Abstract Motivation Structural variants are defined as genomic variants larger than 50 bp. They have been shown to affect more bases in any given genome than single-nucleotide polymorphisms or small insertions and deletions. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single-molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long-read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities. Results We present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long-read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from Pacific Biosciences and Nanopore sequencing machines. Availability and implementation The source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index. Supplementary information Supplementary data are available at Bioinformatics online.

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SVIM: Structural Variant Identification using Mapped Long Reads

10.1101/494096 ◽

2018 ◽

Cited By ~ 2

Author(s):

David Heller ◽

Martin Vingron

Keyword(s):

Single Molecule ◽

Simulated Data ◽

Structural Variants ◽

Human Phenotype ◽

Structural Variant ◽

Small Indels ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read

AbstractMotivationStructural variants are defined as genomic variants larger than 50bp. They have been shown to affect more bases in any given genome than SNPs or small indels. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities.ResultsWe present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from PacBio and Nanopore sequencing machines.Availability and implementationThe source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package [email protected]

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Mapping and phasing of structural variation in patient genomes using nanopore sequencing

10.1101/129379 ◽

2017 ◽

Cited By ~ 4

Author(s):

Mircea Cretu Stancu ◽

Markus J. van Roosmalen ◽

Ivo Renkens ◽

Marleen Nieboer ◽

Sjors Middelkamp ◽

...

Keyword(s):

Single Molecule ◽

De Novo ◽

Structural Variants ◽

Human Genetic Disease ◽

Structural Genomic ◽

Short Read ◽

Sequencing Technologies ◽

Genome Wide ◽

Long Read ◽

Complex Structural

AbstractStructural genomic variants form a common type of genetic alteration underlying human genetic disease and phenotypic variation. Despite major improvements in genome sequencing technology and data analysis, the detection of structural variants still poses challenges, particularly when variants are of high complexity. Emerging long-read single-molecule sequencing technologies provide new opportunities for detection of structural variants. Here, we demonstrate sequencing of the genomes of two patients with congenital abnormalities using the ONT MinION at 11x and 16x mean coverage, respectively. We developed a bioinformatic pipeline - NanoSV - to efficiently map genomic structural variants (SVs) from the long-read data. We demonstrate that the nanopore data are superior to corresponding short-read data with regard to detection of de novo rearrangements originating from complex chromothripsis events in the patients. Additionally, genome-wide surveillance of SVs, revealed 3,253 (33%) novel variants that were missed in short-read data of the same sample, the majority of which are duplications < 200bp in size. Long sequencing reads enabled efficient phasing of genetic variations, allowing the construction of genome-wide maps of phased SVs and SNVs. We employed read-based phasing to show that all de novo chromothripsis breakpoints occurred on paternal chromosomes and we resolved the long-range structure of the chromothripsis. This work demonstrates the value of long-read sequencing for screening whole genomes of patients for complex structural variants.

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Highly-accurate long-read sequencing improves variant detection and assembly of a human genome

10.1101/519025 ◽

2019 ◽

Cited By ~ 27

Author(s):

Aaron M. Wenger ◽

Paul Peluso ◽

William J. Rowell ◽

Pi-Chuan Chang ◽

Richard J. Hall ◽

...

Keyword(s):

Single Molecule ◽

De Novo ◽

Structural Variants ◽

Short Reads ◽

Sequencing Technologies ◽

Long Reads ◽

Long Read ◽

Variant Detection ◽

High Quality Genome ◽

Circular Consensus Sequencing

AbstractThe major DNA sequencing technologies in use today produce either highly-accurate short reads or noisy long reads. We developed a protocol based on single-molecule, circular consensus sequencing (CCS) to generate highly-accurate (99.8%) long reads averaging 13.5 kb and applied it to sequence the well-characterized human HG002/NA24385. We optimized existing tools to comprehensively detect variants, achieving precision and recall above 99.91% for SNVs, 95.98% for indels, and 95.99% for structural variants. We estimate that 2,434 discordances are correctable mistakes in the high-quality Genome in a Bottle benchmark. Nearly all (99.64%) variants are phased into haplotypes, which further improves variant detection. De novo assembly produces a highly contiguous and accurate genome with contig N50 above 15 Mb and concordance of 99.998%. CCS reads match short reads for small variant detection, while enabling structural variant detection and de novo assembly at similar contiguity and markedly higher concordance than noisy long reads.

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Longshot enables accurate variant calling in diploid genomes from single-molecule long read sequencing

Nature Communications ◽

10.1038/s41467-019-12493-y ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 26

Author(s):

Peter Edge ◽

Vikas Bansal

Keyword(s):

Single Molecule ◽

Variant Calling ◽

Small Scale ◽

Whole Genome ◽

Limited Information ◽

Single Nucleotide Variants ◽

Pacific Biosciences ◽

Sequencing Technologies ◽

Long Reads ◽

Long Read

Abstract Whole-genome sequencing using sequencing technologies such as Illumina enables the accurate detection of small-scale variants but provides limited information about haplotypes and variants in repetitive regions of the human genome. Single-molecule sequencing (SMS) technologies such as Pacific Biosciences and Oxford Nanopore generate long reads that can potentially address the limitations of short-read sequencing. However, the high error rate of SMS reads makes it challenging to detect small-scale variants in diploid genomes. We introduce a variant calling method, Longshot, which leverages the haplotype information present in SMS reads to accurately detect and phase single-nucleotide variants (SNVs) in diploid genomes. We demonstrate that Longshot achieves very high accuracy for SNV detection using whole-genome Pacific Biosciences data, outperforms existing variant calling methods, and enables variant detection in duplicated regions of the genome that cannot be mapped using short reads.

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Reference-free reconstruction and quantification of transcriptomes from Nanopore long-read sequencing

10.1101/2020.02.08.939942 ◽

2020 ◽

Author(s):

Ivan de la Rubia ◽

Joel A. Indi ◽

Silvia Carbonell-Sala ◽

Julien Lagarde ◽

M Mar Albà ◽

...

Keyword(s):

Single Molecule ◽

Reference Genome ◽

Simulated Data ◽

Cost Effective ◽

Dna Assembly ◽

Sequencing Data ◽

Consensus Sequences ◽

Sequencing Technologies ◽

Long Reads ◽

Long Read

AbstractSingle-molecule long-read sequencing with Nanopore provides an unprecedented opportunity to measure transcriptomes from any sample1–3. However, current analysis methods rely on the comparison with a reference genome or transcriptome2,4,5, or the use of multiple sequencing technologies6,7, thereby precluding cost-effective studies in species with no genome assembly available, in individuals underrepresented in the existing reference, and for the discovery of disease-specific transcripts not directly identifiable from a reference genome. Methods for DNA assembly8–10 cannot be directly transferred to transcriptomes since their consensus sequences lack the required interpretability for genes with multiple transcript isoforms. To address these challenges, we have developed RATTLE, the first tool to perform reference-free reconstruction and quantification of transcripts from Nanopore long reads. Using simulated data, isoform spike-ins, and sequencing data from tissues and cell lines, we demonstrate that RATTLE accurately determines transcript sequence and abundance, is comparable to reference-based methods, and shows saturation in the number of predicted transcripts with increasing number of input reads.

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Survey of the Bradysia odoriphaga Transcriptome Using PacBio Single-Molecule Long-Read Sequencing

Genes ◽

10.3390/genes10060481 ◽

2019 ◽

Vol 10 (6) ◽

pp. 481 ◽

Cited By ~ 1

Author(s):

Chen ◽

Lin ◽

Xie ◽

Zhong ◽

Zhang ◽

...

Keyword(s):

Insecticide Resistance ◽

Single Molecule ◽

Functional Categories ◽

Genetic Studies ◽

Sequencing Technologies ◽

Clusters Of Orthologous Groups ◽

Long Read ◽

Main Gene ◽

First Time ◽

Main Factor

The damage caused by Bradysia odoriphaga is the main factor threatening the production of vegetables in the Liliaceae family. However, few genetic studies of B. odoriphaga have been conducted because of a lack of genomic resources. Many long-read sequencing technologies have been developed in the last decade; therefore, in this study, the transcriptome including all development stages of B. odoriphaga was sequenced for the first time by Pacific single-molecule long-read sequencing. Here, 39,129 isoforms were generated, and 35,645 were found to have annotation results when checked against sequences available in different databases. Overall, 18,473 isoforms were distributed in 25 various Clusters of Orthologous Groups, and 11,880 isoforms were categorized into 60 functional groups that belonged to the three main Gene Ontology classifications. Moreover, 30,610 isoforms were assigned into 44 functional categories belonging to six main Kyoto Encyclopedia of Genes and Genomes functional categories. Coding DNA sequence (CDS) prediction showed that 36,419 out of 39,129 isoforms were predicted to have CDS, and 4319 simple sequence repeats were detected in total. Finally, 266 insecticide resistance and metabolism-related isoforms were identified as candidate genes for further investigation of insecticide resistance and metabolism in B. odoriphaga.

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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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SVIM-asm: Structural variant detection from haploid and diploid genome assemblies

10.1101/2020.10.27.356907 ◽

2020 ◽

Author(s):

David Heller ◽

Martin Vingron

Keyword(s):

Genetic Information ◽

Source Code ◽

Supplementary Information ◽

Supplementary Data ◽

Diploid Genome ◽

Insertions And Deletions ◽

Structural Variant ◽

Sequencing Technologies ◽

Variant Detection ◽

Genome Assemblies

AbstractMotivationWith the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes.ResultsWe introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual.Availability and ImplementationSVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/[email protected] informationSupplementary data are available online.

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Identification of Structural Variants in Two Novel Genomes of Maize Inbred Lines Possibly Related to Glyphosate Tolerance

Plants ◽

10.3390/plants9040523 ◽

2020 ◽

Vol 9 (4) ◽

pp. 523

Author(s):

Medhat Mahmoud ◽

Joanna Gracz-Bernaciak ◽

Marek Żywicki ◽

Wojciech Karłowski ◽

Tomasz Twardowski ◽

...

Keyword(s):

Gene Expression ◽

Single Molecule ◽

Zea Mays L ◽

Shikimate Pathway ◽

High Impact ◽

Maize Genome ◽

Structural Variants ◽

Shikimate Dehydrogenase ◽

Epsps Gene ◽

Sequencing Technologies

To study genetic variations between genomes of plants that are naturally tolerant and sensitive to glyphosate, we used two Zea mays L. lines traditionally bred in Poland. To overcome the complexity of the maize genome, two sequencing technologies were employed: Illumina and Single Molecule Real-Time (SMRT) PacBio. Eleven thousand structural variants, 4 million SNPs and approximately 800 thousand indels differentiating the two genomes were identified. Detailed analyses allowed to identify 20 variations within the EPSPS gene, but all of them were predicted to have moderate or unknown effects on gene expression. Other genes of the shikimate pathway encoding bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase and chorismate synthase were altered by variants predicted to have a high impact on gene expression. Additionally, high-impact variants located within the genes involved in the active transport of glyphosate through the cell membrane encoding phosphate transporters as well as multidrug and toxic compound extrusion have been identified.

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L1EM: a tool for accurate locus specific LINE-1 RNA quantification

Bioinformatics ◽

10.1093/bioinformatics/btz724 ◽

2019 ◽

Cited By ~ 3

Author(s):

Wilson McKerrow ◽

David Fenyö

Keyword(s):

Expectation Maximization Algorithm ◽

Simulated Data ◽

Cellular Damage ◽

Supplementary Information ◽

Genomic Locus ◽

Protein Coding ◽

Disease States ◽

Rna Quantification ◽

Long Read ◽

Specific Line

Abstract Motivation LINE-1 elements are retrotransposons that are capable of copying their sequence to new genomic loci. LINE-1 derepression is associated with a number of disease states, and has the potential to cause significant cellular damage. Because LINE-1 elements are repetitive, it is difficult to quantify LINE-1 RNA at specific loci and to separate transcripts with protein coding capability from other sources of LINE-1 RNA. Results We provide a tool, L1EM that uses the expectation maximization algorithm to quantify LINE-1 RNA at each genomic locus, separating transcripts that are capable of generating retrotransposition from those that are not. We show the accuracy of L1EM on simulated data and against long read sequencing from HEK cells. Availability and implementation L1EM is written in python. The source code along with the necessary annotations are available at https://github.com/FenyoLab/L1EM and distributed under GPLv3. Supplementary information Supplementary data are available at Bioinformatics online.

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