scholarly journals SVJedi: genotyping structural variations with long reads

2020 ◽  
Vol 36 (17) ◽  
pp. 4568-4575
Author(s):  
Lolita Lecompte ◽  
Pierre Peterlongo ◽  
Dominique Lavenier ◽  
Claire Lemaitre
Keyword(s):  
Supplementary Information ◽  
Sequencing Data ◽  
Structural Variations ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Clinical Diagnoses ◽  
Long Read ◽  
The One

Abstract Motivation Studies on structural variants (SVs) 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. Results We present a novel method to genotype known SVs from long read sequencing data. The method is based on the generation of a set of representative allele sequences that represent the two alleles of each structural variant. Long reads are aligned to these allele 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 SVs with long reads. The tool has been applied to both simulated and real human datasets and achieves high genotyping accuracy. We show that SVJedi obtains better performances than other existing long read genotyping tools and we also demonstrate that SV genotyping is considerably improved with SVJedi compared to other approaches, namely SV discovery and short read SV genotyping approaches. Availability and implementation https://github.com/llecompte/SVJedi.git Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SVJedi: Genotyping structural variations with long reads

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]

scholarly journals Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

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.

scholarly journals BleTIES: Annotation of natural genome editing in ciliates using long read sequencing

2021 ◽  
Author(s):  
Brandon K. B. Seah ◽  
Estienne C. Swart
Keyword(s):  
Dna Sequences ◽  
Sequence Data ◽  
Low Complexity ◽  
Supplementary Information ◽  
Neighboring Element ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Element Elimination

Ciliates are single-celled eukaryotes that eliminate specific, interspersed DNA sequences (internally eliminated sequences, IESs) from their genomes during development. These are challenging to annotate and assemble because IES-containing sequences are much less abundant in the cell than those without, and IES sequences themselves often contain repetitive and low-complexity sequences. Long read sequencing technologies from Pacific Biosciences and Oxford Nanopore have the potential to reconstruct longer IESs than has been possible with short reads, and also the ability to detect correlations of neighboring element elimination. Here we present BleTIES, a software toolkit for detecting, assembling, and analyzing IESs using mapped long reads. Availability and implementation: BleTIES is implemented in Python 3. Source code is available at https://github.com/Swart-lab/bleties (MIT license), and also distributed via Bioconda. Contact: [email protected] Supplementary information: Benchmarking of BleTIES with published sequence data.

scholarly journals TALC: Transcript-level Aware Long-read Correction

2020 ◽  
Vol 36 (20) ◽  
pp. 5000-5006 ◽  
Author(s):  
Lucile Broseus ◽  
Aubin Thomas ◽  
Andrew J Oldfield ◽  
Dany Severac ◽  
Emeric Dubois ◽  
...  
Keyword(s):  
Transcriptome Sequencing ◽  
Transcript Level ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Rna Transcript

Abstract Motivation Long-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous ‘hybrid correction’ algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. Results We have created a novel reference-free algorithm called Transcript-level Aware Long-Read Correction (TALC) which models changes in RNA expression and isoform representation in a weighted De Bruijn graph to correct long reads from transcriptome studies. We show that transcript-level aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology. Availability and implementation TALC is implemented in C++ and available at https://github.com/lbroseus/TALC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals HASLR: Fast Hybrid Assembly of Long Reads

2020 ◽  
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.

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

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.

scholarly journals BleTIES: Annotation of natural genome editing in ciliates using long read sequencing

2021 ◽  
Author(s):  
Brandon K B Seah ◽  
Estienne C Swart
Keyword(s):  
Dna Sequences ◽  
Sequence Data ◽  
Low Complexity ◽  
Supplementary Information ◽  
Software Toolkit ◽  
Assembly Strategy ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read

Abstract Summary Ciliates are single-celled eukaryotes that eliminate specific, interspersed DNA sequences (internally eliminated sequences, IESs) from their genomes during development. These are challenging to annotate and assemble because IES-containing sequences are typically much less abundant in the cell than those without, and IES sequences themselves often contain repetitive and low-complexity sequences. Long read sequencing technologies from Pacific Biosciences and Oxford Nanopore have the potential to reconstruct longer IESs than has been possible with short reads, but require a different assembly strategy. Here we present BleTIES, a software toolkit for detecting, assembling, and analyzing IESs using mapped long reads. Availability and implementation BleTIES is implemented in Python 3. Source code is available at https://github.com/Swart-lab/bleties (MIT license), and also distributed via Bioconda. Supplementary information Benchmarking of BleTIES with published sequence data.

scholarly journals Comprehensive identification of transposable element insertions using multiple sequencing technologies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chong Chu ◽  
Rebeca Borges-Monroy ◽  
Vinayak V. Viswanadham ◽  
Soohyun Lee ◽  
Heng Li ◽  
...  
Keyword(s):  
Transposable Element ◽  
Structure And Function ◽  
Endogenous Retroviruses ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Read ◽  
And Function

AbstractTransposable elements (TEs) help shape the structure and function of the human genome. When inserted into some locations, TEs may disrupt gene regulation and cause diseases. Here, we present xTea (x-Transposable element analyzer), a tool for identifying TE insertions in whole-genome sequencing data. Whereas existing methods are mostly designed for short-read data, xTea can be applied to both short-read and long-read data. Our analysis shows that xTea outperforms other short read-based methods for both germline and somatic TE insertion discovery. With long-read data, we created a catalogue of polymorphic insertions with full assembly and annotation of insertional sequences for various types of retroelements, including pseudogenes and endogenous retroviruses. Notably, we find that individual genomes have an average of nine groups of full-length L1s in centromeres, suggesting that centromeres and other highly repetitive regions such as telomeres are a significant yet unexplored source of active L1s. xTea is available at https://github.com/parklab/xTea.

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

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.

scholarly journals Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

2020 ◽  
Author(s):  
Andrew J. Page ◽  
Nabil-Fareed Alikhan ◽  
Michael Strinden ◽  
Thanh Le Viet ◽  
Timofey Skvortsov
Keyword(s):  
Mycobacterium Tuberculosis ◽  
State Of The Art ◽  
Sequence Data ◽  
Human Pathogen ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Long Reads ◽  
Long Read

AbstractSpoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

Sign in / Sign up

Export Citation Format

Share Document