scholarly journals SVCollector: Optimized sample selection for validating and long-read resequencing of structural variants

2018 ◽  
Author(s):  
Fritz J. Sedlazeck ◽  
Zachary Lemmon ◽  
Sebastian Soyk ◽  
William J. Salerno ◽  
Zachary Lippman ◽  
...  
Keyword(s):  
Sample Selection ◽  
Supplementary Information ◽  
Structural Variants ◽  
Structural Variations ◽  
Short Read Sequencing ◽  
Variant Discovery ◽  
Large Numbers ◽  
Long Read ◽  
Supplementary Material ◽  
Selection For

AbstractSummaryStructural Variations (SVs) are increasingly recognized for their importance in genomics. Short-read sequencing is the most widely-used approach for genotyping large numbers of samples for SVs but suffers from relatively poor accuracy. Here we present SVCollector, an open-source method that optimally selects samples to maximize variant discovery and validation using long read resequencing or PCR-based validation. SVCollector has two modes: selecting those samples that are individually the most diverse or those that collectively capture the largest number of variations.Availabilityhttps://github.com/fritzsedlazeck/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Ribbon: intuitive visualization for complex genomic variation

2020 ◽  
Author(s):  
Maria Nattestad ◽  
Robert Aboukhalil ◽  
Chen-Shan Chin ◽  
Michael C Schatz
Keyword(s):  
Genomic Variation ◽  
Supplementary Information ◽  
Visualization Tool ◽  
Visualization Method ◽  
Structural Variants ◽  
Long Read ◽  
Complex Structural ◽  
Intuitive View ◽  
Genome Comparisons ◽  
Shed Light

Abstract Summary Ribbon is an alignment visualization tool that shows how alignments are positioned within both the reference and read contexts, giving an intuitive view that enables a better understanding of structural variants and the read evidence supporting them. Ribbon was born out of a need to curate complex structural variant calls and determine whether each was well supported by long-read evidence, and it uses the same intuitive visualization method to shed light on contig alignments from genome-to-genome comparisons. Availability and implementation Ribbon is freely available online at http://genomeribbon.com/ and is open-source at https://github.com/marianattestad/ribbon. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data

2018 ◽  
Author(s):  
Li Fang ◽  
Charlly Kao ◽  
Michael V Gonzalez ◽  
Fernanda A Mafra ◽  
Renata Pellegrino da Silva ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Read Depth ◽  
Structural Variants ◽  
Sequencing Data ◽  
High Coverage ◽  
Short Read ◽  
Short Read Sequencing ◽  
Sequencing Studies ◽  
Long Read ◽  
Local Assembly

AbstractLinked-read sequencing provides long-range information on short-read sequencing data by barcoding reads originating from the same DNA molecule, and can improve the detection and breakpoint identification for structural variants (SVs). We present LinkedSV for SV detection on linked-read sequencing data. LinkedSV considers barcode overlapping and enriched fragment endpoints as signals to detect large SVs, while it leverages read depth, paired-end signals and local assembly to detect small SVs. Benchmarking studies demonstrates that LinkedSV outperforms existing tools, especially on exome data and on somatic SVs with low variant allele frequencies. We demonstrate clinical cases where LinkedSV identifies disease causal SVs from linked-read exome sequencing data missed by conventional exome sequencing, and show examples where LinkedSV identifies SVs missed by high-coverage long-read sequencing. In summary, LinkedSV can detect SVs missed by conventional short-read and long-read sequencing approaches, and may resolve negative cases from clinical genome/exome sequencing studies.

scholarly journals TSD: A computational tool to study the complex structural variants using PacBio targeted sequencing data

2018 ◽  
Author(s):  
Guofeng Meng ◽  
Ying Tan ◽  
Yue Fan ◽  
Yan Wang ◽  
Guang Yang ◽  
...  
Keyword(s):  
Human Cell Line ◽  
Targeted Sequencing ◽  
Structural Variants ◽  
Sequencing Data ◽  
Rna Sequences ◽  
Variant Discovery ◽  
Powerful Approach ◽  
Full Profile ◽  
Long Read ◽  
Complex Structural

ABSTRACTThe PacBio sequencing is a powerful approach to study the DNA or RNA sequences in a longer scope. It is especially useful in exploring the complex structural variants generated by random integration or multiple rearrangement of internal or external sequences. However, there is still no tool designed to uncover their structural organization in the host genome. Here, we present a tool, TSD, for complex structural variant discovery using PacBio targeted sequencing data. It allows researchers to identify and visualize the genomic structures of targeted sequences by unlimited splitting, alignment and assembly of long PacBio reads. Application to the sequencing data derived from an HBV integrated human cell line(PLC/PRF/5) indicated that TSD could recover the full profile of HBV integration events, especially for the regions with the complex human-HBV genome integrations and multiple HBV rearrangements. Compared to other long read analysis tools, TSD showed a better performance for detecting complex genomic structural variants. TSD is publicly available at: https://github.com/menggf/tsd

scholarly journals NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data

2016 ◽  
Author(s):  
Li Fang ◽  
Jiang Hu ◽  
Depeng Wang ◽  
Kai Wang
Keyword(s):  
Whole Genome ◽  
Ashkenazi Jewish ◽  
Structural Variants ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Human Genomes ◽  
Long Read ◽  
Personal Genomes ◽  
Low Coverage

AbstractBackgroundStructural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, it is unclear what coverage is needed and how to optimally use the aligners and SV callers.ResultsIn this study, we developed NextSV, a meta-caller to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purposes. We evaluated SV calling performance of NextSV under different PacBio coverages on two personal genomes, NA12878 and HX1. Our results showed that, compared with running any single SV caller, NextSV stringent call set had higher precision and balanced accuracy (F1 score) while NextSV sensitive call set had a higher recall. At 10X coverage, the recall of NextSV sensitive call set was 93.5% to 94.1% for deletions and 87.9% to 93.2% for insertions, indicating that ~10X coverage might be an optimal coverage to use in practice, considering the balance between the sequencing costs and the recall rates. We further evaluated the Mendelian errors on an Ashkenazi Jewish trio dataset.ConclusionsOur results provide useful guidelines for SV detection from low coverage whole-genome PacBio data and we expect that NextSV will facilitate the analysis of SVs on long-read sequencing data.

scholarly journals SeqsLab: an integrated platform for cohort-based annotation and interpretation of genetic variants on Spark

2017 ◽  
Author(s):  
Ming-Tai Chang ◽  
Yi-An Tung ◽  
Jen-Ming Chung ◽  
Hung-Fei Yao ◽  
Yun-Lung Li ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Cluster Computing ◽  
Supplementary Information ◽  
Web Browsers ◽  
Structural Variations ◽  
Variant Annotation ◽  
Whole Genomes ◽  
Speed Up ◽  
Supplementary Material ◽  
Personal Genomes

AbstractSummarySeqsLab is a platform that helps researchers to easily annotate and interpret genetic variants derived from a large quantity of personal genomes. It provides an integrated interface to annotate the variants based on curated databases as well as in silico estimation on the effects of the variants. SeqsLab adopts the scalable cluster computing framework, Spark, and incorporates several customized algorithms to speed up the process of variant annotation and interpretation. The key features of SeqsLab include efficient annotation on large structural variations, diverse combinations of variant filters, easy incorporation with a vast amount of public databases, and scalable architecture of analyzing hundreds of human whole genomes simultaneously.Availability and ImplementationSeqsLab is implemented with JAVA. The generated annotation will then be stored in Elasticsearch for real-time query and exploratory analysis. SeqsLab can be accessed by web browsers and is freely available at http://portal.seqslab.net/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Li Fang ◽  
Charlly Kao ◽  
Michael V. Gonzalez ◽  
Fernanda A. Mafra ◽  
Renata Pellegrino da Silva ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Read Depth ◽  
Structural Variants ◽  
Sequencing Data ◽  
High Coverage ◽  
Short Read ◽  
Short Read Sequencing ◽  
Sequencing Studies ◽  
Long Read ◽  
Local Assembly

AbstractLinked-read sequencing provides long-range information on short-read sequencing data by barcoding reads originating from the same DNA molecule, and can improve detection and breakpoint identification for structural variants (SVs). Here we present LinkedSV for SV detection on linked-read sequencing data. LinkedSV considers barcode overlapping and enriched fragment endpoints as signals to detect large SVs, while it leverages read depth, paired-end signals and local assembly to detect small SVs. Benchmarking studies demonstrate that LinkedSV outperforms existing tools, especially on exome data and on somatic SVs with low variant allele frequencies. We demonstrate clinical cases where LinkedSV identifies disease-causal SVs from linked-read exome sequencing data missed by conventional exome sequencing, and show examples where LinkedSV identifies SVs missed by high-coverage long-read sequencing. In summary, LinkedSV can detect SVs missed by conventional short-read and long-read sequencing approaches, and may resolve negative cases from clinical genome/exome sequencing studies.

scholarly journals Detection and assembly of novel sequence insertions using Linked-Read technology

2019 ◽  
Author(s):  
Dmitry Meleshko ◽  
Patrick Marks ◽  
Stephen Williams ◽  
Iman Hajirasouliha
Keyword(s):  
Dna Sequences ◽  
De Novo Assembly ◽  
De Novo ◽  
Supplementary Information ◽  
Computational Techniques ◽  
Whole Genome ◽  
Structural Variations ◽  
Short Read ◽  
Link Type ◽  
Long Read

AbstractMotivationEmerging Linked-Read (aka read-cloud) technologies such as the 10x Genomics Chromium system have great potential for accurate detection and phasing of largescale human genome structural variations (SVs). By leveraging the long-range information encoded in Linked-Read sequencing, computational techniques are able to detect and characterize complex structural variations that are previously undetectable by short-read methods. However, there is no available Linked-Read method for detection and assembly of novel sequence insertions, DNA sequences present in a given sequenced sample but missing in the reference genome, without requiring whole genome de novo assembly. In this paper, we propose a novel integrated alignment-based and local-assembly-based algorithm, Novel-X, that effectively uses the barcode information encoded in Linked-Read sequencing datasets to improve detection of such events without the need of whole genome de novo assembly. We evaluated our method on two haploid human genomes, CHM1 and CHM13, sequenced on the 10x Genomics Chromium system. These genomes have been also characterized with high coverage PacBio long-reads recently. We also tested our method on NA12878, the wellknown HapMap CEPH diploid genome and the child genome in a Yoruba trio (NA19240) which was recently studied on multiple sequencing platforms. Detecting insertion events is very challenging using short reads and the only viable available solution is by long-read sequencing (e.g. PabBio or ONT). Our experiments, however, show that Novel-X finds many insertions that cannot be found by state of the art tools using short-read sequencing data but present in PacBio data. Since Linked-Read sequencing is significantly cheaper than long-read sequencing, our method using Linked-Reads enables routine large-scale screenings of sequenced genomes for novel sequence insertions.AvailabilitySoftware is freely available at https://github.com/1dayac/[email protected] informationSupplementary data are available at https://github.com/1dayac/novel_insertions_supplementary

scholarly journals SiLiCO: A Simulator of Long Read Sequencing in PacBio and Oxford Nanopore

2016 ◽  
Author(s):  
Ethan Alexander García Baker ◽  
Sara Goodwin ◽  
W. Richard McCombie ◽  
Olivia Mendivil Ramos
Keyword(s):  
Reference Data ◽  
Supplementary Information ◽  
Data Sets ◽  
Simulation Tool ◽  
Supplementary Data ◽  
Structural Variants ◽  
Oxford Nanopore ◽  
Long Read ◽  
Sequencing Platforms ◽  
Core Facilities

AbstractSummaryLong read sequencing platforms, which include the widely used Pacific Biosciences (PacBio) platform and the emerging Oxford Nanopore platform, aim to produce sequence fragments in excess of 15-20 kilobases, and have proved advantageous in the identification of structural variants and easing genome assembly. However, long read sequencing remains relatively expensive and error prone, and failed sequencing runs represent a significant problem for genomics core facilities. To quantitatively assess the underlying mechanics of sequencing failure, it is essential to have highly reproducible and controllable reference data sets to which sequencing results can be compared. Here, we present SiLiCO, the first in silico simulation tool to generate standardized sequencing results from both of the leading long read sequencing platforms.AvailabilitySiLiCO is an open source package written in Python. It is freely available at https://www.github.com/ethanagbaker/SiLiCO under the GNU GPL 3.0 license.Contact<emails>Supplementary informationSupplementary data are available at Bioinformatics online.

NextPolish: a fast and efficient genome polishing tool for long-read assembly

2019 ◽  
Vol 36 (7) ◽  
pp. 2253-2255 ◽  
Author(s):  
Jiang Hu ◽  
Junpeng Fan ◽  
Zongyi Sun ◽  
Shanlin Liu
Keyword(s):  
Error Rates ◽  
Supplementary Information ◽  
Sequencing Technologies ◽  
Large Numbers ◽  
Long Reads ◽  
Long Read ◽  
Genome Assemblies ◽  
Polishing Tool ◽  
Sequence Errors ◽  
Plant Arabidopsis Thaliana

Abstract Motivation Although long-read sequencing technologies can produce genomes with long contiguity, they suffer from high error rates. Thus, we developed NextPolish, a tool that efficiently corrects sequence errors in genomes assembled with long reads. This new tool consists of two interlinked modules that are designed to score and count K-mers from high quality short reads, and to polish genome assemblies containing large numbers of base errors. Results When evaluated for the speed and efficiency using human and a plant (Arabidopsis thaliana) genomes, NextPolish outperformed Pilon by correcting sequence errors faster, and with a higher correction accuracy. Availability and implementation NextPolish is implemented in C and Python. The source code is available from https://github.com/Nextomics/NextPolish. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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