Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome

The advent of long-read sequencing offers a new assessment method of detecting genomic structural variation (SV) in numerous rare genetic diseases. For autism spectrum disorders (ASD) cases where pathogenic variants fail to be found in the protein-coding genic regions along chromosomes, we proposed a scalable workflow to characterize the risk factor of SVs impacting non-coding elements of the genome. We applied whole-genome sequencing on an Emirati family having three children with ASD using long and short-read sequencing technology. A series of analytical pipelines were established to identify a set of SVs with high sensitivity and specificity. At 15-fold coverage, we observed that long-read sequencing technology (987 variants) detected a significantly higher number of SVs when compared to variants detected using short-read technology (509 variants) (p-value < 1.1020 × 10−57). Further comparison showed 97.9% of long-read sequencing variants were spanning within the 1–100 kb size range (p-value < 9.080 × 10−67) and impacting over 5000 genes. Moreover, long-read variants detected 604 non-coding RNAs (p-value < 9.02 × 10−9), comprising 58% microRNA, 31.9% lncRNA, and 9.1% snoRNA. Even at low coverage, long-read sequencing has shown to be a reliable technology in detecting SVs impacting complex elements of the genome.

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Long-read-based Human Genomic Structural Variation Detection with cuteSV

10.1101/780700 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tao Jiang ◽

Bo Liu ◽

Yue Jiang ◽

Junyi Li ◽

Yan Gao ◽

...

Keyword(s):

Structural Variation ◽

High Sensitivity ◽

Structural Variations ◽

Genomic Structural Variation ◽

Long Reads ◽

Detection Approach ◽

Refinement Method ◽

Long Read ◽

Human Genomic ◽

And Performance

AbstractLong-read sequencing enables the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high sensitivity and performance simultaneously due to the complex SV characteristics implied by noisy long reads. Therefore, we propose cuteSV, a sensitive, fast and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to analyze the signatures to implement sensitive SV detection. Benchmarks on real PacBio and ONT datasets demonstrate that cuteSV has better yields and scalability than state-of-the-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV.

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Direct Read and Quantify Damage Nucleotide from an Oligonucleotide Using a Single Molecule Interface

10.26434/chemrxiv.10080638.v2 ◽

2019 ◽

Author(s):

Jiajun Wang ◽

Meng-Yin Li ◽

Jie Yang ◽

Ya-Qian Wang ◽

Xue-Yuan Wu ◽

...

Keyword(s):

Dna Sequencing ◽

Single Molecule ◽

Genetic Diseases ◽

High Sensitivity ◽

Dna Lesions ◽

Lesion Detection ◽

The Novel ◽

Structural Variations ◽

Dna Lesion ◽

Base Lesion

DNA lesion such as metholcytosine(mC), 8-OXO-guanine(OG), inosine(I) etc could cause the genetic diseases. Identification of the varieties of lesion bases are usually beyond the capability of conventional DNA sequencing which is mainly designed to discriminate four bases only. Therefore, lesion detection remain challenge due to the massive varieties and less distinguishable readouts for minor structural variations. Moreover, standard amplification and labelling hardly works in DNA lesions detection. Herein, we designed a single molecule interface from the mutant K238Q Aerolysin, whose confined sensing region shows the high compatible to capture and then directly convert each base lesion into distinguishable current readouts. Compared with previous single molecule sensing interface, the resolution of the K238Q Aerolysin nanopore is enhanced by 2-order. The novel K238Q could direct discriminate at least 3 types (mC, OG, I) lesions without lableing and quantify modification sites under mixed hetero-composition condition of oligonucleotide. Such nanopore could be further applied to diagnose genetic diseases at high sensitivity.

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Evaluating nanopore sequencing data processing pipelines for structural variation identification

Genome Biology ◽

10.1186/s13059-019-1858-1 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 9

Author(s):

Anbo Zhou ◽

Timothy Lin ◽

Jinchuan Xing

Keyword(s):

Detection Accuracy ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Sequencing Technology ◽

Structural Variations ◽

Human Genomes ◽

Data Assessment ◽

Machine Learning Approach ◽

Long Read ◽

The Impact

Abstract Background Structural variations (SVs) account for about 1% of the differences among human genomes and play a significant role in phenotypic variation and disease susceptibility. The emerging nanopore sequencing technology can generate long sequence reads and can potentially provide accurate SV identification. However, the tools for aligning long-read data and detecting SVs have not been thoroughly evaluated. Results Using four nanopore datasets, including both empirical and simulated reads, we evaluate four alignment tools and three SV detection tools. We also evaluate the impact of sequencing depth on SV detection. Finally, we develop a machine learning approach to integrate call sets from multiple pipelines. Overall SV callers’ performance varies depending on the SV types. For an initial data assessment, we recommend using aligner minimap2 in combination with SV caller Sniffles because of their speed and relatively balanced performance. For detailed analysis, we recommend incorporating information from multiple call sets to improve the SV call performance. Conclusions We present a workflow for evaluating aligners and SV callers for nanopore sequencing data and approaches for integrating multiple call sets. Our results indicate that additional optimizations are needed to improve SV detection accuracy and sensitivity, and an integrated call set can provide enhanced performance. The nanopore technology is improving, and the sequencing community is likely to grow accordingly. In turn, better benchmark call sets will be available to more accurately assess the performance of available tools and facilitate further tool development.

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Precise characterization of somatic structural variations and mobile element insertions from paired long-read sequencing data with nanomonsv

10.1101/2020.07.22.214262 ◽

2020 ◽

Author(s):

Yuichi Shiraishi ◽

Junji Koya ◽

Kenichi Chiba ◽

Yuki Saito ◽

Ai Okada ◽

...

Keyword(s):

Matched Control ◽

Mobile Element ◽

Sequencing Data ◽

Structural Variations ◽

Short Read ◽

Long Read ◽

Functional Consequences ◽

Single Base Resolution ◽

Mutational Processes

AbstractWe introduce our novel software, nanomonsv, for detecting somatic structural variations (SVs) using tumor and matched control long-read sequencing data with a single-base resolution. Using paired long-read sequencing data from three cancer cell-lines and their matched lymphoblastoid lines, we demonstrate that our approach can identify not only somatic SVs that can be captured with short-read technologies but also novel ones especially those whose breakpoints are located in repeat regions. In addition, we have developed a workflow for classifying mobile element insertions while elucidating their in-depth properties such as 5′ truncations, internal inversion as well as source sites in the case of LINE1 transductions. Finally, we identify complex SVs probably caused by replication mechanisms or telomere crisis by examining the co-occurrence of multiple somatic SVs in common supporting reads. In summary, our approaches applied to cancer long-read sequencing data can reveal various features of somatic SVs and will lead to further understanding of mutational processes and functional consequences of somatic SVs.

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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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Long-Read Sequencing of the Zebrafish Genome Reorganizes Genomic Architecture

10.1101/2021.08.27.457855 ◽

2021 ◽

Author(s):

Yelena Chernyavskaya ◽

Xiaofei Zhang ◽

Jinze Liu ◽

Jessica S. Blackburn

Keyword(s):

Low Complexity ◽

Zebrafish Genome ◽

Nanopore Sequencing ◽

Sequencing Technology ◽

Short Read ◽

Short Read Sequencing ◽

Genomic Landscape ◽

Long Reads ◽

Long Read ◽

Sequencing Platforms

Nanopore sequencing technology has revolutionized the field of genome biology with its ability to generate extra-long reads that can resolve regions of the genome that were previously inaccessible to short-read sequencing platforms. Although long-read sequencing has been used to resolve several vertebrate genomes, a nanopore-based zebrafish assembly has not yet been released. Over 50% of the zebrafish genome consists of difficult to map, highly repetitive, low complexity elements that pose inherent problems for short-read sequencers and assemblers. We used nanopore sequencing to improve upon and resolve the issues plaguing the current zebrafish reference assembly (GRCz11). Our long-read assembly improved the current resolution of the reference genome by identifying 1,697 novel insertions and deletions over 1Kb in length and placing 106 previously unlocalized scaffolds. We also discovered additional sites of retrotransposon integration previously unreported in GRCz11 and observed their expression in adult zebrafish under physiologic conditions, implying they have active mobility in the zebrafish genome and contribute to the ever-changing genomic landscape.

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Targeted short read sequencing and assembly of re-arrangements and candidate gene loci provide megabase diplotypes

Nucleic Acids Research ◽

10.1093/nar/gkz661 ◽

2019 ◽

Vol 47 (19) ◽

pp. e115-e115 ◽

Cited By ~ 5

Author(s):

GiWon Shin ◽

Stephanie U Greer ◽

Li C Xia ◽

HoJoon Lee ◽

Jun Zhou ◽

...

Keyword(s):

Human Genetics ◽

Large Population ◽

Regulation Of Gene Expression ◽

Brca1 Gene ◽

Structural Variations ◽

High Molecular Weight Dna ◽

Short Read ◽

Guide Rna ◽

Major Histocompatibility Complex Locus ◽

Long Read

Abstract The human genome is composed of two haplotypes, otherwise called diplotypes, which denote phased polymorphisms and structural variations (SVs) that are derived from both parents. Diplotypes place genetic variants in the context of cis-related variants from a diploid genome. As a result, they provide valuable information about hereditary transmission, context of SV, regulation of gene expression and other features which are informative for understanding human genetics. Successful diplotyping with short read whole genome sequencing generally requires either a large population or parent-child trio samples. To overcome these limitations, we developed a targeted sequencing method for generating megabase (Mb)-scale haplotypes with short reads. One selects specific 0.1–0.2 Mb high molecular weight DNA targets with custom-designed Cas9–guide RNA complexes followed by sequencing with barcoded linked reads. To test this approach, we designed three assays, targeting the BRCA1 gene, the entire 4-Mb major histocompatibility complex locus and 18 well-characterized SVs, respectively. Using an integrated alignment- and assembly-based approach, we generated comprehensive variant diplotypes spanning the entirety of the targeted loci and characterized SVs with exact breakpoints. Our results were comparable in quality to long read sequencing.

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Paragraph: a graph-based structural variant genotyper for short-read sequence data

Genome Biology ◽

10.1186/s13059-019-1909-7 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 19

Author(s):

Sai Chen ◽

Peter Krusche ◽

Egor Dolzhenko ◽

Rachel M. Sherman ◽

Roman Petrovski ◽

...

Keyword(s):

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome ◽

Structural Variations ◽

Short Read ◽

Three Samples ◽

Genomics Research ◽

Long Read ◽

Short Read Sequence ◽

Population Scale

AbstractAccurate detection and genotyping of structural variations (SVs) from short-read data is a long-standing area of development in genomics research and clinical sequencing pipelines. We introduce Paragraph, an accurate genotyper that models SVs using sequence graphs and SV annotations. We demonstrate the accuracy of Paragraph on whole-genome sequence data from three samples using long-read SV calls as the truth set, and then apply Paragraph at scale to a cohort of 100 short-read sequenced samples of diverse ancestry. Our analysis shows that Paragraph has better accuracy than other existing genotypers and can be applied to population-scale studies.

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BreakNet: detecting deletions using long reads and a deep learning approach

BMC Bioinformatics ◽

10.1186/s12859-021-04499-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Junwei Luo ◽

Hongyu Ding ◽

Jiquan Shen ◽

Haixia Zhai ◽

Zhengjiang Wu ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Genetic Diseases ◽

Structural Variations ◽

Short Term ◽

New Approach ◽

Feature Vectors ◽

Long Reads ◽

Long Read ◽

Long Short Term Memory

Abstract Background Structural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs. Results In this paper, we propose BreakNet, a deep learning method that detects deletions by using long reads. BreakNet first extracts feature matrices from long-read alignments. Second, it uses a time-distributed convolutional neural network (CNN) to integrate and map the feature matrices to feature vectors. Third, BreakNet employs a bidirectional long short-term memory (BLSTM) model to analyse the produced set of continuous feature vectors in both the forward and backward directions. Finally, a classification module determines whether a region refers to a deletion. On real long-read sequencing datasets, we demonstrate that BreakNet outperforms Sniffles, SVIM and cuteSV in terms of their F1 scores. The source code for the proposed method is available from GitHub at https://github.com/luojunwei/BreakNet. Conclusions Our work shows that deep learning can be combined with long reads to call deletions more effectively than existing methods.

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

10.1101/551028 ◽

2019 ◽

Cited By ~ 3

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

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