Identifying structural variants using linked-read sequencing data

Elyanow, Rebecca; Wu, Hsin Ta; Raphael, Benjamin J.

doi:10.1093/bioinformatics/btx712

Cited by 65 publications

(33 citation statements)

References 40 publications

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“…目前, 基于短序列测序平台的新技术被不断开发, 如基于合成长读段的TruSeq长读段合成技术 [49] 和10× Genomics技术 [50] . [50] , NAIBR [77] 和gemtools [78] 等, 且被广泛用于肿瘤中SVs 且拥有短读段测序的高准确度等技术优点 [79] . .…”

Section: 酶切的同时加上荧光标记利用基于Bionano平台的纳米通道把Dna分子拉直并线性化展开进行超长的unclassified

Identification of genomic structure variation based on Hi-C technology and its application in tumor research

Liu¹,

Zhang²

2020

Sci. Sin.-Vitae

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Section: 酶切的同时加上荧光标记利用基于Bionano平台的纳米通道把Dna分子拉直并线性化展开进行超长的unclassified

Identification of genomic structure variation based on Hi-C technology and its application in tumor research

Liu¹,

Zhang²

2020

Sci. Sin.-Vitae

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“…Novel computational approaches leveraging the special characteristics of 10x Genomics data have already generated significant advances in power and accuracy of haplotyping [19, 20], cancer genome reconstruction [21, 22], metagenomic assemblies [23], and de novo assembly of human and other genomes [24–26], compared to standard Illumina short-fragment sequencing. While the uniformity of sequence coverage is not as good as with PCR-free Illumina libraries, 10x Linked-Read sequencing is a promising technology that combines low per-base error and good small-variant discovery with long-range information for much improved SV detection in mapping-based approaches [22, 27], and the possibility of long-range contiguity in de novo assembly [24, 26, 28].…”

Section: Introductionmentioning

confidence: 99%

Assessment of human diploid genome assembly with 10x Linked-Reads data

Zhang

Zhou

Weng

et al. 2019

Preprint

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14Background: Producing cost-effective haplotype-resolved personal genomes remains 15 challenging. 10x Linked-Read sequencing, with its high base quality and long-range information, 16 has been demonstrated to facilitate de novo assembly of human genomes and variant detection. 17In this study, we investigate in depth how the parameter space of 10x library preparation and 18 sequencing affects assembly quality, on the basis of both simulated and real libraries. 19Findings: We prepared and sequenced eight 10x libraries with a diverse set of parameters from 20 standard cell lines NA12878 and NA24385 and performed whole genome assembly on the data. 21We also developed the simulator LRTK-SIM to follow the workflow of 10x data generation and 22 produce realistic simulated Linked-Read data sets. We found that assembly quality could be 23 improved by increasing the total sequencing coverage (C) and keeping physical coverage of 24 DNA fragments (C F ) or read coverage per fragment (C R ) within broad ranges. The optimal 25 physical coverage was between 332X and 823X and assembly quality worsened if it increased 26 to greater than 1,000X for a given C. Long DNA fragments could significantly extend phase 27 blocks, but decreased contig contiguity. The optimal length-weighted fragment length (Wߤ ி ) 28 was around 50 -150kb. When broadly optimal parameters were used for library preparation 29 and sequencing, ca. 80% of the genome was assembled in a diploid state. 30Conclusion: The Linked-Read libraries we generated and the parameter space we identified 31 provide theoretical considerations and practical guidelines for personal genome assemblies 32 based on 10x Linked-Read sequencing. 33 Keywords: 10x Linked-Read sequencing, de novo assembly, diploid human genome, library 34 preparation 35 3 Data description 36 Introduction 37 The human genome holds the key for understanding the genetic basis of human evolution, 38 hereditary illnesses and many phenotypes. Whole-genome reconstruction and variant discovery, 39 accomplished by analysis of data from whole-genome sequencing experiments, are 40 foundational for the study of human genomic variation and analysis of genotype-phenotype 41 relationships. Over the past decades, cost-effective whole-genome sequencing has been 42 revolutionized by short-fragment approaches, the most widespread of which have been the 43 consistently improving generations of the original Solexa technology [1, 2], now referred to as 44 Illumina sequencing. Illumina's strengths and weaknesses are inherent in the sample 45 preparation and sequencing chemistry. Illumina generates short paired reads (2x150 base pairs 46 for the highest-throughput platforms) from short fragments (usually 400-500 base pairs) [3]. 47 Because many clonally amplified molecules generate a robust signal during the sequencing 48 reaction, Illumina's average per-base error rates are very low. 50The lack of long-range contiguity between end-sequenced short fragments limits their 51 application for reconstructing personal genomes. Long-rang...

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“…Previously proposed algorithms enabled the inference of copy number aberrations (CNA) (e.g., deletion, amplification) of genomic segments [10, 16, 17] and novel adjacencies (NA) between genomic loci that are distant in the reference but are adjacent in cancer genomes [5–7, 9], in sequenced tumor samples. In the more recent study[18] a novel methodological framework RCK was proposed for reconstruction of cancer genomes karyotype graphs , taking into account both the CNA and NA signals, the non-haploid nature of both the reference and the derived genomes, and, when applicable, possible sample heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Recovering rearranged cancer chromosomes from karyotype graphs

et al. 2019

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BackgroundMany cancer genomes are extensively rearranged with highly aberrant chromosomal karyotypes. Structural and copy number variations in cancer genomes can be determined via abnormal mapping of sequenced reads to the reference genome. Recently it became possible to reconcile both of these types of large-scale variations into a karyotype graph representation of the rearranged cancer genomes. Such a representation, however, does not directly describe the linear and/or circular structure of the underlying rearranged cancer chromosomes, thus limiting possible analysis of cancer genomes somatic evolutionary process as well as functional genomic changes brought by the large-scale genome rearrangements.ResultsHere we address the aforementioned limitation by introducing a novel methodological framework for recovering rearranged cancer chromosomes from karyotype graphs. For a cancer karyotype graph we formulate an Eulerian Decomposition Problem (EDP) of finding a collection of linear and/or circular rearranged cancer chromosomes that are determined by the graph. We derive and prove computational complexities for several variations of the EDP. We then demonstrate that Eulerian decomposition of the cancer karyotype graphs is not always unique and present the Consistent Contig Covering Problem (CCCP) of recovering unambiguous cancer contigs from the cancer karyotype graph, and describe a novel algorithm CCR capable of solving CCCP in polynomial time. We apply CCR on a prostate cancer dataset and demonstrate that it is capable of consistently recovering large cancer contigs even when underlying cancer genomes are highly rearranged.ConclusionsCCR can recover rearranged cancer contigs from karyotype graphs thereby addressing existing limitation in inferring chromosomal structures of rearranged cancer genomes and advancing our understanding of both patient/cancer-specific as well as the overall genetic instability in cancer.

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Identifying structural variants using linked-read sequencing data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 65 publications

References 40 publications

Identification of genomic structure variation based on Hi-C technology and its application in tumor research

Identification of genomic structure variation based on Hi-C technology and its application in tumor research

Assessment of human diploid genome assembly with 10x Linked-Reads data

Recovering rearranged cancer chromosomes from karyotype graphs

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