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Integrative genomic and epigenomic analysis of cancer using long read sequencing

使用长读长测序对癌症进行综合基因组和表观基因组分析

基本信息

批准号：
10599150
负责人：
MICHAEL SCHATZ
金额：
$ 35.4万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10599150
关键词：
Acceleration Address Algorithms Alleles Automobile Driving Basic Science Bioinformatics Biological Sciences Cancer Biology Cancerous Cataloging Characteristics Clinical Communities Compensation Complex Computing Methodologies Copy Number Polymorphism Cytosine DNA Sequence Rearrangement DNA Transposable Elements Data Data Set Detection Development Diagnostic Disease Ensure Epigenetic Process Gene Expression Profiling Generations Genes Genetic Genetic Variation Genetic study Genome Genomics Genotype Goals Graph Growth Health Individual Joints Karyotype Machine Learning Malignant Neoplasms Maps Mediating Methods Methylation Minisatellite Repeats Modeling Monitor Mutation Nature Normal tissue morphology Oncogenes Outcome Pathogenicity Patients Phase Population Protein Isoforms Recurrence Repetitive Sequence Research Research Personnel Resolution Resources Role Sample Size Sampling Signal Transduction Somatic Mutation Statistical Methods Structure System Tandem Repeat Sequences Technology Tissues Tumor Suppressor Proteins Variant Work cancer genome cancer genomics cancer initiation cancer risk cancer therapy cancer type cohort disorder risk driver mutation empowerment epigenetic profiling epigenetic variation epigenome epigenomics experience fusion gene genetic pedigree genetic variant genome analysis genome sequencing improved indexing insight instrument methylome mosaic mosaic variant nanopore novel novel strategies open source power analysis premalignant prognostic indicator risk variant sequencing platform single molecule transcriptome transcriptomic profiling transcriptomics tumor tumor heterogeneity tumor progression

项目摘要

PROJECT SUMMARY The last twenty years have experienced extensive growth in the sequencing of cancer genomes, leading to a dramatically increased understanding of the role of genetic and epigenetic mutations in cancer. This has largely been enabled by developments in high-throughput “second-generation” sequencing technology and analysis that characterize cancer genomes using short-reads. Recently, a new generation of high-throughput long-read sequencing instruments, primarily from Pacific Biosciences and Oxford Nanopore, have become available that are poised to displace short-read sequencing for many applications. We and others have used these technologies to discover tens of thousands of variants per cancer genome that are not detectable using short-reads, including structural variants and differentially methylated regions in known oncogenes and cancer risk genes. These technologies carry the potential to address many open questions in cancer biology, however, the analysis of long-read sequencing data is computationally demanding and needs specialized algorithms that are either too inefficient to use at scale or do not yet exist. In this proposal, we will address several gaps in the application of long-read technology for basic research and clinical use in cancer genomics. First, we will develop improved methods for finding structural variants and complex repeat expansions from long-reads, both of which are major diagnostic and prognostic indicators of disease, yet are not accurately identified using existing methods. Leveraging the improved phasing capabilities of long reads, this work will include the detection of mosaic variants, revealing tumor heterogeneity and variants in precancerous tissues. Next, we will apply machine learning and systems level advances to accelerate and improve the comparison of variants across large patient cohorts. Critically, this will compensate for the error prone nature of single molecule long-read sequencing to make these comparisons more accurate when comparing tumor-normal samples or pedigrees of related patients so that recurrent driving mutations can be accurately identified. Finally, we will develop integrative methods for the joint analysis of genome, transcriptome, and epigenetic profiling of cancer genomes. These advances will improve the identification of fusion genes, and allow for entirely new forms of epigenetic analysis, such as the allele-specific analysis of methylation across transposable elements and other repetitive elements. Synthesizing the many thousands of novel variants we will detect using our methods, we will then develop algorithms that will identify and evaluate recurrent genetic or epigenetic variations as putative driving mutations. All methods will be released open-source and will empower us, our ITCR collaborators, and the cancer genomics community at large to study genetic and epigenetic variants with near perfect accuracy and thereby unlock many new associations to treatment and disease.

项目概要过去二十年来，癌症基因组测序经历了广泛的发展，导致了 dramatically increased understanding of the role of genetic and epigenetic mutations in cancer. This has largely 高通量“第二代”测序技术和分析的发展使之成为可能 that characterize cancer genomes using short-reads. Recently, a new generation of high-throughput long-read 主要来自 Pacific Biosciences 和 Oxford Nanopore 的测序仪器已经上市， are poised to displace short-read sequencing for many applications.我们和其他人已经使用过这些发现每个癌症基因组数以万计的变异的技术，这些变异是使用无法检测到的 short-reads, including structural variants and differentially methylated regions in known oncogenes and cancer 风险基因。这些技术有可能解决癌症生物学中的许多悬而未决的问题，但是，长读长测序数据的分析对计算要求很高，并且需要专门的算法要么效率太低而无法大规模使用，要么还不存在。在本提案中，我们将解决以下几个问题：长读技术在癌症基因组学基础研究和临床中的应用。首先，我们将 develop improved methods for finding structural variants and complex repeat expansions from long-reads, 两者都是疾病的主要诊断和预后指标，但尚未通过使用准确识别现有的方法。利用长读取改进的定相能力，这项工作将包括检测镶嵌变异，揭示肿瘤异质性和癌前组织的变异。接下来，我们将 apply machine learning and systems level advances to accelerate and improve the comparison of variants 跨越大量患者群体。 Critically, this will compensate for the error prone nature of single molecule 长读长测序使这些比较在比较肿瘤与正常样本或相关患者的家谱，以便能够准确识别反复发生的驱动突变。最后，我们将 develop integrative methods for the joint analysis of genome, transcriptome, and epigenetic profiling of cancer 基因组。 These advances will improve the identification of fusion genes, and allow for entirely new forms of epigenetic analysis, such as the allele-specific analysis of methylation across transposable elements and other repetitive elements.综合我们将使用我们的方法检测到的数千种新变体，我们然后将开发算法来识别和评估反复出现的遗传或表观遗传变异推定的驱动突变。 All methods will be released open-source and will empower us, our ITCR collaborators, and the cancer genomics community at large to study genetic and epigenetic variants with near perfect accuracy and thereby unlock many new associations to treatment and disease.

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Long read mitochondrial genome sequencing using Cas9-guided adaptor ligation.

DOI：
10.1016/j.mito.2022.06.003
发表时间：
2022-07
期刊：
MITOCHONDRION
影响因子：
4.4
作者：
Vandiver, Amy R.;Pielstick, Brittany;Gilpatrick, Timothy;Hoang, Austin N.;Vernon, Hillary J.;Wanagat, Jonathan;Timp, Winston
通讯作者：
Timp, Winston

Sigmoni: classification of nanopore signal with a compressed pangenome index.

Sigmoni：使用压缩的泛基因组索引对纳米孔信号进行分类。