Advanced development of Lancet, an emerging tool for complex variant calling in cancer genomics

柳叶刀的高级开发，一种用于癌症基因组学中复杂变异调用的新兴工具

• 批准号: 10491232
• 负责人: Giuseppe Narzisi
• 金额: $ 49.08万
• 依托单位: NEW YORK GENOME CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10491232
• 关键词: Address; Adoption; Advanced Development; Algorithms; Bandage; Behavior; Blood; Chromosomes; Color; Communities; Complex; Computer software; DNA sequencing; Data; Detection; Development; Disease; Disseminated Malignant Neoplasm; Documentation; Engineering; Event; Evolution; Genome; Genomic Segment; Goals; Graph; Haplotypes; Joints; Knowledge; Libraries; Link; Longitudinal cohort; Malignant Neoplasms; Manuals; Methods; Modeling; Modernization; Mutation; Patients; Performance; Phase; Play; Primary Neoplasm; Publications; Relapse; Research; Research Personnel; Resolution; Role; Sample Size; Sampling; Single Nucleotide Polymorphism; Somatic Mutation; Source Code; Structure; Testing; The Cancer Genome Atlas; Time; Update; Variant; Visualization; anticancer research; base; cancer cell; cancer genomics; cancer therapy; cohort; cost; data visualization; design; exome sequencing; genome analysis; genome sequencing; genome-wide; high throughput analysis; improved; improved functioning; insertion/deletion mutation; light weight; longitudinal analysis; novel strategies; performance tests; programs; prototype; tool; tumor; tumor progression; tumorigenesis; user-friendly; variant detection; web site; whole genome

ABSTRACT One of the central challenges in cancer genomics is the ability to accurately detect somatic mutations in heterogeneous tumors, and precisely determine their clonal origin and evolution. This fundamental knowledge is central to the discovery of new cancer therapies. In recent years, reductions in the cost of whole-genome and whole-exome sequencing have enabled researchers to address these questions in unprecedented detail. However, a major limitation in the field has been a paucity of methods for variant calling that extend beyond identifying simple single-nucleotide variants (SNVs) and small indels to allow the characterization of complex structural changes that also play a significant role in tumorigenesis and cancer progression. Indels of more than a few bases are challenging to discover with typically used alignment-based methods. In addition, most variant callers analyze tumor and normal data separately, which can introduce false positives such as when a mutation shows partial support in the normal sample. Towards addressing these shortcomings, we recently introduced Lancet, a new somatic variant caller developed under the auspices of the ITCR R21. Lancet leverages local assembly and joint analysis of tumor-normal paired data using region-focused colored de Bruijn graphs, with on- the-fly repeat composition analysis and a self-tuning k-mer strategy. This results in relatively reduced reference bias; an improved ability to detect variations that significantly diverge from the reference chromosome representations; a reduction in the scale of the analysis, leading to increased power and sensitivity to detect variants through localized, comprehensive graph exploration; and dynamic adjustment of calling behavior according to the sequence conditions of each genomic region. In testing, Lancet shows superior performance to all major alignment-based methods in terms of accuracy, particularly in the detection of ‘twilight zone’ indels (30- 250 bp). Given its continued adoption and successful application in over a dozen high-impact publications, Lancet is poised for more advanced development to enable continued improvements in its variant calling power, precision, and analytical capabilities. Specifically, Lancet is currently limited by longer runtimes than alignment- based methods, reduced sensitivity for longer insertions, lack of interactive visualization of the colored de Bruijn graph, and the inability to jointly analyze longitudinal data. To address these limitations, we propose the following Specific Aims: 1) Increase computational performance and facilitate user adoption and third-party development; 2) Add new features and enhancements to improve variant detection, phasing, and data visualization; and 3) Enable joint assembly and analysis of longitudinal data. Impact: With additional development, the next iteration of Lancet will feature advanced algorithms for fast, efficient, accurate, localized, user-friendly, and application- modifiable variant analysis of phased genome-wide timeseries data, establishing it as one of the leading methods for variant calling in cancer research.

抽象的 癌症基因组学的核心挑战之一是能够准确检测到体细胞突变的能力 异质性肿瘤，并精确地确定其克隆起源和进化。这种基本知识 是发现新的癌症疗法的核心。近年来，全基因组和 全外观测序使研究人员能够以前所未有的细节解决这些问题。 但是，该领域的主要限制是对变体呼叫的方法的稀少 识别简单的单核苷酸变体（SNV）和小实例，以允许复杂的表征 结构变化在肿瘤发生和癌症进展中也起着重要作用。多数超过 挑战一些基本的挑战是通过典型使用的基于对齐的方法发现。另外，大多数变体 呼叫者分别分析肿瘤和正常数据，这可以引入误报，例如突变时 在正常样本中显示部分支持。为了解决这些缺点，我们最近介绍了 柳叶刀是在ITCR R21的主持下开发的一种新的体细胞呼叫者。柳叶刀利用本地 使用以区域为中心的彩色de Bruijn图对肿瘤正常配对数据进行组装和联合分析，并具有 即时重复的组成分析和自我调整的K-MER策略。这导致相对降低的参考 偏见;提高的检测变化的能力，与参考染色体显着不同 表示；分析规模的减少，导致功率和灵敏度提高检测 通过局部，全面的图形探索变体；和呼叫行为的动态调整 根据每个基因组区域的序列条件。在测试中，柳叶刀表现出优越的性能 所有基于准确性的主要对齐方法的精度，尤其是在检测“暮光区” Indels（30-- 250 bp）。鉴于它继续采用并在十几个高影响力出版物中成功应用 柳叶刀被毒化以进行更先进的开发，以使其变异的呼叫能力持续改善， 精度和分析能力。具体而言，柳叶刀目前的限制比对齐时间更长 - 基于较长插入的敏感性降低，彩色de Bruijn缺乏互动可视化 图形以及无法共同分析纵向数据。为了解决这些限制，我们建议以下 具体目的：1）提高计算绩效并促进用户采用和第三方开发； 2）添加新功能和增强功能，以改善变体检测，相位和数据可视化； 3） 实现纵向数据的联合组装和分析。影响：随着额外的发展，下一次迭代 Lancet的of of Advanced算法将用于快速，高效，准确，本地化，用户友好和应用程序 - 对分阶段基因组的时间表数据的可修改变体分析，将其确定为领先的方法之一 用于癌症研究的变体。