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: 10697390
• 负责人: Giuseppe Narzisi
• 金额: $ 46.96万
• 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/10697390
• 关键词: Acceleration; Address; Adoption; Advanced Development; Algorithms; Bandage; Behavior; Blood; Chromosomes; Code; Color; Communities; Complex; Computer software; DNA sequencing; Data; Detection; Development; Disease; Disseminated Malignant Neoplasm; Documentation; Engineering; Event; Evolution; Friends; Genome; Genomic Segment; Goals; Graph; Haplotypes; Joints; Knowledge; Libraries; Link; Malignant Neoplasms; Manuals; Methods; Modeling; Modernization; Mutation; Patients; Performance; Phase; Play; Primary Neoplasm; Publications; Research; Research Personnel; Resolution; Role; Running; Sample Size; Sampling; Scientific Advances and Accomplishments; 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; 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.

摘要 癌症基因组学的核心挑战之一是准确检测细胞突变的能力 异质性肿瘤，并精确地确定其克隆起源和进化。这一基础知识 是发现新的癌症疗法的核心。近年来，全基因组和全基因组成本的降低 整个外显子组测序使研究人员能够以前所未有的细节解决这些问题。 然而，该领域的一个主要限制是缺乏可扩展的变量调用方法 识别简单单核苷酸变异体(SNV)和小INDELs，以便对复合体进行表征 结构变化在肿瘤发生和癌症进展中也起着重要作用。INDELS超过 使用通常使用的基于比对的方法来发现一些碱基是具有挑战性的。此外，大多数变种 呼叫者分别分析肿瘤和正常数据，这可能会导致假阳性，例如当突变时 显示了正常样本中的部分支持。为了解决这些缺点，我们最近推出了 Lancet是在ITCR R21的支持下发展起来的一个新的体细胞变异愈伤组织。《柳叶刀》利用当地 用区域聚焦彩色de Bruijn图组装和联合分析肿瘤-正常配对数据 Fly-Repeat成分分析和自校正k-mer策略。这会导致相对较少的引用 偏倚；检测显著偏离参考染色体的变异的改进能力 表示；分析规模的缩小，导致检测能力和灵敏度的提高 通过本地化、全面的图形探索实现变体；以及动态调整调用行为 根据各基因组区域的测序情况进行编码。在测试中，Lancet显示出优于 在准确性方面，所有主要的基于比对的方法，特别是在检测‘暮光地带’INDELS(30- 250个碱基)。鉴于其在十几种高影响力出版物中的持续采用和成功应用， Lancet准备进行更高级的开发，以实现其变体调用能力的持续改进， 精确度和分析能力。具体地说，Lancet目前受到运行时间比Align更长的限制- 基于方法，对较长插入的敏感度降低，缺乏彩色De Bruijn的交互可视化 图表，以及无法联合分析纵向数据。为了解决这些限制，我们提出了以下建议 具体目标：1)提高计算性能，促进用户采用和第三方开发； 2)添加新功能和增强功能，以改进变异检测、阶段化和数据可视化；以及3) 启用接头装配和纵向数据分析。影响：随着进一步的开发，下一次迭代 将采用先进的算法，实现快速、高效、准确、本地化、用户友好和应用程序- 对阶段性全基因组时间序列数据进行可修改的变异分析，将其确立为主导方法之一 癌症研究中的变种呼唤。