Cross-platform structural variant discovery with deep learning

通过深度学习跨平台结构变体发现

• 批准号: 10453237
• 负责人: Victoria Popic
• 金额: $ 59.39万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10453237
• 关键词: Algorithms; Alzheimer' s Disease; Architecture; Autoimmune Diseases; Benchmarking; Cardiovascular Diseases; Clinical; Communities; Complex; Computer Vision Systems; Computer software; Consensus; Coupled; Data; Data Reporting; Data Set; Detection; Development; Diagnosis; Dimensions; Disease; Engineering; Ensure; Evaluation; Formulation; Generations; Genetic; Genetic Diseases; Genetic Variation; Genome; Genotype; Goals; Hand; Hand functions; Hi-C; Human Genetics; Human Genome; Hybrids; Image; Learning; Link; Machine Learning; Malignant Neoplasms; Manuals; Medicine; Methodology; Methods; Mind; Modeling; Pattern; Performance; Play; Property; Research; Resolution; Role; Sampling; Science; Sequence Alignment; Signal Transduction; Source; Statistical Models; Structural Models; Structure; Techniques; Technology; Training; Variant; Work; autism spectrum disorder; blind; cancer genome; convolutional neural network; deep learning; deep learning model; deep neural network; design; diverse data; engineering design; experimental study; flexibility; genome sequencing; genomic data; heuristics; improved; method development; nervous system disorder; neural network; precision medicine; prototype; sequencing platform; simulation; tumor; variant detection; whole genome

Structural variants (SV) are a major driver of the genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine and our understanding of human genetics. Due to revolutionary breakthroughs in whole-genome sequencing technologies, we now have access to genomic data at an unprecedented scale and resolution. However, despite tremendous effort and progress in SV calling methodology, general SV discovery still remains unsolved. Existing techniques use hand-engineered features and heuristics to model SV classes, relying heavily on developer expertise, which cannot scale to the vast diversity of SV types and sequencing platforms nor fully harness all the information available in raw sequencing data. As a result, these methods are usually tightly coupled to the properties of a particular sequencing technology and operate optimally only on certain SV types and sizes, rendering us blind to many other classes of SVs and their role in disease. Deep neural networks have the ability to learn complex abstractions automatically from the data and hence offer a promising avenue for general SV discovery. Deep learning has recently transformed the field of machine learning and led to remarkable advances in science and medicine. In this proposal we aim to leverage the potential of deep learning for the problem of SV detection. We lay out how to efficiently formulate SV detection as a deep learning task, and propose the development of a comprehensive framework to call and genotype SVs of different size and type, including complex and subclonal SVs, given data from a range of sequencing platforms. In particular, we demonstrate that state-of-the-art results can be obtained using our approach for short, linked, and long read datasets. In order to ensure that our models generalize across different datasets, an important goal of our proposal is also to assemble diverse and representative training data and perform extensive evaluation using publicly- available multi-platform datasets to accurately assess model performance. Our software will be built with extensibility and scalability in mind, and will be released, along with pretrained models and callsets, freely to the community.

结构变异（SV）是人类基因组遗传多样性和疾病的主要驱动力 对于精确医学的进步和我们对人类遗传学的理解至关重要。由于 全基因组测序技术的革命性突破，我们现在可以访问一个基因组数据 空前的规模和解决方案。但是，尽管SV通话方法巨大的努力和进展，但 SV发现将军仍然未解决。现有技术使用手工设计的功能和启发式方法 模型SV类，严重依赖开发人员专业知识，这些专业知识无法扩展到SV类型的大量多样性和 测序平台或完全利用原始测序数据中可用的所有信息。结果，这些 方法通常紧密耦合到特定测序技术的属性，并最佳地运行 仅在某些SV类型和大小上，使我们对许多其他类别的SV及其在疾病中的作用视而不见。深的 神经网络能够从数据自动学习复杂的摘要，因此提供了 通用SV发现的有希望的大道。深度学习最近改变了机器学习领域 并在科学和医学方面取得了显着进步。在此提案中，我们旨在利用深厚的潜力 学习SV检测问题。我们布置了如何有效地提出SV检测作为深度学习 任务，并建议开发一个综合框架，以调用不同规模的svs和基因型SV 类型，包括复杂和亚克隆SVS，给出了来自一系列测序平台的数据。特别是我们 证明可以使用我们的方法来简短，链接和长阅读来获得最新的结果 数据集。为了确保我们的模型在不同数据集中概括，这是我们建议的重要目标 还要组装多样化和代表性的培训数据，并使用公开进行广泛的评估 可用的多平台数据集可准确评估模型性能。我们的软件将与 可扩展性和可伸缩性，并将与验证的型号和呼叫一起释放，自由地向 社区。