人类基因组变异的可扩展知识图谱构建与推理
Scalable Knowledge Graph Construction and Inference on Human Genome Variants
AI总结：
本文将未接种疫苗COVID-19患者RNA测序变异数据、VCF注释与CADD评分构建为可扩展RDF知识图谱，并通过图查询和GNN分类案例验证其推理能力。
摘要：
现实世界的知识可以表示为由实体及实体之间关系组成的图。在处理RNA测序等海量基因组数据时，需要高效且可扩展的解决方案。知识图谱为此类大规模基因组数据中的分析和推理等各种任务提供了一种强大方法。在本研究中，从未接种疫苗的COVID-19患者RNA序列中提取的变异层面信息被表示为一个统一的大型知识图谱。研究对包含变异层面信息的变异调用格式（VCF）文件进行注释，以为每个变异加入进一步信息。随后，将注释文件中的数据记录转换为资源描述框架（RDF）三元组。每个获得的VCF文件都有一个关联的CADD评分文件，其中包含每个变异的原始评分和Phred标度评分。研究为VCF和CADD评分文件定义了本体。利用该本体和提取的信息，创建了一个大型、可扩展的知识图谱。随后利用现有的图存储进行查询并创建数据集，以服务于进一步的下游任务。我们还展示了一个使用该知识图谱的案例研究，并使用图机器学习执行分类任务。同时，我们在该案例研究中对不同的图神经网络（GNN）进行了比较。
英文摘要：
Real-world knowledge can be represented as a graph consisting of entities and relationships between the entities. The need for efficient and scalable solutions arises when dealing with vast genomic data, like RNA-sequencing. Knowledge graphs offer a powerful approach for various tasks in such large-scale genomic data, such as analysis and inference. In this work, variant-level information extracted from the RNA-sequences of vaccine-naïve COVID-19 patients have been represented as a unified, large knowledge graph. Variant call format (VCF) files containing the variant-level information were annotated to include further information for each variant. The data records in the annotated files were then converted to Resource Description Framework (RDF) triples. Each VCF file obtained had an associated CADD scores file that contained the raw and Phred-scaled scores for each variant. An ontology was defined for the VCF and CADD scores files. Using this ontology and the extracted information, a large, scalable knowledge graph was created. Available graph storage was then leveraged to query and create datasets for further downstream tasks. We also present a case study using the knowledge graph and perform a classification task using graph machine learning. We also draw comparisons between different Graph Neural Networks (GNNs) for the case study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
