Core C- Bioinformatics Core

核心C-生物信息学核心

• 批准号: 10533741
• 负责人: Vineet Bafna
• 金额: $ 17.62万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10533741
• 关键词: Algorithms; Area; Bioinformatics; Candidate Disease Gene; Cells; Code; Computer Analysis; Computing Methodologies; DNA Sequence Alteration; DNA methylation profiling; DNA sequencing; Data; Data Analyses; Data Set; Data Sources; Databases; Detection; Development; Elements; Epigenetic Process; Ethnic Origin; Folic Acid; Gene Frequency; Genes; Genetic; Genetic Transcription; Genomics; Genotype; Goals; Human; Inherited; Massive Parallel Sequencing; Meningomyelocele; Methods; Methylation; Mining; Minisatellite Repeats; Modality; Modeling; Modernization; Molecular Biology; Mus; Mutation; Neural tube; Parents; Pathogenicity; Pathway Analysis; Pathway interactions; Patients; Phenotype; Population; Productivity; Promoter Regions; Proteins; Rana; Reproducibility; Research Personnel; Risk; Sampling; Science; Services; Short Tandem Repeat; Statistical Models; Tandem Repeat Sequences; Techniques; Technology; Tissues; Validation; Variant; Visualization; Work; analysis pipeline; bioinformatics pipeline; bisulfite sequencing; candidate identification; computer science; data harmonization; data pipeline; dietary; differential expression; exome sequencing; gene environment interaction; gene interaction; genetic variant; improved; in silico; innovation; large datasets; method development; methylome; multiple omics; next generation sequencing; novel; programs; protein expression; protein function; protein protein interaction; risk variant; segregation; single cell analysis; single cell sequencing; single-cell RNA sequencing; statistics; transcriptome sequencing; whole genome

PROJECT SUMMARY – Core C: Bioinformatics Bioinformatics is the application of statistics and computer science to the field of molecular biology. It has emerged as a field unto itself, as the datasets that are generated by modern biomedical researchers easily exceeds what can be directly visualized. The vast amount of data increases the chance of false-negative and false-positive results, and argue for robust statistical models and reproducible workflows. Core C will work with the data generated from massive parallel sequencing from human, frog and mouse in Project I, II and III and Core B to extract variants that have potential to cause meningomyelocele or influence neural tube phenotypes. The PIs of the Projects and Cores have worked together extensively in the past, and have an established track record of productivity in the area of next generation sequencing (NGS) data analysis. Dr. Bafna has worked broadly in bioinformatics and genomics in the development computational methodologies employing novel algorithms and statistical techniques for NGS datasets. We envision that the DNA sequencing derived from Project I in the form of whole genome or whole exome sequencing from patients and their parents will be delivered to Core C for determination of potentially pathogenic risk-associated variant prioritization. RNA sequencing, single cell sequencing and epigenetic sequencing data generated from Core B, as well as imported from Project I, II and III, will be delivered to Core C for extraction of expression changes, which will be delivered to each of the Projects for segregation analysis and further validation. The Bioinformatics Core will provide these analysis pipelines to identify and annotate variants, and to develop innovative network analyses, RNAseq, Methylseq and single cell analysis to discover novel genetic mechanisms of MM based on Protein-Protein Interaction (PPI) and gene co-expression networks, to interpret large datasets from current genetic and genomic technologies, and to apply these in the different components of this Program Project. Although our primary goal is to provide service using existing computational methods, we expect that the Core B will also develop novel computational methods as required by the Projects and Cores, as we have done to develop our current WGS analysis pipeline. Methods development will be geared towards fundamental unsolved problems underlying the above four key functions, such as algorithms for correlating variants to phenotypes, further improvements in methods for computing epistatic interactions, detection of short tandem repeats and mobile elements from WGS, advanced methods for integration of genotypes with pathways, use of next- generation sequencing (NGS) in analysis of gene association, and discovery of genetic variants that influence protein expression or function.

项目摘要 - 核心C：生物信息学 生物信息学是统计和计算机科学在分子生物学领域的应用。它有 作为一个领域，作为一个现代生物医学研究人员生成的数据集出现 超过可以直接可视化的东西。大量数据增加了假阴性和 假阳性结果，并主张强大的统计模型和可再现的工作流程。核心将与 项目I，II和III和 核心B提取有可能引起脑膜瘤或影响神经管的变体 表型。过去的项目和核心的PI在过去已经广泛合作，并且有一个 在下一代测序（NGS）数据分析领域的生产力的建立记录。 Bafna博士 在采用开发计算方法的生物信息学和基因组学方面广泛工作 NGS数据集的新型算法和统计技术。我们设想DNA测序得出 以全基因组或整个外显子组测序的形式从患者及其父母进行的整个外显子组测序的形式 递送到核心C，以确定潜在的致病风险相关变体优先级。 RNA 测序，单细胞测序和表观遗传测序数据从核心B产生，并导入 从项目I，II和III项目中，将传递到Core C以提取表达更改，将交付 每个项目进行隔离分析和进一步验证。生物信息学核心将提供这些 分析管道以识别和注释变体，并开发创新的网络分析，RNASEQ， 基于蛋白质 - 蛋白质的甲基甲基和单细胞分析，以发现MM的新遗传机制 相互作用（PPI）和基因共表达网络，以解释当前遗传和 基因组技术，并将其应用于该程序项目的不同组成部分。虽然我们的 主要目标是使用现有计算方法提供服务，我们希望核心B也将 按照我们开发的项目和核心的要求，开发了新颖的计算方法 我们当前的WGS分析管道。方法开发将针对基本未解决 上述四个关键函数的基础问题，例如将变体与表型相关的算法， 进一步改进了计算上皮相互作用的方法，检测短串联重复序列和 WGS的移动元素，用于将基因型与途径集成的高级方法，使用下一步 在基因关联分析中生成测序（NGS），并发现影响的遗传变异 蛋白质表达或功能。