Core C- Bioinformatics Core

项目摘要-核心C：生物信息学 生物信息学是统计学和计算机科学在分子生物学领域的应用。它有 作为一个领域出现，作为由现代生物医学研究人员轻松生成的数据集 超出了可以直接可视化的范围。海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重复的工作流程。核心C将与 项目I、II和III中对人、青蛙和小鼠进行大规模平行测序所产生的数据 核心B提取可能导致脑膜脊髓膨出或影响神经管的变体 表型。项目和核心的绩效指标在过去曾广泛合作，并具有 建立了下一代测序(NGS)数据分析领域的生产率跟踪记录。巴夫纳博士 在生物信息学和基因组学的开发计算方法论方面进行了广泛的工作 NGS数据集的新算法和统计技术。我们设想DNA测序是从 从项目I以患者及其父母的全基因组或全外显子组测序的形式 交付给核心C，以确定潜在致病风险相关变异的优先顺序。核糖核酸 测序、单细胞测序和表观遗传测序数据，从核心B产生以及进口 项目I、II和III将交付给核心C，以提取表情变化，并将交付 对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， 甲基化和单细胞分析发现多发性骨髓瘤基于蛋白质-蛋白质的新遗传机制 相互作用(PPI)和基因共表达网络，以解释来自当前遗传和 基因组技术，并将这些技术应用于本计划项目的不同组成部分。虽然我们的 主要目标是使用现有的计算方法提供服务，我们预计核心B也将 根据项目和核心的要求开发新的计算方法，就像我们为开发 我们目前的WGS分析管道。方法开发将面向根本未解决的问题 上述四个关键功能的潜在问题，例如将变体与表型相关联的算法， 上位性相互作用的计算方法、短串联重复序列的检测和 来自WGS的移动元件，整合基因和途径的先进方法，使用NEXT- 世代测序(NGS)在基因关联分析和发现影响基因的遗传变异中的作用 蛋白质的表达或功能。

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.