Bioinformatics and Biostatistics Core

生物统计和生物信息学核心(BBC)支持统计、生物信息学和计算学 Discovery和靶向蛋白质组学核心以及中心研究人员及其博士后的需求 助理和学生，以及飞行员资助奖获得者。BBC有四个相互关联的具体目标：1) 生物统计学；2)生物信息学；3)高性能计算；4)培训和教育。在目标1中， 我们将为实验设计和数据分析提供统计指导，包括样本质量 对各种类型的蛋白质组学数据集进行评估和探索性分析；继续开发 并为蛋白质组学数据分析和可视化工具ProteomicsBrowser添加更多特性/功能 由BBC开发，以帮助中心用户更好地解释复杂的蛋白质组学数据；开发和 实施新的统计方法以计算蛋白质组学数据中的缺失信息； 实现了一个在线的蛋白质组数据前处理工具，包括数据归一化、批处理效果 更正和缺失数据归罪。在目标2中，我们将提供先进的生物信息学软件和 协助中心调查人员和飞行员获奖者充分解释他们的比较 蛋白质和蛋白质翻译后修饰图谱数据；我们将利用来自单细胞的信息 RNA-SEQ通过将细胞类型级别的随机表达合并到分析框架中来去卷积 组织水平转录(RNA-seq)为个别样本的组成细胞类型的一部分，以及 确定在RNA和蛋白质水平之间显示显著差异的基因和细胞类型；并制定一种 用于检测等位基因特异性多肽和等位基因特异性事件的统一计算框架 现有的全基因组测序、全转录组测序和蛋白质组学数据 死后的人脑样本。在目标3中，我们将继续为大规模多肽提供支持 序列比对和支持整合基因组、转录组和蛋白质组数据集的新管道； 与生物信息学和生物统计团队密切合作，帮助制定基准、扩展、优化和加快 涉及大规模数据分析和数据库查询的计算任务；并探索替代方案 容器系统、私有云计算等传统高性能计算环境。 在目标4中，我们将提供生物统计学、生物信息学、数据库和高性能方面的培训和教育 通过与中心调查人员的互动和协作进行计算，包括与 耶鲁大学医学图书馆生物信息学支持计划和其他耶鲁组织。

The Biostatistics and Bioinformatics Core (BBC) supports statistical, bioinformatic and computational needs of the Discovery and Targeted Proteomics Cores, as well as Center Investigators, their postdoctoral associates and students, and Pilot Grant awardees. The BBC has four inter-related Specific Aims: 1) Biostatistics; 2) Bioinformatics; 3) High Performance Computing; 4) Training and Education. In Aim 1, we will provide statistical guidance on experimental design and data analysis, including sample quality assessment, and exploratory analysis for a wide range of types of proteomics data sets; continue to develop and add more features/functions to ProteomicsBrowser, a proteomics data analysis and visualization tool developed by the BBC to assist Center users in better interpreting complex proteomics data; develop and implement novel statistical methods to impute missing information in proteomics data; and develop and implement an online tool for proteomics data preprocessing, including data normalization, batch effect correction, and missing data imputation. In Aim 2, we will provide advanced bioinformatics software and approaches to assist Center investigators and Pilot Grant awardees in fully interpreting their comparative protein and protein post-translational modification profiling data; we will leverage information from single-cell RNA-seq by incorporating stochastic expression at the cell-type-level into an analytic framework to deconvolve tissue-level transcriptomics (RNA-seq) into fractions of constituent cell types for individual samples, and identify genes and cell types showing significant discrepancies between RNA and protein levels; and develop a unified computational framework for the detection of allele-specific peptides and allele-specific events from existing whole-genome sequencing, whole-transcriptome sequencing, and proteomics data generated from post-mortem human brain samples. In Aim 3, we will provide continued support for large-scale peptide sequence alignment and support novel pipelines to integrate genomic, transcriptomic, and proteomic datasets; work closely with the bioinformatics and biostatistics teams to help benchmark, scale, optimize, and speed up computing tasks involving large-scale data analyses and database queries; and explore alternatives to traditional high performance computing environments such as container systems and private cloud computing. In Aim 4, we will provide training and education in biostatistics, bioinformatics, database and high performance computing through interaction and collaboration with the Center investigators, including working closely with the Yale Medical Library Bioinformatics Support Program and other Yale organizations.