Neurogenomics/Bioinformatics Core

神经基因组学/生物信息学核心

• 批准号: 8516542
• 负责人: DANIEL H GESCHWIND
• 金额: $ 13.73万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8516542
• 关键词: Behavior; Bioinformatics; Biological; Biomedical Research; Core Facility; DNA; Data; Data Set; Data Storage and Retrieval; Dideoxy Chain Termination DNA Sequencing; Educational process of instructing; Epigenetic Process; Experimental Designs; Functional RNA; Future; Gene Expression Profile; Gene Expression Profiling; Genes; Genetic; Genome; Genomics; Heterogeneity; Informatics; International; Investigation; Knowledge; Laboratories; Laboratory Organism; Lead; Maps; Marketing; Mental Retardation and Developmental Disabilities Research Centers; Methods; Nervous system structure; Neurosciences; Neurosciences Research; Process; Protocols documentation; Publications; RNA Sequences; Reading; Research; Running; Science; Series; Services; Societies; System; Techniques; Technology; Tissues; Variant; base; cost; experience; follow-up; functional genomics; genome sequencing; histone modification; insight; intellectual and developmental disability; interest; man; meetings; member; next generation sequencing; research study; statistics

The availability of genome sequence for a wide variety of experimental organisms and man has challenged us to use this information in the most effective manner. This Core focuses on the application of genome-level analyses in neuroscientific investigation, most based on microarray related technologies, but also supporting a burgeoning use of next-generation sequencing-based approaches, such as RNA sequencing (RNA-seq) and ChlP-seq. For the foreseeable future, expertise in both microarray and sequencing applications need to be maintained within the Core, as both of these technologies are the Core platforms used in functional genomics today. The nervous system poses specific challenges, such as unprecedented tissue heterogeneity and the need to study the genome in relation to circuits and behavior. Further, the analysis of large-scale data sets requires specific expertise in bioinformatics that most neuroscience laboratories lack. It is not practical for most laboratories to become fully proficient in all of the aspects of experimental design and statistics to amplification and hybridization technology that is necessary to perform adequately powered microarray experiments. Furthermore, although Core facilities exist to perform microarray hybridizations, the downstream bioinformatic follow up and statistical support needed to bring a study to completion over a series of months or even years is sorely absent for members of the IDDRC. This is why in the past, many performed such experiments, but many experiments did not lead to publications because the analytic know how was lacking. Further, how to move from a list of genes to biological knowledge poses additional challenges that most laboratories cannot meet alone. The recent advent of high-throughput next-generation sequencing (NGS) is having a major impact in the sciences, especially in biomedical research. Since its introduction to the market in 2005, massively parallel sequencing has dramatically altered genomic research. The degree of throughput and the decreasing cost per base, along with a relatively low error rate, have made it possible to obtain genomic sequence information on a previously unimaginable scale and at a cost that is dramatically lower than that achievable with traditional Sanger sequencing. In addition, these new systems are extending the field to new applications, previously out of reach for conventional sequencing, such as gene expression profiling, small non-coding RNA profiling, structural variant analysis, and analysis of epigenetic modifications of histones and DNA. Soon, whole-genome sequencing will replace existing targeted array technologies and reveal new insights into transcriptomes, genetic and genomic variation, and allow for systematic epigenetic profiling. In particular, RNA-seq is expected to completely change the landscape in the field of gene expression analysis, due to the powerful combination of increased throughput, unprecedented detail, and significantly lower cost. Given the enormous amount of data generated in any NGS experiment, issues related to informatics have been magnified by 1-2 orders of magnitude. Specifically, this is due to massively increased storage and processing capacity for NGS data, as well as the need for mapping reads to the genome for every run. Thus, now more than ever, cores with both data storage, processing and bioinformatics capability are necessary to help support modern neuroscience research.

基因组序列可用于多种实验生物和人类的挑战，要求我们以最有效的方式使用这些信息。该核心侧重于基因组水平分析在神经科学研究中的应用，大多数基于与微阵列相关的技术，但也支持对下一代测序方法的新兴使用，例如RNA测序（RNA-SEQ）和CHLP-SEQ。在可预见的将来，需要保持在核心内的微阵列和测序应用程序方面的专业知识，因为这两种技术都是当今功能基因组学中使用的核心平台。 神经系统提出了特定的挑战，例如前所未有的组织异质性，以及与电路和行为有关的基因组的需求。此外，对大规模数据集的分析还需要大多数神经科学实验室缺乏的生物信息学方面的特定专业知识。对于大多数实验室而言，在实验设计和统计数据的所有方面都可以完全熟练地进行扩增和杂交技术，这是进行足够动力的微阵列实验所必需的。 此外，尽管存在用于进行微阵列杂交的核心设施，但对于IDDRC的成员来说，非常缺乏在一系列几个月甚至几年内完成研究完成的下游生物信息的后续和统计支持。这就是为什么过去，许多人进行了这样的实验，但是许多实验并没有导致出版物，因为该分析知道如何缺乏。此外，如何从基因列表转变为生物知识还带来了大多数实验室无法独自面对的其他挑战。 高通量下一代测序（NGS）最近出现的是对科学产生重大影响，尤其是在生物医学研究中。自2005年推出市场以来，大规模平行的测序发生了巨大的基因组研究改变。吞吐量的程度和每个基数的成本以及相对较低的错误率，使得以先前难以想象的量表获得基因组序列信息，并且以比传统的Sanger测序相比可实现的成本低。此外，这些新系统将该领域扩展到了新的应用程序，以前无法实现常规测序，例如基因表达分析，小型非编码RNA分析， 结构变异分析以及组蛋白和DNA的表观遗传修饰的分析。很快，全基因组测序将取代现有的靶向阵列技术，并揭示对转录组，遗传和基因组变异的新见解，并允许系统的表观遗传分析。特别是，由于吞吐量增加，前所未有的细节和成本明显较低的组合，RNA-Seq有望完全改变基因表达分析领域的景观。考虑到任何NGS实验中产生的大量数据，与信息学有关的问题已通过1-2个数量级放大。具体而言，这是由于NGS数据的存储和处理能力大大提高，以及每次运行的映射读数的需求。因此，现在比以往任何时候都更重要的是，具有数据存储，处理和生物信息学能力的核心对于帮助现代神经科学研究是必要的。