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Cancer Genomics Technology Development

癌症基因组学技术开发

基本信息

批准号：
10702469
负责人：
PAUL S. MELTZER
金额：
$ 75.2万
依托单位：
DIVISION OF BASIC SCIENCES - NCI
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10702469
关键词：
Address Animal Model Base Sequence Basic Science Bioinformatics Biological Assay Cancer Biology Cell Line Cells Chromatin Chromosomes Clinical Clinical Research Code DNA DNA Methylation DNA Microarray Chip DNA Sequence DNA Sequence Alteration DNA copy number Data Deoxyribonuclease I Detection Development Fine needle aspiration biopsy Formalin Future Gene Expression Gene Expression Profiling Genes Genetic Transcription Genome Genomics Goals Growth Harvest Hospitals Human Human Genome Hypersensitivity Image Individual Laboratories Malignant Neoplasms Measurement Measures Methodology Methods Morphologic artifacts Mutation Mutation Detection Nucleic Acids Paraffin Embedding Pathology Pathway interactions Preparation Process Protocols documentation Reaction Research Role Sampling Scientist Single Nucleotide Polymorphism Site Small RNA Software Tools Specialist Specimen Structure Technology Testing Tissue Sample Tissues Translational Research Untranslated RNA Variant Work analysis pipeline base cancer cell cancer genome cancer genomics chromatin modification genome-wide analysis improved information model interest multidisciplinary next generation sequencing novel novel strategies pediatric patients technology development transcription factor transcriptome tumor whole genome

项目摘要

We have worked to extend the range of analyzable samples by developing protocols suitable for sample types including formalin fixed, paraffin embedded samples, flow sorted primary cells and fine needle aspirates. We have established that useful nucleic acid preparations can be obtained from these fixed tissues and are continuing to extend the analysis of this material for a wider range of genomic technologies, especially for sequencing. Indeed, current efforts have been directed primarily at the implementation of next generation sequencing technologies and the incorporation of long-read sequencing technologies. These methods primarily depend on producing an array of DNA molecules which are sequentially imaged during the sequencing reaction. We are investigating the use of these methods for gene expression profiling for large and small RNAs, for the detection of genome rearrangements, mutations, and for the measurement of chromatin modifications, DNase I hypersensitive sites, and transcription factor localization. A major part of this effort is the development and implementation of software tools can be used to analyze the massive amount of sequence data which is generated by this work. Although this is a challenging process, it ultimately will yield a streamlined analysis pipeline in which multiple sequence-based assays will be easy to integrate and free of array platform specific artifacts. Specific goals of our computational efforts include the optimization of pipelines to process sequence data for chromatin analysis, chromosome rearrangements, gene expression, and mutation detection. We are currently engaged in pursuing new approaches to target sequencing efforts to regions of interest such as the small proportion of the genome composed of genes, or subsets of genes of particular interest in order to be able to sequence thousands of genes in individual samples or in a complementary fashion, to sequence a few key genes in hundreds of samples. We are also developing efficient methods to target intergenic and intronic regions which are often the sites of important structural rearrangements in cancer. We make use of informative models as need to test our novel approaches to genome characterization. Recently, we have implemented newer long read technologies and are applying these to better characterize genome rearrangements and their effects on the transcriptome. These studies typically utilize cell line or animal models derived from cancers of interest in the laboratory. This work has extensive potential applications in basic, translational, and clinical research..

我们一直致力于通过开发协议来扩大可分析样本的范围适用于福尔马林固定、石蜡包埋、流式分选等样品类型原代细胞和细针抽吸物。我们已经确定了有用的核酸可以从这些固定的组织中获得制备物，并且正在继续延长分析这种材料的更广泛的基因组技术，特别是测序事实上，目前的努力主要是为了执行下一个代测序技术和长读段测序技术的结合。这些方法主要依赖于产生DNA分子的阵列，这些DNA分子依次在测序反应期间成像。我们正在研究使用这些方法进行基因大RNA和小RNA的表达谱，用于检测基因组重排，突变，以及用于测量染色质修饰，DNA酶I超敏位点，和转录因子定位。这项工作的一个主要部分是发展和软件工具的实现可用于分析大量的序列数据这是由这项工作产生的。虽然这是一个具有挑战性的过程，但它最终将产生一个流线型的分析管道，其中多个基于序列的测定将很容易集成且没有阵列平台特定工件。我们计算的具体目标努力包括优化处理染色质分析的序列数据的流水线，染色体重排、基因表达和突变检测。我们现在正忙着在寻求新的方法来将测序工作定位于感兴趣的区域，基因组的一小部分由基因组成，或特别感兴趣的基因的子集为了能够对单个样本或互补样本中的数千个基因进行测序，时尚，在数百个样本中测序几个关键基因。我们也在开发高效的靶向基因间区和内含子区的方法，癌症中的结构重排我们根据需要使用信息模型来测试我们的基因组表征的新方法。最近，我们实现了较新的长读技术，并将其应用于更好地表征基因组重排及其对转录组的影响。这些研究通常利用细胞系或动物模型在实验室中从感兴趣的癌症中获得。这项工作具有广泛的潜力在基础、转化和临床研究中的应用