An Integrative Bioinformatics Platform with Application in Single Cancer Cells

应用于单个癌细胞的综合生物信息学平台

基本信息

批准号：
9321082
负责人：
Lana X Garmire
金额：
$ 34.45万
依托单位：
UNIVERSITY OF HAWAII AT MANOA
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9321082
关键词：
Acute Erythroblastic Leukemia Acute Myelocytic Leukemia Address Big Data Bioinformatics Biological Biological Assay Cancer cell line Cell Line Cells Collaborations Communities Computer software Computing Methodologies Copy Number Polymorphism Data Data Set Development Evolution Generic Drugs Genes Genetic Genetic Polymorphism Genomics Goals Hematologist Heterogeneity Human Individual K-562 K562 Cells Laboratories Malignant Neoplasms Messenger RNA Methodology Methods Methylation Modeling Myeloid Leukemia Noise Nucleotides Pathway interactions Patients Pattern Play Population Quality Control Research Personnel Resolution Role Sample Size Sampling Services Source Stem cells Technology Testing Tissues Treatment Efficacy Universities Validation Variant base cancer cell carbene data integration design exome exome sequencing experimental study genome-wide genomic variation improved insertion/deletion mutation insight leukemia methylome neoplastic cell new technology novel restriction enzyme single cell analysis single cell sequencing success therapeutic target tool transcriptome transcriptome sequencing tumor tumor heterogeneity user-friendly

项目摘要

Project Summary Human cancers are highly heterogeneous. However, due to the limit of technologies, the intercellular heterogeneity was not detectable genome-wide at single-cell level until recently. New technologies such as single-cell RNA-Seq and exome-Seq have revealed new insights and more profound complexity than what was previously thought. In collaboration with Dr. Weissman's group in the Genetics Department at Yale University, it is now feasible to perform multiple integrative assays from the same single tumor cell. However, rigorous computational methods are still lagging behind, in order to solve the computational challenge and determine true heterogeneity rather than noise. Here we propose a user-friendly bioinformatics platform that is optimized to integrative multiple types of single-cell NGS data, in particular, transcriptome, exome and CpG methylome data that are all obtained from the same cell. Specifically, in this study we will first construct and validate in parallel three new NGS bioinformatics pipelines to enable single-cell based RNA-Seq, exome- Seq, and CpG methylome data. We will then develop and validate an integration pipeline to analyze multiple types of high- throughput data, exemplified by the RNA-Seq, exome-Seq and CpG methylome single-cell data. To test the software suite, we will first obtain data sets from erythroleukemia cell line K562. We will then utilize this bioinformatics suite to investigate heterogeneity in Myeloid Leukemia patient samples provided by hematologist Dr. Stephanie Halene at Yale Stem Cell Center. Beyond deciphering tumor heterogeneity, this user-friendly bioinformatics platform is expected to be used widely by the single-cell sequencing community.

项目摘要人类癌症是高度异质的。但是，由于技术的极限，细胞间直到最近，异质性在单细胞水平上才可检测到全基因组。新技术，例如单细胞RNA-seq和Exome-Seq揭示了与当时的新见解和更深刻的复杂性以前想到。与耶鲁大学遗传学系Weissman博士小组合作，现在可以从同一单个肿瘤细胞中执行多个集成测定。但是，严格计算方法仍在落后，以解决计算挑战并确定真正的异质性而不是噪音。在这里，我们提出了一个优化的用户友好的生物信息学平台对于多种类型的单细胞NGS数据，特别是转录组，外显组和CPG甲基全部从同一单元格获得的数据。具体而言，在这项研究中，我们将首先构建并验证并行三个新的NGS生物信息学管道，以启用基于单细胞的RNA-Seq，Exome-Seq和CPG 甲基团数据。然后，我们将开发和验证一条集成管道，以分析多种类型的高级用RNA-Seq，Exome-Seq和CpG甲基甲基单细胞数据示例的吞吐量数据。测试软件套件，我们将首先从Erythrolyukemia细胞系K562获得数据集。然后我们将使用此生物信息学套件研究髓样白血病患者样本中的异质性耶鲁大学干细胞中心的血液学家斯蒂芬妮·哈利（Stephanie Halene）博士。除了破译肿瘤异质性之外，预计用户友好的生物信息学平台将被单细胞测序社区广泛使用。