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.

项目概要人类癌症具有高度异质性。但由于技术的限制，细胞间质直到最近，异质性还无法在单细胞水平上在全基因组范围内检测到。新技术，例如单细胞 RNA 测序和外显子组测序揭示了新的见解和比以前更深刻的复杂性之前以为。与耶鲁大学遗传学系 Weissman 博士的团队合作，现在可以对同一个肿瘤细胞进行多种综合分析。然而，严格计算方法仍然落后，为了解决计算挑战并确定真正的异质性而不是噪音。在这里，我们提出了一个经过优化的用户友好的生物信息学平台整合多种类型的单细胞NGS数据，特别是转录组、外显子组和CpG甲基化组数据均来自同一单元格。具体来说，在本研究中，我们将首先构建并验证并行三个新的 NGS 生物信息学流程，以实现基于单细胞的 RNA-Seq、外显子组 Seq 和 CpG 甲基化组数据。然后，我们将开发并验证一个集成管道来分析多种类型的高吞吐量数据，例如 RNA-Seq、外显子组 Seq 和 CpG 甲基化单细胞数据。测试软件套件中，我们首先从红白血病细胞系 K562 获取数据集。然后我们将利用这个生物信息学套件用于研究髓系白血病患者样本的异质性耶鲁干细胞中心的血液学家 Stephanie Halene 博士。除了破译肿瘤异质性之外，这用户友好的生物信息学平台预计将被单细胞测序界广泛使用。