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Analysis of genomics datasets at a massive scale

大规模基因组学数据集分析

基本信息

批准号：
10223343
负责人：
Kasper Daniel Hansen
金额：
$ 46.17万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10223343
关键词：
Address Alleles Bioconductor Biology Catalogs Cloud Computing Communities Computer software Computing Methodologies Data Disease Event Excision Exons Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genetic Transcription Genetic Variation Genome Genotype-Tissue Expression Project Goals Human Human Biology Impact evaluation Lead Licensing Light Linear Models Maps Measures Metadata Methods Modeling Morphologic artifacts Nucleotides Pathogenicity Pattern Procedures Process Quality Control RNA RNA Splicing Regulation Research Resolution Resources Sample Size Sampling Sequence Read Archive Spliced Genes Statistical Methods Systematic Bias The Cancer Genome Atlas Tissues Transcript Update Variant Visualization Work base cell type data resource genome-wide genomic data human RNA sequencing human disease insight open source reconstruction statistics transcriptome transcriptome sequencing

项目摘要

Understanding both normal and pathogenic patterns of human gene expression can help shed light on the biology of human disease. Thousands of studies have now been undertaken measuring gene expression in different tissues and diseases. By aggregating and analyzing all available human RNA-sequencing data using a high powered computational and statistical framework, we will provide a transformative resource for characterizing human gene expression patterns including rare transcriptional events, cellular networks, and genetic variation. In Aim 1 we propose to uniformly process all publicly available human transcriptome sequencing data and collect it into a publicly available resource called the Transcriptome Aggregation Resource (TAR); at least 150,000 samples will be processed using cloud computing. This resource will contain single-base resolution maps of expression, de novo mapped exon-exon splice junctions and allele speciﬁc expression across a set of common variations. We will supplement the expression data with cleaned and predicted metadata. In Aim 2 we will develop statistical and computational methods necessary to fully realize the potential of this resource. Speciﬁcally we will remove unwanted variation at scale and develop mixture models to summarize the large data resource at the gene, junction and single base levels. In Aim 3 we will analyze this resource to address fundamental questions in expression biology, include a systematic study of expression outliers and allele speciﬁc expression at the gene, junction and single base resolution. We will infer well-powered co-expression networks over both expressed genes and splicing patterns. This work will contribute signiﬁcantly to our understanding of gene expression by analyzing genomics data at a massive scale.

了解人类基因表达的正常和致病模式有助于了解生物学人类疾病的威胁。现在已经进行了数千项研究，以测量不同物种的基因表达组织和疾病。通过使用HIGH来聚集和分析所有可用的人类RNA测序数据强大的计算和统计框架，我们将提供一个变革性的资源来描述人类基因表达模式包括罕见的转录事件、细胞网络和遗传变异。在目标1中，我们建议统一处理所有公开可用的人类转录组测序数据并收集它们到名为Transcriptome Aggregation Resource(TAR)的公开可用资源中；至少150,000个样本将使用云计算进行处理。该资源将包含表达的单基分辨率图，从新定位外显子-外显子剪接连接和等位基因ﬁc在一组常见变异中的表达。我们将使用经过清理和预测的元数据来补充表达式数据。在目标2中，我们将发展统计学以及充分实现这一资源潜力所需的计算方法。我们将删除特定的ﬁ 并开发混合模型来总结基因中的大量数据资源，交界处标高和单基准标高。在目标3中，我们将分析此资源以解决以下基本问题表达生物学，包括对基因上表达异常值和等位基因ﬁc表达的系统研究，结点和单碱基分辨率。我们将在两个表达的基础上推断功能强大的共表达网络基因和拼接模式。这项工作将通过对基因组数据的分析，为我们理解基因表达做出重大贡献。ﬁ 巨大的规模。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Missense variants causing Wiedemann-Steiner syndrome preferentially occur in the KMT2A-CXXC domain and are accurately classified using AlphaFold2.

DOI：
10.1371/journal.pgen.1010278
发表时间：
2022-06
期刊：
PLoS genetics
影响因子：
4.5
作者：
通讯作者：

recountmethylation enables flexible analysis of public blood DNA methylation array data.

DOI：
10.1093/bioadv/vbad020
发表时间：
2023
期刊：
Bioinformatics advances
影响因子：
0
作者：
通讯作者：

Leveraging the Mendelian disorders of the epigenetic machinery to systematically map functional epigenetic variation.

DOI：
10.7554/elife.65884
发表时间：
2021-08-31
期刊：
eLife
影响因子：
7.7
作者：
Luperchio TR;Boukas L;Zhang L;Pilarowski G;Jiang J;Kalinousky A;Hansen KD;Bjornsson HT
通讯作者：
Bjornsson HT