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ARCHS4: Massive Mining of Publicly Available RNA Sequencing Data

ARCHS4: Massive Mining of Publicly Available RNA Sequencing Data
ARCHS4:大规模挖掘公开的 RNA 测序数据
批准号:
10527721
负责人:
Avi Ma'ayan
金额:
$79.09万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-01 至 2027-08-31

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中文摘要
翻译
总结 许多采用批量和单细胞RNA-seq的癌症相关独立研究仍然处于重复使用状态, 它们的可查找性、可访问性、互操作性和可重用性较低。这些研究的数据可以在 基因表达综合数据库(GEO),但它主要作为具有非统一元数据的原始FASTQ文件提供 注释。虽然一些研究提供了对齐的读取文件,但这些文件的处理不均匀。这 这一缺陷使得难以查询和整合跨研究的数据以及额外的外部数据。 为了弥合目前存在于RNA-seq数据生成和RNA-seq数据处理之间的差距, 为了重复使用,我们开发了资源All RNA-seq和ChIP-Seq Sample and Signature Search(ARCHS 4)。 ARCHS 4提供来自GEO的经过处理的RNA-seq数据,以支持回顾性数据分析和重复使用。 ARCHS 4迎合了具有不同计算专业知识水平的用户,并已被用于 许多事后分析和项目。这些目标远远超出了仅仅为癌症研究人员提供直接的 通过基于网络的用户界面访问RNA-seq数据。我们计划将其他转录组学数据 利用深度学习技术构建类似RNA-seq的图谱,识别人类RNA-seq样本中的致病序列, 从RNA-seq读数中鉴定短变体,从共表达数据中预测基因功能,包括 用小分子调节长的非编码RNA的表达,最重要的是, ARCHS 4高性价比的基础设施,继续为社会提供免费的FASTQ对齐服务。
英文摘要
SUMMARY Many cancer-related independent studies that employ bulk and single cell RNA-seq remain under reused due to their lower findability, accessibility, interoperability, and reusability. The data from these studies can be found in the Gene Expression Omnibus (GEO) but it is provided mostly as raw FASTQ files with non-uniform metadata annotations. While some studies provide aligned reads files, these are processed non-uniformly. This shortcoming makes it difficult to query and integrate this data across studies and with additional external data. To bridge the gap that currently exists between RNA-seq data generation and RNA-seq data processing and reuse, we developed the resource All RNA-seq and ChIP-Seq Sample and Signature Search (ARCHS4). ARCHS4 provides processed RNA-seq data from GEO to support retrospective data analyses and reuse. ARCHS4 caters to users with different levels of computational expertise and has been already employed for many post-hoc analyses and projects. The goals go far beyond just providing cancer researchers with direct access to RNA-seq data through a web-based user interface. We plan to transform other transcriptomics data into RNA-seq-like profiles with Deep Learning, identify pathogenic sequences in human RNA-seq samples, identify short variants from RNA-seq reads, predict gene function from co-expression data including ways to modulate the expression of long non-coding RNAs with small molecules, and most importantly, using the ARCHS4 cost-effective infrastructure, continue to provide a free FASTQ alignment service to the community.
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