ARCHS4: Massive Mining of Publicly Available RNA Sequencing Data

ARCHS4：大规模挖掘公开的 RNA 测序数据

基本信息

批准号：
10527721
负责人：
Avi Ma'ayan
金额：
$ 79.09万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10527721
关键词：
Address Algorithmic Software Algorithms Atlases Benchmarking Biological Process Cancer cell line Case Study Cell Line Cells ChIP-seq Classification Communities Companions Complex Data Data Analyses Development Disease Educational workshop FAIR principles FarGo Felis catus Frequencies Gene Expression Gene Expression Process Gene Expression Profile Generations Genes Goals Human Infrastructure Laboratories Malignant Neoplasms Manuals Metadata Methods Mining Molecular Molecular Evolution Mutation Names Natural Language Processing Normal tissue morphology Online Systems Organism Pathogenicity Performance Pharmaceutical Preparations Process Publishing Pythons Quantitative Trait Loci Readability Research Personnel Resources Sampling Services Single Nucleotide Polymorphism Source Supervision Technology Tissues Training Transcript Tumor Subtype Tumor Tissue Untranslated RNA Update Variant Work anticancer research application programming interface base cell type cloud based computational pipelines computerized data processing cost cost effective crowdsourcing data format data mining data portal data repository data reuse data visualization deep learning disease phenotype experimental study file format gene function genome analysis graphical user interface human RNA sequencing improved insertion/deletion mutation interoperability neoplastic cell novel open data outreach pathogen repository single-cell RNA sequencing small molecule transcriptome sequencing transcriptomics tumor virtual

项目摘要

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.

概括许多采用批量和单细胞 RNA-seq 的癌症相关独立研究由于以下原因仍未被重复使用：它们的可发现性、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 (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。这缺点使得跨研究和其他外部数据查询和整合这些数据变得困难。弥合目前 RNA-seq 数据生成和 RNA-seq 数据处理之间存在的差距为了重复利用，我们开发了资源 All RNA-seq and ChIP-Seq Sample and Signature Search (ARCHS4)。 ARCHS4 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用。 ARCHS4 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。这些目标不仅仅是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。我们计划转换其他转录组数据通过深度学习转化为类似 RNA-seq 的图谱，识别人类 RNA-seq 样本中的致病序列，从 RNA-seq 读取中识别短变异，从共表达数据中预测基因功能，包括用小分子调节长非编码 RNA 的表达，最重要的是，使用 ARCHS4高性价比的基础设施，继续向社区提供免费的FASTQ对齐服务。