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.

概括许多采用大量和单细胞RNA-seq的与癌症相关的独立研究由于它们较低的可发现性，可访问性，互操作性和可重复性。这些研究的数据可以在基因表达综合（GEO），但主要作为具有不均匀元数据的RAW FASTQ文件提供注释。虽然一些研究提供了对齐的读取文件，但它们是不均匀处理的。这缺点使得难以查询和集成在研究中以及其他外部数据的数据。弥合RNA-seq数据生成和RNA-seq数据处理和重用，我们开发了所有RNA-Seq和Chip-Seq样本和签名搜索（Archs4）的资源。 ArchS4提供了从GEO中处理的RNA-SEQ数据，以支持回顾性数据分析和重复使用。 ArchS4迎合具有不同计算专业知识的用户，并且已经被用过许多事后分析和项目。目标远远超出了为癌症研究人员提供直接的通过基于Web的用户界面访问RNA-Seq数据。我们计划改变其他转录组学数据进入具有深度学习的RNA-Seq样轮廓，鉴定人RNA-Seq样品中的致病序列，从RNA-seq读取中确定短变体，从共表达数据中预测基因函数，包括通往的方法使用小分子调节长的非编码RNA的表达，最重要的是，使用 ArchS4具有成本效益的基础设施，继续为社区提供免费的FASTQ对齐服务。