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AD 知识门户:阿尔茨海默病和衰老的多组学数据存储库。

The AD Knowledge Portal: A Repository for Multi-Omic Data on Alzheimer's Disease and Aging.

机构信息

Sage Bionetworks, Seattle, Washington.

出版信息

Curr Protoc Hum Genet. 2020 Dec;108(1):e105. doi: 10.1002/cphg.105.

DOI:10.1002/cphg.105
PMID:33085189
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7587039/
Abstract

The AD Knowledge Portal (adknowledgeportal.org) is a public data repository that shares data and other resources generated by multiple collaborative research programs focused on aging, dementia, and Alzheimer's disease (AD). In this article, we highlight how to use the Portal to discover and download genomic variant and transcriptomic data from the same individuals. First, we show how to use the web interface to browse and search for data of interest using relevant file annotations. We demonstrate how to learn more about the context surrounding the data, including diagnostic criteria and methodological details about sample preparation and data analysis. We present two primary ways to download data-using a web interface, and using a programmatic method that provides access using the command line. Finally, we show how to merge separate sources of metadata into a comprehensive file that contains factors and covariates necessary in downstream analyses. © 2020 The Authors. Basic Protocol 1: Find and download files associated with a selected study Basic Protocol 2: Download files in bulk using the command line client Basic Protocol 3: Working with file annotations and metadata.

摘要

AD 知识门户(adknowledgeportal.org)是一个公共数据存储库,它共享了多个专注于衰老、痴呆和阿尔茨海默病(AD)的合作研究计划生成的数据和其他资源。在本文中,我们将重点介绍如何使用门户来发现和下载来自同一组个体的基因组变异和转录组数据。首先,我们展示了如何使用 Web 界面使用相关文件注释浏览和搜索感兴趣的数据。我们演示了如何了解有关数据上下文的更多信息,包括诊断标准以及有关样本制备和数据分析的方法细节。我们介绍了两种主要的数据下载方法——使用 Web 界面和使用提供命令行访问的编程方法。最后,我们展示了如何将单独的元数据源合并到一个包含下游分析所需的因素和协变量的综合文件中。© 2020 作者。基础方案 1:查找并下载与选定研究相关的文件基础方案 2:使用命令行客户端批量下载文件基础方案 3:使用文件注释和元数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5f3/7685097/90154ec73bd9/CPHG-108-e105-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5f3/7685097/90154ec73bd9/CPHG-108-e105-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5f3/7685097/2d504e375ec4/CPHG-108-e105-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5f3/7685097/804131c5b82a/CPHG-108-e105-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5f3/7685097/2aff3a90e88a/CPHG-108-e105-g006.jpg
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Expert Opin Ther Targets. 2016;20(4):389-91. doi: 10.1517/14728222.2016.1135132. Epub 2016 Feb 7.
3
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NPJ Aging. 2025 Jul 16;11(1):66. doi: 10.1038/s41514-025-00258-5.
4
Proteomizer: Leveraging the Transcriptome-Proteome Mismatch to Infer Novel Gene Regulatory Relations.蛋白质组生成器:利用转录组与蛋白质组的不匹配来推断新型基因调控关系。
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5
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6
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9
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