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AgeAnno: a knowledgebase of single-cell annotation of aging in human
Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging...
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Published in: | Nucleic acids research 2023-01, Vol.51 (D1), p.D805-D815 |
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creator | Huang, Kexin Gong, Hoaran Guan, Jingjing Zhang, Lingxiao Hu, Changbao Zhao, Weiling Huang, Liyu Zhang, Wei Kim, Pora Zhou, Xiaobo |
description | Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy tissue samples with ages ranging from 0 to 110 years. We collected 5580 aging-related genes from previous resources and performed dynamic functional annotations of the cellular context. For the scRNA data, we performed analyses include differential gene expression, gene variation coefficient, cell communication network, transcription factor (TF) regulatory network, and immune cell proportionc. AgeAnno also provides differential chromatin accessibility analysis, motif/TF enrichment and footprint analysis, and co-accessibility peak analysis for scATAC data. AgeAnno will be a unique resource to systematically characterize aging-related genes across diverse tissue-cell types in human, and it could facilitate antiaging and aging-related disease research. |
doi_str_mv | 10.1093/nar/gkac847 |
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The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy tissue samples with ages ranging from 0 to 110 years. We collected 5580 aging-related genes from previous resources and performed dynamic functional annotations of the cellular context. For the scRNA data, we performed analyses include differential gene expression, gene variation coefficient, cell communication network, transcription factor (TF) regulatory network, and immune cell proportionc. AgeAnno also provides differential chromatin accessibility analysis, motif/TF enrichment and footprint analysis, and co-accessibility peak analysis for scATAC data. AgeAnno will be a unique resource to systematically characterize aging-related genes across diverse tissue-cell types in human, and it could facilitate antiaging and aging-related disease research.</description><identifier>ISSN: 0305-1048</identifier><identifier>ISSN: 1362-4962</identifier><identifier>EISSN: 1362-4962</identifier><identifier>DOI: 10.1093/nar/gkac847</identifier><identifier>PMID: 36200838</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Adolescent ; Adult ; Aged ; Aged, 80 and over ; Aging - genetics ; Aging - pathology ; Child ; Child, Preschool ; Chromatin - genetics ; Database Issue ; Humans ; Infant ; Infant, Newborn ; Knowledge Bases ; Middle Aged ; Single-Cell Analysis ; Young Adult</subject><ispartof>Nucleic acids research, 2023-01, Vol.51 (D1), p.D805-D815</ispartof><rights>The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.</rights><rights>The Author(s) 2022. 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The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy tissue samples with ages ranging from 0 to 110 years. We collected 5580 aging-related genes from previous resources and performed dynamic functional annotations of the cellular context. For the scRNA data, we performed analyses include differential gene expression, gene variation coefficient, cell communication network, transcription factor (TF) regulatory network, and immune cell proportionc. AgeAnno also provides differential chromatin accessibility analysis, motif/TF enrichment and footprint analysis, and co-accessibility peak analysis for scATAC data. AgeAnno will be a unique resource to systematically characterize aging-related genes across diverse tissue-cell types in human, and it could facilitate antiaging and aging-related disease research.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aging - genetics</subject><subject>Aging - pathology</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Chromatin - genetics</subject><subject>Database Issue</subject><subject>Humans</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Knowledge Bases</subject><subject>Middle Aged</subject><subject>Single-Cell Analysis</subject><subject>Young Adult</subject><issn>0305-1048</issn><issn>1362-4962</issn><issn>1362-4962</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpVkE1Lw0AQhhdRbK2evEuOgsTuRz42HoRSP6HgRc_LbDJJY5Pdmk0U_71bWoue5vA-PDPzEnLO6DWjmZga6KbVCnIZpQdkzETCwyhL-CEZU0HjkNFIjsiJc--UsojF0TEZeYZSKeSY3M0qnBljbwIIVsZ-NVhUqMFhYMvA1aZqMMyxaQLwUA99bc0mgcpHQW2C5dCCOSVHJTQOz3ZzQt4e7l_nT-Hi5fF5PluEuZCsD6VIUhBScA2c55CKWEstWMQFahlDoUteMEAGOqY0Q_8bxzyKCilo4jMQE3K79a4H3WKRo-k7aNS6q1vovpWFWv1PTL1Ulf1UmeSxd3rB5U7Q2Y8BXa_a2m3eA4N2cIqnnAvGYi49erVF884612G5X8Oo2vSufO9q17unL_5etmd_ixY_CNh__Q</recordid><startdate>20230106</startdate><enddate>20230106</enddate><creator>Huang, Kexin</creator><creator>Gong, Hoaran</creator><creator>Guan, Jingjing</creator><creator>Zhang, Lingxiao</creator><creator>Hu, Changbao</creator><creator>Zhao, Weiling</creator><creator>Huang, Liyu</creator><creator>Zhang, Wei</creator><creator>Kim, Pora</creator><creator>Zhou, Xiaobo</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-7191-6495</orcidid><orcidid>https://orcid.org/0000-0001-6534-2712</orcidid><orcidid>https://orcid.org/0000-0002-8321-6864</orcidid><orcidid>https://orcid.org/0000-0002-5661-4090</orcidid></search><sort><creationdate>20230106</creationdate><title>AgeAnno: a knowledgebase of single-cell annotation of aging in human</title><author>Huang, Kexin ; Gong, Hoaran ; Guan, Jingjing ; Zhang, Lingxiao ; Hu, Changbao ; Zhao, Weiling ; Huang, Liyu ; Zhang, Wei ; Kim, Pora ; Zhou, Xiaobo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-8367a3832ba22ca735b8b31423eb85adbf2d1ae1ab5009e0932ec44d8306bf2a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Aging - genetics</topic><topic>Aging - pathology</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Chromatin - genetics</topic><topic>Database Issue</topic><topic>Humans</topic><topic>Infant</topic><topic>Infant, Newborn</topic><topic>Knowledge Bases</topic><topic>Middle Aged</topic><topic>Single-Cell Analysis</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Kexin</creatorcontrib><creatorcontrib>Gong, Hoaran</creatorcontrib><creatorcontrib>Guan, Jingjing</creatorcontrib><creatorcontrib>Zhang, Lingxiao</creatorcontrib><creatorcontrib>Hu, Changbao</creatorcontrib><creatorcontrib>Zhao, Weiling</creatorcontrib><creatorcontrib>Huang, Liyu</creatorcontrib><creatorcontrib>Zhang, Wei</creatorcontrib><creatorcontrib>Kim, Pora</creatorcontrib><creatorcontrib>Zhou, Xiaobo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nucleic acids research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Kexin</au><au>Gong, Hoaran</au><au>Guan, Jingjing</au><au>Zhang, Lingxiao</au><au>Hu, Changbao</au><au>Zhao, Weiling</au><au>Huang, Liyu</au><au>Zhang, Wei</au><au>Kim, Pora</au><au>Zhou, Xiaobo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AgeAnno: a knowledgebase of single-cell annotation of aging in human</atitle><jtitle>Nucleic acids research</jtitle><addtitle>Nucleic Acids Res</addtitle><date>2023-01-06</date><risdate>2023</risdate><volume>51</volume><issue>D1</issue><spage>D805</spage><epage>D815</epage><pages>D805-D815</pages><issn>0305-1048</issn><issn>1362-4962</issn><eissn>1362-4962</eissn><abstract>Aging is a complex process that accompanied by molecular and cellular alterations. 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subjects | Adolescent Adult Aged Aged, 80 and over Aging - genetics Aging - pathology Child Child, Preschool Chromatin - genetics Database Issue Humans Infant Infant, Newborn Knowledge Bases Middle Aged Single-Cell Analysis Young Adult |
title | AgeAnno: a knowledgebase of single-cell annotation of aging in human |
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