癌症基因表达数据库(二)
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">这一期<span style="color: black;">博主</span>和<span style="color: black;">大众</span><span style="color: black;">一块</span>学习一下癌症芯片表达数据<span style="color: black;">关联</span>的数据库。尽管<span style="color: black;">日前</span>转录组深度测序技术被广泛应用,基因芯片技术依靠其低成本和癌症<span style="color: black;">科研</span>的大样本量需求使得芯片数据<span style="color: black;">日前</span>仍在<span style="color: black;">持续</span>的产生。与RNA-seq数据产生的方式有所不同,芯片数据产生的平台多样,平台之间的数据<span style="color: black;">常常</span><span style="color: black;">不可</span>直接比较。这<span style="color: black;">针对</span>芯片数据跨平台的比较和整合分析是<span style="color: black;">有害</span>的。幸运的是,<span style="color: black;">有些</span>很好的芯片数据库和分析平台被<span style="color: black;">研发</span>。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">GEO (https://www.ncbi.nlm.nih.gov/geo/)与ArrayExpress(http://www.ebi.ac.uk/arrayexpress/)存储了大部分已公开的基因芯片表达原始数据和归一化数据,<span style="color: black;">重点</span>是<span style="color: black;">源自</span>于全世界<span style="color: black;">专家</span>的<span style="color: black;">研究</span>工作。当然,这<span style="color: black;">亦</span><span style="color: black;">包含</span><span style="color: black;">海量</span>的癌症<span style="color: black;">科研</span>工作。<span style="color: black;">针对</span>GEO,用户<span style="color: black;">能够</span>利用<span style="color: black;">重要</span>词进行搜索,如Humancancer。图1.1是搜索结果页面。用户<span style="color: black;">能够</span><span style="color: black;">经过</span>左边栏选项对<span style="color: black;">查找</span>结果进行过滤。图1.2是一个数据集的下载页面。<span style="color: black;">针对</span>ArrayExpres,用户<span style="color: black;">亦</span><span style="color: black;">能够</span>利用<span style="color: black;">重要</span>词进行搜索,如Cancer,图1.3是结果页面。<span style="color: black;">一样</span>用户<span style="color: black;">亦</span><span style="color: black;">能够</span>进行数据过滤和数据下载(图1.4)【1-2】。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q3.itc.cn/images01/20240410/cd434b97eb804b3b8a24cc446891e5e2.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.1 GEO <span style="color: black;">查找</span>结果页面</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q4.itc.cn/images01/20240410/b9909176f2074d298804885510579865.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.2 GEO 数据集下载页面</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q8.itc.cn/images01/20240410/f884712e039e4bcfa65ee4122eecec21.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.3 ArrayExpress<span style="color: black;">查找</span>结果页面</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q3.itc.cn/images01/20240410/39feb42f60074382b61053ee31ed6e7f.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.4 ArrayExpress数据集下载页面</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">GENT(http://medicalgenome.kribb.re.kr/GENT/)收集了41000个癌症组织、癌症细胞系、和正常组织的芯片表达数据。并通图形化展示了在组织、细胞系中的表达模式。用户<span style="color: black;">能够</span>基于表达模式,找到<span style="color: black;">有些</span>表达<span style="color: black;">反常</span>的基因。用户<span style="color: black;">能够</span><span style="color: black;">按照</span>基因名<span style="color: black;">或</span>Affymetrix ID搜索。图1.5展示了ERBB2在癌症和正常组织中表达<span style="color: black;">状况</span>。<span style="color: black;">另外</span>,用户还<span style="color: black;">能够</span><span style="color: black;">同期</span>搜索多个基因在多个组织中的表达模式<span style="color: black;">状况</span>【3】。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q3.itc.cn/images01/20240410/79a221511521497ebdc577dec643be10.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.5 ERBB2基因在癌症和正常组织中的表达</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">Oncomine(https://www.oncomine.org/resource/login.html)是一个癌症芯片数据库和整合型的数据挖掘平台。该数据库收集了715套芯片表达数据集,共<span style="color: black;">包括</span>了86733个样本以及<span style="color: black;">关联</span>的临床数据。用户既<span style="color: black;">能够</span>得到一个基因的差异表达分析,<span style="color: black;">亦</span><span style="color: black;">能够</span>得到某一个<span style="color: black;">科研</span>工作中所有差异表达的基因。差异表达分析<span style="color: black;">能够</span>在癌症与正常组织之间、不同癌症亚型组织之间、基于临床和病理数据<span style="color: black;">归类</span>的样本之间进行。<span style="color: black;">另外</span>,用户还<span style="color: black;">能够</span>进行多基因搜索、依据Gene ontology和临床的注释信息进行过滤。图1.6是Oncomine主界面,图1.7是Oncomine数据呈现界面【4】。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q8.itc.cn/images01/20240410/e8b09e9d5184492695a72f5308e4e7a5.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.6 Oncomine主界面</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q2.itc.cn/images01/20240410/da7af018bd094cc0b42ecbb97e93df6b.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.7 Oncomine数据呈现页面</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">CancerMA(http://www.cancerma.org.uk/)一个芯片数据meta-analysis分析平台。其芯片数据<span style="color: black;">源自</span>于GEO与ArrayExpress。<span style="color: black;">日前</span>,<span style="color: black;">包括</span>了来自13种癌症的80套数据集。用户上传关心的基因名列表之后,该数据库会基于基因表达信息进行meta-analysis来找到新的候选癌症生物标志物。图1.8是数据最后的提交页面,用户需<span style="color: black;">供给</span>邮箱<span style="color: black;">位置</span>。图1.9是CAV1基因在肺癌中meta-analysis分析部分结果【5】。</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q9.itc.cn/images01/20240410/05eb7ffce685413589f11d05cb9284c2.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.8 CancerMA数据提交页面</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="//q1.itc.cn/images01/20240410/db3fb42adc5745ad9f5926251e70dab1.jpeg" style="width: 50%; margin-bottom: 20px;"></p>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">图1.9 CAV1基因在肺癌中分析结果</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">参考文献</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">1. BarrettT, Edgar R: Gene expression omnibus: microarray data storage, submission,retrieval, and analysis. Methods in enzymology 2006, 411:352-369.</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">2. ParkinsonH: ArrayExpress--a public repository for microarray gene expression data at theEBI. Nucleic acids research 2004, 33(Database issue):D553-D55</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">3. Seon-YoungK, Gwangsik S, Kang, Sungjin Y, Su-Jin B, Yong-Su J: GENT: Gene ExpressionDatabase of Normal and Tumor Tissues. Cancer Informatics 2011:149.</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">4. RhodesDR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A,Chinnaiyan AM: ONCOMINE: a cancer microarray database and integrateddata-mining platform. Neoplasia 2004, 6(1):1-6.</p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;">5. FeichtingerJ, McFarlane RJ, Larcombe LD: CancerMA: a web-based tool for automaticmeta-analysis of public cancer microarray data. Database : the journal ofbiological databases and curation 2012, 2012:bas055.<a style="color: black;"><span style="color: black;">返回<span style="color: black;">外链论坛:http://www.fok120.com/</span>,查看<span style="color: black;">更加多</span></span></a></p>
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