GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery.
GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery.
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DOI：
10.1093/nar/gkad399
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发表时间：
2023-07-05
影响因子：
14.9
通讯作者：
中科院分区：
文献类型：
--
作者：
一些图谱工作旨在分析正常生理学、发育和疾病中组织、细胞类型和细胞系中的人类基因和蛋白质表达。这些资源的一个效用是检查每个图谱中所有细胞类型、组织和细胞系中单个基因的表达。然而，目前还没有一个集中的地方来整合来自几个地图集的数据，以统一的格式提供这类数据，供可视化、分析和下载，并通过应用程序编程接口。为了满足这一需求，GeneRanger是一个网络服务器，它提供了对来自几个图谱的正常人类细胞类型、组织和细胞系的基因和蛋白质表达的处理数据的访问。与此同时，TargetRanger是一个相关的网络服务器，它从分析的人类细胞和组织中获取RNA-seq数据作为输入，然后将上传的输入数据与图谱中的表达水平进行比较，以识别在输入中高表达而在正常人类细胞类型和组织中低表达的基因。可通过跨膜或分泌蛋白过滤鉴定的靶标。GeneRanger和TargetRanger的结果以箱线图和散点图以及交互式表格的形式可视化。GeneRanger和TargetRanger可分别从https://generanger.maayanlab.cloud和https://targetranger.maayanlab.cloud获得。
Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.
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影响因子：
64.8
作者：
Kim, Min-Sik;Pinto, Sneha M.;Getnet, Derese;Nirujogi, Raja Sekhar;Manda, Srikanth S.;Chaerkady, Raghothama;Madugundu, Anil K.;Kelkar, Dhanashree S.;Isserlin, Ruth;Jain, Shobhit;Thomas, Joji K.;Muthusamy, Babylakshmi;Leal-Rojas, Pamela;Kumar, Praveen;Sahasrabuddhe, Nandini A.;Balakrishnan, Lavanya;Advani, Jayshree;George, Bijesh;Renuse, Santosh;Selvan, Lakshmi Dhevi N.;Patil, Arun H.;Nanjappa, Vishalakshi;Radhakrishnan, Aneesha;Prasad, Samarjeet;Subbannayya, Tejaswini;Raju, Rajesh;Kumar, Manish;Sreenivasamurthy, Sreelakshmi K.;Marimuthu, Arivusudar;Sathe, Gajanan J.;Chavan, Sandip;Datta, Keshava K.;Subbannayya, Yashwanth;Sahu, Apeksha;Yelamanchi, Soujanya D.;Jayaram, Savita;Rajagopalan, Pavithra;Sharma, Jyoti;Murthy, Krishna R.;Syed, Nazia;Goel, Renu;Khan, Aafaque A.;Ahmad, Sartaj;Dey, Gourav;Mudgal, Keshav;Chatterjee, Aditi;Huang, Tai-Chung;Zhong, Jun;Wu, Xinyan;Shaw, Patrick G.;Freed, Donald;Zahari, Muhammad S.;Mukherjee, Kanchan K.;Shankar, Subramanian;Mahadevan, Anita;Lam, Henry;Mitchell, Christopher J.;Shankar, Susarla Krishna;Satishchandra, Parthasarathy;Schroeder, John T.;Sirdeshmukh, Ravi;Maitra, Anirban;Leach, Steven D.;Drake, Charles G.;Halushka, Marc K.;Prasad, T. S. Keshava;Hruban, Ralph H.;Kerr, Candace L.;Bader, Gary D.;Iacobuzio-Donahue, Christine A.;Gowda, Harsha;Pandey, Akhilesh
通讯作者：
Pandey, Akhilesh
实现癌症基因组数据的共同愿景。
DOI：
10.1056/nejmp1607591
发表时间：
2016-09-22
期刊：
The New England journal of medicine
影响因子：
--
作者：
Grossman RL;Heath AP;Ferretti V;Varmus HE;Lowy DR;Kibbe WA;Staudt LM
通讯作者：
Staudt LM
影响因子：
64.5
作者：
Jiang L;Wang M;Lin S;Jian R;Li X;Chan J;Dong G;Fang H;Robinson AE;GTEx Consortium;Snyder MP
通讯作者：
Snyder MP
影响因子：
64.8
作者：
Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
14.9
作者：
Gremse M;Chang A;Schomburg I;Grote A;Scheer M;Ebeling C;Schomburg D
通讯作者：
Schomburg D