Expression Atlas update: insights from sequencing data at both bulk and single cell level.
Expression Atlas update: insights from sequencing data at both bulk and single cell level.
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Expression Atlas更新：从批量和单细胞水平的测序数据中获得的见解
DOI：
10.1093/nar/gkad1021
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发表时间：
2024-01-05
影响因子：
14.9
通讯作者：
Papatheodorou, Irene
中科院分区：
文献类型：
--
作者：
George, Nancy;Fexova, Silvie;Fuentes, Alfonso Munoz;Madrigal, Pedro;Bi, Yalan;Iqbal, Haider;Kumbham, Upendra;Nolte, Nadja Francesca;Zhao, Lingyun;Thanki, Anil S.;Yu, Iris D.;Marugan Calles, Jose C.;Erdos, Karoly;Vilmovsky, Liora;Kurri, Sandeep R.;Vathrakokoili-Pournara, Anna;Osumi-Sutherland, David;Prakash, Ananth;Wang, Shengbo;Tello-Ruiz, Marcela K.;Kumari, Sunita;Ware, Doreen;Goutte-Gattat, Damien;Hu, Yanhui;Brown, Nick;Perrimon, Norbert;Vizcaino, Juan Antonio;Burdett, Tony;Teichmann, Sarah;Brazma, Alvis;Papatheodorou, Irene
Expression Atlas（www.ebi.ac.uk/gxa）及其最新版本的Single Cell Expression Atlas（www.ebi.ac.uk/gxa/sc）是EMBL-EBI在批量和单细胞水平上进行基因和蛋白质表达和定位的知识库。这些资源旨在允许用户研究其在正常组织中的表达（基线）或对疾病或多个物种的基因型变化（差异）等扰动的反应。用户被邀请在标准化的一致界面中搜索跨物种或生物条件的基因或元数据术语。除了这些数据，单细胞表达图谱的新功能允许用户通过我们的新细胞类型轮搜索查询元数据。在实验层面，可以通过两种类型的降维图来探索数据，t分布随机邻居嵌入（tSNE）和均匀流形近似和投影（UMAP），覆盖聚类或元数据信息以帮助用户理解。数据也被可视化为标记基因热图，识别有助于赋予聚类身份的基因。对于某些数据，额外的可视化可作为交互式细胞水平解剖图和细胞类型基因表达热图。
Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI’s knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users’ understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.
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影响因子：
14.9
作者：
Harrison PW;Ahamed A;Aslam R;Alako BTF;Burgin J;Buso N;Courtot M;Fan J;Gupta D;Haseeb M;Holt S;Ibrahim T;Ivanov E;Jayathilaka S;Balavenkataraman Kadhirvelu V;Kumar M;Lopez R;Kay S;Leinonen R;Liu X;O'Cathail C;Pakseresht A;Park Y;Pesant S;Rahman N;Rajan J;Sokolov A;Vijayaraja S;Waheed Z;Zyoud A;Burdett T;Cochrane G
通讯作者：
Cochrane G
影响因子：
3.3
作者：
Gramates LS;Agapite J;Attrill H;Calvi BR;Crosby MA;Dos Santos G;Goodman JL;Goutte-Gattat D;Jenkins VK;Kaufman T;Larkin A;Matthews BB;Millburn G;Strelets VB;the FlyBase Consortium
通讯作者：
the FlyBase Consortium
影响因子：
14.9
作者：
Perez-Riverol Y;Bai J;Bandla C;García-Seisdedos D;Hewapathirana S;Kamatchinathan S;Kundu DJ;Prakash A;Frericks-Zipper A;Eisenacher M;Walzer M;Wang S;Brazma A;Vizcaíno JA
通讯作者：
Vizcaíno JA
影响因子：
14.9
作者：
Papatheodorou, Irene;Moreno, Pablo;Brazma, Alvis
通讯作者：
Brazma, Alvis
影响因子：
14.9
作者：
Tryka KA;Hao L;Sturcke A;Jin Y;Wang ZY;Ziyabari L;Lee M;Popova N;Sharopova N;Kimura M;Feolo M
通讯作者：
Feolo M