SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks.
SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks.
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SignaLink3:揭示组织特异性信号网络的多层资源。
DOI:
10.1093/nar/gkab909
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发表时间:
2022-01-07
影响因子:
14.9
通讯作者:
Korcsmáros T
中科院分区:
文献类型:
--
作者:
Csabai L;Fazekas D;Kadlecsik T;Szalay-Bekő M;Bohár B;Madgwick M;Módos D;Ölbei M;Gul L;Sudhakar P;Kubisch J;Oyeyemi OJ;Liska O;Ari E;Hotzi B;Billes VA;Molnár E;Földvári-Nagy L;Csályi K;Demeter A;Pápai N;Koltai M;Varga M;Lenti K;Farkas IJ;Türei D;Csermely P;Vellai T;Korcsmáros T
Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.
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影响因子:
14.9
作者:
Howe KL;Achuthan P;Allen J;Allen J;Alvarez-Jarreta J;Amode MR;Armean IM;Azov AG;Bennett R;Bhai J;Billis K;Boddu S;Charkhchi M;Cummins C;Da Rin Fioretto L;Davidson C;Dodiya K;El Houdaigui B;Fatima R;Gall A;Garcia Giron C;Grego T;Guijarro-Clarke C;Haggerty L;Hemrom A;Hourlier T;Izuogu OG;Juettemann T;Kaikala V;Kay M;Lavidas I;Le T;Lemos D;Gonzalez Martinez J;Marugán JC;Maurel T;McMahon AC;Mohanan S;Moore B;Muffato M;Oheh DN;Paraschas D;Parker A;Parton A;Prosovetskaia I;Sakthivel MP;Salam AIA;Schmitt BM;Schuilenburg H;Sheppard D;Steed E;Szpak M;Szuba M;Taylor K;Thormann A;Threadgold G;Walts B;Winterbottom A;Chakiachvili M;Chaubal A;De Silva N;Flint B;Frankish A;Hunt SE;IIsley GR;Langridge N;Loveland JE;Martin FJ;Mudge JM;Morales J;Perry E;Ruffier M;Tate J;Thybert D;Trevanion SJ;Cunningham F;Yates AD;Zerbino DR;Flicek P
通讯作者:
Flicek P
影响因子:
14.9
作者:
Larkin A;Marygold SJ;Antonazzo G;Attrill H;Dos Santos G;Garapati PV;Goodman JL;Gramates LS;Millburn G;Strelets VB;Tabone CJ;Thurmond J;FlyBase Consortium
通讯作者:
FlyBase Consortium
影响因子:
--
作者:
Fazekas D;Koltai M;Türei D;Módos D;Pálfy M;Dúl Z;Zsákai L;Szalay-Bekő M;Lenti K;Farkas IJ;Vellai T;Csermely P;Korcsmáros T
通讯作者:
Korcsmáros T
影响因子:
14.9
作者:
Li JH;Liu S;Zhou H;Qu LH;Yang JH
通讯作者:
Yang JH
影响因子:
2.5
作者:
Melas, Ioannis N.;Sakellaropoulos, Theodore;Bai, Jane P. F.
通讯作者:
Bai, Jane P. F.