Lnc2Meth: a manually curated database of regulatory relationships between long non-coding RNAs and DNA methylation associated with human disease.
Lnc2Meth: a manually curated database of regulatory relationships between long non-coding RNAs and DNA methylation associated with human disease.
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Lnc2Meth:一个手动管理的数据库,记录与人类疾病相关的长非编码 RNA 和 DNA 甲基化之间的调控关系
DOI:
10.1093/nar/gkx985
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发表时间:
2018-01-04
影响因子:
14.9
通讯作者:
Li X
中科院分区:
文献类型:
--
作者:
Zhi H;Li X;Wang P;Gao Y;Gao B;Zhou D;Zhang Y;Guo M;Yue M;Shen W;Ning S;Jin L;Li X
Abstract Lnc2Meth (http://www.bio-bigdata.com/Lnc2Meth/), an interactive resource to identify regulatory relationships between human long non-coding RNAs (lncRNAs) and DNA methylation, is not only a manually curated collection and annotation of experimentally supported lncRNAs-DNA methylation associations but also a platform that effectively integrates tools for calculating and identifying the differentially methylated lncRNAs and protein-coding genes (PCGs) in diverse human diseases. The resource provides: (i) advanced search possibilities, e.g. retrieval of the database by searching the lncRNA symbol of interest, DNA methylation patterns, regulatory mechanisms and disease types; (ii) abundant computationally calculated DNA methylation array profiles for the lncRNAs and PCGs; (iii) the prognostic values for each hit transcript calculated from the patients clinical data; (iv) a genome browser to display the DNA methylation landscape of the lncRNA transcripts for a specific type of disease; (v) tools to re-annotate probes to lncRNA loci and identify the differential methylation patterns for lncRNAs and PCGs with user-supplied external datasets; (vi) an R package (LncDM) to complete the differentially methylated lncRNAs identification and visualization with local computers. Lnc2Meth provides a timely and valuable resource that can be applied to significantly expand our understanding of the regulatory relationships between lncRNAs and DNA methylation in various human diseases.
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影响因子:
12.3
作者:
Buels R;Yao E;Diesh CM;Hayes RD;Munoz-Torres M;Helt G;Goodstein DM;Elsik CG;Lewis SE;Stein L;Holmes IH
通讯作者:
Holmes IH
影响因子:
8.8
作者:
Bamford, S;Dawson, E;Forbes, S;Clements, J;Pettett, R;Dogan, A;Flanagan, A;Teague, J;Futreal, PA;Stratton, MR;Wooster, R
通讯作者:
Wooster, R
影响因子:
64.8
作者:
通讯作者:
--
影响因子:
7
作者:
Harrow J;Frankish A;Gonzalez JM;Tapanari E;Diekhans M;Kokocinski F;Aken BL;Barrell D;Zadissa A;Searle S;Barnes I;Bignell A;Boychenko V;Hunt T;Kay M;Mukherjee G;Rajan J;Despacio-Reyes G;Saunders G;Steward C;Harte R;Lin M;Howald C;Tanzer A;Derrien T;Chrast J;Walters N;Balasubramanian S;Pei B;Tress M;Rodriguez JM;Ezkurdia I;van Baren J;Brent M;Haussler D;Kellis M;Valencia A;Reymond A;Gerstein M;Guigó R;Hubbard TJ
通讯作者:
Hubbard TJ
影响因子:
14.9
作者:
Gong J;Liu C;Liu W;Xiang Y;Diao L;Guo AY;Han L
通讯作者:
Han L