starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data.
starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data.
复制标题
starBase:用于探索 Argonaute CLIP-Seq 和 Degradome-Seq 数据中 microRNA 与 mRNA 相互作用图谱的数据库
DOI:
10.1093/nar/gkq1056
复制
发表时间:
2011-01
影响因子:
14.9
通讯作者:
Qu LH
中科院分区:
文献类型:
--
作者:
Yang JH;Li JH;Shao P;Zhou H;Chen YQ;Qu LH
MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (sRNAs) that regulate gene expression by targeting messenger RNAs. However, assigning miRNAs to their regulatory target genes remains technically challenging. Recently, high-throughput CLIP-Seq and degradome sequencing (Degradome-Seq) methods have been applied to identify the sites of Argonaute interaction and miRNA cleavage sites, respectively. In this study, we introduce a novel database, starBase (sRNA target Base), which we have developed to facilitate the comprehensive exploration of miRNA–target interaction maps from CLIP-Seq and Degradome-Seq data. The current version includes high-throughput sequencing data generated from 21 CLIP-Seq and 10 Degradome-Seq experiments from six organisms. By analyzing millions of mapped CLIP-Seq and Degradome-Seq reads, we identified ∼1 million Ago-binding clusters and ∼2 million cleaved target clusters in animals and plants, respectively. Analyses of these clusters, and of target sites predicted by 6 miRNA target prediction programs, resulted in our identification of approximately 400 000 and approximately 66 000 miRNA-target regulatory relationships from CLIP-Seq and Degradome-Seq data, respectively. Furthermore, two web servers were provided to discover novel miRNA target sites from CLIP-Seq and Degradome-Seq data. Our web implementation supports diverse query types and exploration of common targets, gene ontologies and pathways. The starBase is available at http://starbase.sysu.edu.cn/.
登录
查看更多内容
影响因子:
64.8
作者:
Baek, Daehyun;Villen, Judit;Shin, Chanseok;Camargo, Fernando D.;Gygi, Steven P.;Bartel, David P.
通讯作者:
Bartel, David P.
影响因子:
64.5
作者:
Hafner M;Landthaler M;Burger L;Khorshid M;Hausser J;Berninger P;Rothballer A;Ascano M Jr;Jungkamp AC;Munschauer M;Ulrich A;Wardle GS;Dewell S;Zavolan M;Tuschl T
通讯作者:
Tuschl T
影响因子:
9.2
作者:
Addo-Quaye, Charles;Eshoo, Tifani W.;Axtell, Michael J.
通讯作者:
Axtell, Michael J.
影响因子:
64.8
作者:
Jaillon, Olivier;Aury, Jean-Marc;Wincker, Patrick
通讯作者:
Wincker, Patrick
影响因子:
5.8
作者:
Addo-Quaye, Charles;Miller, Webb;Axtell, Michael J.
通讯作者:
Axtell, Michael J.