Translation regulation gets its 'omics' moment.
Translation regulation gets its 'omics' moment.
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DOI：
10.1002/wrna.1173
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发表时间：
2013-11
影响因子：
7.3
通讯作者：
Penalva, Luiz O. F.
中科院分区：
文献类型：
--
作者：
Kuersten, Scott;Radek, Agnes;Vogel, Christine;Penalva, Luiz O. F.
The fate of cellular RNA is largely determined by complex networks of protein-RNA interactions through ribonucleoprotein (RNPs) complexes. Despite their relatively short half-life, transcripts associate with many different proteins that process, modify, translate, and degrade the RNA. Following biogenesis some mRNPs are immediately directed to translation and produce proteins, but many are diverted and regulated by processes including miRNA-mediated mechanisms, transport and localization as well as turnover. Because of this complex interplay estimates of steady state expression by methods such as RNAseq alone cannot capture critical aspects of cellular fate, environmental response, tumorigenesis or gene expression regulation. More selective and integrative tools are needed to measure protein-RNA complexes and the regulatory processes involved. One focus area are measurements of the transcriptome associated with ribosomes and translation. These so-called polysome or ribosome profiling techniques can evaluate translation efficiency as well as the interplay between translation initiation, elongation and termination - subject areas not well understood at a systems biology level. Ribosome profiling is a highly promising technique which provides mRNA positional information of ribosome occupancy, potentially bridging the gap between gene expression (i.e. RNAseq and microarray analysis) and protein quantification (i.e. mass spectrometry). In combination with methods such as RNA immunoprecipitation, miRNA profiling, or proteomics, we obtain a fresh view of global post-transcriptional and translational gene regulation. In addition, these techniques also provide new insight into new regulatory elements, such as alternative open reading frames, and translation regulation under different conditions.
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影响因子：
64.8
作者：
Hsieh, Andrew C.;Liu, Yi;Edlind, Merritt P.;Ingolia, Nicholas T.;Janes, Matthew R.;Sher, Annie;Shi, Evan Y.;Stumpf, Craig R.;Christensen, Carly;Bonham, Michael J.;Wang, Shunyou;Ren, Pingda;Martin, Michael;Jessen, Katti;Feldman, Morris E.;Weissman, Jonathan S.;Shokat, Kevan M.;Rommel, Christian;Ruggero, Davide
通讯作者：
Ruggero, Davide
DOI：
10.1126/science.1215691
发表时间：
2012-04-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Djuranovic S;Nahvi A;Green R
通讯作者：
Green R
影响因子：
12.3
作者：
Cloonan N;Wani S;Xu Q;Gu J;Lea K;Heater S;Barbacioru C;Steptoe AL;Martin HC;Nourbakhsh E;Krishnan K;Gardiner B;Wang X;Nones K;Steen JA;Matigian NA;Wood DL;Kassahn KS;Waddell N;Shepherd J;Lee C;Ichikawa J;McKernan K;Bramlett K;Kuersten S;Grimmond SM
通讯作者：
Grimmond SM
DOI：
10.1126/science.1215110
发表时间：
2012-02-03
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Brar GA;Yassour M;Friedman N;Regev A;Ingolia NT;Weissman JS
通讯作者：
Weissman JS
影响因子：
14.8
作者：
Ingolia, Nicholas T.;Brar, Gloria A.;Rouskin, Silvia;McGeachy, Anna M.;Weissman, Jonathan S.
通讯作者：
Weissman, Jonathan S.