Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 60

1.

Two separate pathways regulate protein stability of ATM/ATR-related protein kinases Mec1 and Tel1 in budding yeast.

Goto GH, Ogi H, Biswas H, Ghosh A, Tanaka S, Sugimoto K.

PLoS Genet. 2017 Aug 21;13(8):e1006873. doi: 10.1371/journal.pgen.1006873. eCollection 2017 Aug.

2.

Multiplexed gene control reveals rapid mRNA turnover.

Baudrimont A, Voegeli S, Viloria EC, Stritt F, Lenon M, Wada T, Jaquet V, Becskei A.

Sci Adv. 2017 Jul 12;3(7):e1700006. doi: 10.1126/sciadv.1700006. eCollection 2017 Jul.

3.

The interplay between transcription and mRNA degradation in Saccharomyces cerevisiae.

Das S, Sarkar D, Das B.

Microb Cell. 2017 Jul 3;4(7):212-228. doi: 10.15698/mic2017.07.580. Review.

4.

Structure-Function Model for Kissing Loop Interactions That Initiate Dimerization of Ty1 RNA.

Gamache ER, Doh JH, Ritz J, Laederach A, Bellaousov S, Mathews DH, Curcio MJ.

Viruses. 2017 Apr 26;9(5). pii: E93. doi: 10.3390/v9050093.

5.

Capturing in vivo RNA transcriptional dynamics from the malaria parasite Plasmodium falciparum.

Painter HJ, Carrasquilla M, LlinĂ¡s M.

Genome Res. 2017 Jun;27(6):1074-1086. doi: 10.1101/gr.217356.116. Epub 2017 Apr 17.

PMID:
28416533
6.

Kinetic CRAC uncovers a role for Nab3 in determining gene expression profiles during stress.

van Nues R, Schweikert G, de Leau E, Selega A, Langford A, Franklin R, Iosub I, Wadsworth P, Sanguinetti G, Granneman S.

Nat Commun. 2017 Apr 11;8(1):12. doi: 10.1038/s41467-017-00025-5.

7.

Extremely fast and incredibly close: cotranscriptional splicing in budding yeast.

Wallace EWJ, Beggs JD.

RNA. 2017 May;23(5):601-610. doi: 10.1261/rna.060830.117. Epub 2017 Feb 2. Review.

8.

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans.

Peterson JR, Thor S, Kohler L, Kohler PR, Metcalf WW, Luthey-Schulten Z.

BMC Genomics. 2016 Nov 16;17(1):924.

9.

Measures of RNA metabolism rates: Toward a definition at the level of single bonds.

Wachutka L, Gagneur J.

Transcription. 2017 Mar 15;8(2):75-80. doi: 10.1080/21541264.2016.1257972. Epub 2016 Nov 14.

10.
11.

A prior-based integrative framework for functional transcriptional regulatory network inference.

Siahpirani AF, Roy S.

Nucleic Acids Res. 2017 Feb 28;45(4):e21. doi: 10.1093/nar/gkw963.

12.

Multiple Transcript Properties Related to Translation Affect mRNA Degradation Rates in Saccharomyces cerevisiae.

Neymotin B, Ettore V, Gresham D.

G3 (Bethesda). 2016 Sep 15. pii: g3.116.032276. doi: 10.1534/g3.116.032276. [Epub ahead of print]

13.

Determinants of Genomic RNA Encapsidation in the Saccharomyces cerevisiae Long Terminal Repeat Retrotransposons Ty1 and Ty3.

Pachulska-Wieczorek K, Le Grice SF, Purzycka KJ.

Viruses. 2016 Jul 14;8(7). pii: E193. doi: 10.3390/v8070193. Review.

14.

Opposing PKA and Hog1 signals control the post-transcriptional response to glucose availability in Cryptococcus neoformans.

Banerjee D, Bloom AL, Panepinto JC.

Mol Microbiol. 2016 Oct;102(2):306-320. doi: 10.1111/mmi.13461. Epub 2016 Aug 11.

PMID:
27387858
15.

Spatial organization shapes the turnover of a bacterial transcriptome.

Moffitt JR, Pandey S, Boettiger AN, Wang S, Zhuang X.

Elife. 2016 May 20;5. pii: e13065. doi: 10.7554/eLife.13065.

16.

Steady-state and dynamic gene expression programs in Saccharomyces cerevisiae in response to variation in environmental nitrogen.

Airoldi EM, Miller D, Athanasiadou R, Brandt N, Abdul-Rahman F, Neymotin B, Hashimoto T, Bahmani T, Gresham D.

Mol Biol Cell. 2016 Apr 15;27(8):1383-96. doi: 10.1091/mbc.E14-05-1013. Epub 2016 Mar 3.

17.

Determinants of RNA metabolism in the Schizosaccharomyces pombe genome.

Eser P, Wachutka L, Maier KC, Demel C, Boroni M, Iyer S, Cramer P, Gagneur J.

Mol Syst Biol. 2016 Feb 16;12(2):857. doi: 10.15252/msb.20156526.

18.

Snf1-Dependent Transcription Confers Glucose-Induced Decay upon the mRNA Product.

Braun KA, Dombek KM, Young ET.

Mol Cell Biol. 2015 Dec 14;36(4):628-44. doi: 10.1128/MCB.00436-15.

19.

A cell size- and cell cycle-aware stochastic model for predicting time-dynamic gene network activity in individual cells.

Song R, Peng W, Liu P, Acar M.

BMC Syst Biol. 2015 Dec 9;9:91. doi: 10.1186/s12918-015-0240-5.

20.

Integrated multi-omics analyses reveal the pleiotropic nature of the control of gene expression by Puf3p.

Kershaw CJ, Costello JL, Talavera D, Rowe W, Castelli LM, Sims PF, Grant CM, Ashe MP, Hubbard SJ, Pavitt GD.

Sci Rep. 2015 Oct 23;5:15518. doi: 10.1038/srep15518.

Supplemental Content

Support Center