Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 1 to 50 of 104

1.

A proteome-integrated, carbon source dependent genetic regulatory network in Saccharomyces cerevisiae.

Garcia-Albornoz M, Holman SW, Antonisse T, Daran-Lapujade P, Teusink B, Beynon RJ, Hubbard SJ.

Mol Omics. 2019 Dec 23. doi: 10.1039/c9mo00136k. [Epub ahead of print]

PMID:
31868867
2.

Disease modeling of core pre-mRNA splicing factor haploinsufficiency.

Wood KA, Rowlands CF, Qureshi WMS, Thomas HB, Buczek WA, Briggs TA, Hubbard SJ, Hentges KE, Newman WG, O'Keefe RT.

Hum Mol Genet. 2019 Nov 15;28(22):3704-3723. doi: 10.1093/hmg/ddz169.

3.

Glycolytic flux in Saccharomyces cerevisiae is dependent on RNA polymerase III and its negative regulator Maf1.

Szatkowska R, Garcia-Albornoz M, Roszkowska K, Holman SW, Furmanek E, Hubbard SJ, Beynon RJ, Adamczyk M.

Biochem J. 2019 Apr 4;476(7):1053-1082. doi: 10.1042/BCJ20180701.

4.

Ribosomal flavours: an acquired taste for specific mRNAs?

Bates C, Hubbard SJ, Ashe MP.

Biochem Soc Trans. 2018 Dec 17;46(6):1529-1539. doi: 10.1042/BST20180160. Epub 2018 Nov 12. Review.

PMID:
30420413
5.

Archetypal transcriptional blocks underpin yeast gene regulation in response to changes in growth conditions.

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

Sci Rep. 2018 May 21;8(1):7949. doi: 10.1038/s41598-018-26170-5.

6.

A quantitative and temporal map of proteostasis during heat shock in Saccharomyces cerevisiae.

Jarnuczak AF, Albornoz MG, Eyers CE, Grant CM, Hubbard SJ.

Mol Omics. 2018 Feb 1;14(1):37-52. doi: 10.1039/c7mo00050b. Epub 2018 Jan 16.

PMID:
29570196
7.

Dynamic changes in eIF4F-mRNA interactions revealed by global analyses of environmental stress responses.

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

Genome Biol. 2017 Oct 27;18(1):201. doi: 10.1186/s13059-017-1338-4.

8.

Dynamic Acclimation to High Light in Arabidopsis thaliana Involves Widespread Reengineering of the Leaf Proteome.

Miller MAE, O'Cualain R, Selley J, Knight D, Karim MF, Hubbard SJ, Johnson GN.

Front Plant Sci. 2017 Jul 20;8:1239. doi: 10.3389/fpls.2017.01239. eCollection 2017.

9.

Analysis of Intrinsic Peptide Detectability via Integrated Label-Free and SRM-Based Absolute Quantitative Proteomics.

Jarnuczak AF, Lee DC, Lawless C, Holman SW, Eyers CE, Hubbard SJ.

J Proteome Res. 2016 Sep 2;15(9):2945-59. doi: 10.1021/acs.jproteome.6b00048. Epub 2016 Aug 8.

PMID:
27454336
10.

Absolute protein quantification of the yeast chaperome under conditions of heat shock.

Mackenzie RJ, Lawless C, Holman SW, Lanthaler K, Beynon RJ, Grant CM, Hubbard SJ, Eyers CE.

Proteomics. 2016 Aug;16(15-16):2128-40. doi: 10.1002/pmic.201500503. Epub 2016 Jul 22.

11.

Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring.

Lawless C, Holman SW, Brownridge P, Lanthaler K, Harman VM, Watkins R, Hammond DE, Miller RL, Sims PF, Grant CM, Eyers CE, Beynon RJ, Hubbard SJ.

Mol Cell Proteomics. 2016 Apr;15(4):1309-22. doi: 10.1074/mcp.M115.054288. Epub 2016 Jan 10.

12.

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.

13.

Focus on Quantitative Proteomics.

Lilley KS, Beynon RJ, Eyers CE, Hubbard SJ.

Proteomics. 2015 Sep;15(18):3101-3. doi: 10.1002/pmic.201570163. No abstract available.

PMID:
26372724
14.

The 4E-BP Caf20p Mediates Both eIF4E-Dependent and Independent Repression of Translation.

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

PLoS Genet. 2015 May 14;11(5):e1005233. doi: 10.1371/journal.pgen.1005233. eCollection 2015 May.

15.

The effectiveness of different interventions to promote poison prevention behaviours in households with children: a network meta-analysis.

Achana FA, Sutton AJ, Kendrick D, Wynn P, Young B, Jones DR, Hubbard SJ, Cooper NJ.

PLoS One. 2015 Apr 20;10(3):e0121122. doi: 10.1371/journal.pone.0121122. eCollection 2015.

16.

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae.

Jarnuczak AF, Eyers CE, Schwartz JM, Grant CM, Hubbard SJ.

Proteomics. 2015 Sep;15(18):3126-39. doi: 10.1002/pmic.201400527. Epub 2015 Apr 10.

17.

Global mRNA selection mechanisms for translation initiation.

Costello J, Castelli LM, Rowe W, Kershaw CJ, Talavera D, Mohammad-Qureshi SS, Sims PF, Grant CM, Pavitt GD, Hubbard SJ, Ashe MP.

Genome Biol. 2015 Jan 5;16:10. doi: 10.1186/s13059-014-0559-z.

18.

The yeast La related protein Slf1p is a key activator of translation during the oxidative stress response.

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

PLoS Genet. 2015 Jan 8;11(1):e1004903. doi: 10.1371/journal.pgen.1004903. eCollection 2015 Jan.

19.

A decision analytic model to investigate the cost-effectiveness of poisoning prevention practices in households with young children.

Achana F, Sutton AJ, Kendrick D, Hayes M, Jones DR, Hubbard SJ, Cooper NJ.

BMC Public Health. 2016 Aug 3;15:705. doi: 10.1186/s12889-016-3334-0.

20.

Computational phosphoproteomics: from identification to localization.

Lee DC, Jones AR, Hubbard SJ.

Proteomics. 2015 Mar;15(5-6):950-63. doi: 10.1002/pmic.201400372. Epub 2015 Feb 17. Review.

21.

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes.

Achana FA, Cooper NJ, Bujkiewicz S, Hubbard SJ, Kendrick D, Jones DR, Sutton AJ.

BMC Med Res Methodol. 2014 Jul 21;14:92. doi: 10.1186/1471-2288-14-92.

22.

A research agenda for acute care services delivery in low- and middle-income countries.

Moresky RT, Bisanzo M, Rubenstein BL, Hubbard SJ, Cohen H, Ouyang H, Duber HC, Marsh RH.

Acad Emerg Med. 2013 Dec;20(12):1264-71. doi: 10.1111/acem.12259. Epub 2013 Nov 27.

23.

Puf3p induces translational repression of genes linked to oxidative stress.

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

Nucleic Acids Res. 2014 Jan;42(2):1026-41. doi: 10.1093/nar/gkt948. Epub 2013 Oct 25.

24.

Home safety education and provision of safety equipment for injury prevention (Review).

Kendrick D, Young B, Mason-Jones AJ, Ilyas N, Achana FA, Cooper NJ, Hubbard SJ, Sutton AJ, Smith S, Wynn P, Mulvaney C, Watson MC, Coupland C.

Evid Based Child Health. 2013 May;8(3):761-939. doi: 10.1002/ebch.1911. Review.

PMID:
23877910
25.

Quantitative analysis of chaperone network throughput in budding yeast.

Brownridge P, Lawless C, Payapilly AB, Lanthaler K, Holman SW, Harman VM, Grant CM, Beynon RJ, Hubbard SJ.

Proteomics. 2013 Apr;13(8):1276-91. doi: 10.1002/pmic.201200412. Epub 2013 Mar 15.

26.

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies.

Blakeley P, Overton IM, Hubbard SJ.

J Proteome Res. 2012 Nov 2;11(11):5221-34. doi: 10.1021/pr300411q. Epub 2012 Oct 15.

27.

Home safety education and provision of safety equipment for injury prevention.

Kendrick D, Young B, Mason-Jones AJ, Ilyas N, Achana FA, Cooper NJ, Hubbard SJ, Sutton AJ, Smith S, Wynn P, Mulvaney C, Watson MC, Coupland C.

Cochrane Database Syst Rev. 2012 Sep 12;(9):CD005014. doi: 10.1002/14651858.CD005014.pub3. Review.

PMID:
22972081
28.

Prediction of missed proteolytic cleavages for the selection of surrogate peptides for quantitative proteomics.

Lawless C, Hubbard SJ.

OMICS. 2012 Sep;16(9):449-56. doi: 10.1089/omi.2011.0156. Epub 2012 Jul 17.

29.

A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis.

Gonzalez-Galarza FF, Lawless C, Hubbard SJ, Fan J, Bessant C, Hermjakob H, Jones AR.

OMICS. 2012 Sep;16(9):431-42. doi: 10.1089/omi.2012.0022. Epub 2012 Jul 17.

30.

The mzIdentML data standard for mass spectrometry-based proteomics results.

Jones AR, Eisenacher M, Mayer G, Kohlbacher O, Siepen J, Hubbard SJ, Selley JN, Searle BC, Shofstahl J, Seymour SL, Julian R, Binz PA, Deutsch EW, Hermjakob H, Reisinger F, Griss J, Vizcaíno JA, Chambers M, Pizarro A, Creasy D.

Mol Cell Proteomics. 2012 Jul;11(7):M111.014381. doi: 10.1074/mcp.M111.014381. Epub 2012 Feb 27.

31.

CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches.

Eyers CE, Lawless C, Wedge DC, Lau KW, Gaskell SJ, Hubbard SJ.

Mol Cell Proteomics. 2011 Nov;10(11):M110.003384. doi: 10.1074/mcp.M110.003384. Epub 2011 Aug 3.

32.

Global absolute quantification of a proteome: Challenges in the deployment of a QconCAT strategy.

Brownridge P, Holman SW, Gaskell SJ, Grant CM, Harman VM, Hubbard SJ, Lanthaler K, Lawless C, O'Cualain R, Sims P, Watkins R, Beynon RJ.

Proteomics. 2011 Aug;11(15):2957-70. doi: 10.1002/pmic.201100039. Epub 2011 Jun 28. Review.

PMID:
21710569
33.

FDRAnalysis: a tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines.

Wedge DC, Krishna R, Blackhurst P, Siepen JA, Jones AR, Hubbard SJ.

J Proteome Res. 2011 Apr 1;10(4):2088-94. doi: 10.1021/pr101157s. Epub 2011 Feb 21.

34.

Distributions of ion series in ETD and CID spectra: making a comparison.

Hart SR, Lau KW, Gaskell SJ, Hubbard SJ.

Methods Mol Biol. 2011;696:327-37. doi: 10.1007/978-1-60761-987-1_21.

PMID:
21063958
35.

UKPMC: a full text article resource for the life sciences.

McEntyre JR, Ananiadou S, Andrews S, Black WJ, Boulderstone R, Buttery P, Chaplin D, Chevuru S, Cobley N, Coleman LA, Davey P, Gupta B, Haji-Gholam L, Hawkins C, Horne A, Hubbard SJ, Kim JH, Lewin I, Lyte V, MacIntyre R, Mansoor S, Mason L, McNaught J, Newbold E, Nobata C, Ong E, Pillai S, Rebholz-Schuhmann D, Rosie H, Rowbotham R, Rupp CJ, Stoehr P, Vaughan P.

Nucleic Acids Res. 2011 Jan;39(Database issue):D58-65. doi: 10.1093/nar/gkq1063. Epub 2010 Nov 9.

36.

Identifying eIF4E-binding protein translationally-controlled transcripts reveals links to mRNAs bound by specific PUF proteins.

Cridge AG, Castelli LM, Smirnova JB, Selley JN, Rowe W, Hubbard SJ, McCarthy JE, Ashe MP, Grant CM, Pavitt GD.

Nucleic Acids Res. 2010 Dec;38(22):8039-50. doi: 10.1093/nar/gkq686. Epub 2010 Aug 12.

37.

Investigating protein isoforms via proteomics: a feasibility study.

Blakeley P, Siepen JA, Lawless C, Hubbard SJ.

Proteomics. 2010 Mar;10(6):1127-40. doi: 10.1002/pmic.200900445.

38.

Cross species proteomics.

Wright JC, Beynon RJ, Hubbard SJ.

Methods Mol Biol. 2010;604:123-35. doi: 10.1007/978-1-60761-444-9_9.

PMID:
20013368
39.

Computational approaches to peptide identification via tandem MS.

Hubbard SJ.

Methods Mol Biol. 2010;604:23-42. doi: 10.1007/978-1-60761-444-9_3.

PMID:
20013362
40.

An introduction to proteome bioinformatics.

Jones AR, Hubbard SJ.

Methods Mol Biol. 2010;604:1-5. doi: 10.1007/978-1-60761-444-9_1.

PMID:
20013360
41.

Getting a grip on proteomics data - Proteomics Data Collection (ProDaC).

Eisenacher M, Martens L, Hardt T, Kohl M, Barsnes H, Helsens K, Häkkinen J, Levander F, Aebersold R, Vandekerckhove J, Dunn MJ, Lisacek F, Siepen JA, Hubbard SJ, Binz PA, Blüggel M, Thiele H, Cottrell J, Meyer HE, Apweiler R, Stephan C.

Proteomics. 2009 Aug;9(15):3928-33. doi: 10.1002/pmic.200900247.

PMID:
19637238
42.

Observations on the detection of b- and y-type ions in the collisionally activated decomposition spectra of protonated peptides.

Lau KW, Hart SR, Lynch JA, Wong SC, Hubbard SJ, Gaskell SJ.

Rapid Commun Mass Spectrom. 2009 May;23(10):1508-14. doi: 10.1002/rcm.4032.

PMID:
19370712
43.

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines.

Jones AR, Siepen JA, Hubbard SJ, Paton NW.

Proteomics. 2009 Mar;9(5):1220-9. doi: 10.1002/pmic.200800473.

44.

Recent developments in proteome informatics for mass spectrometry analysis.

Wright JC, Hubbard SJ.

Comb Chem High Throughput Screen. 2009 Feb;12(2):194-202. Review.

PMID:
19199887
45.

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger.

Wright JC, Sugden D, Francis-McIntyre S, Riba-Garcia I, Gaskell SJ, Grigoriev IV, Baker SE, Beynon RJ, Hubbard SJ.

BMC Genomics. 2009 Feb 4;10:61. doi: 10.1186/1471-2164-10-61.

46.

Upstream sequence elements direct post-transcriptional regulation of gene expression under stress conditions in yeast.

Lawless C, Pearson RD, Selley JN, Smirnova JB, Grant CM, Ashe MP, Pavitt GD, Hubbard SJ.

BMC Genomics. 2009 Jan 7;10:7. doi: 10.1186/1471-2164-10-7.

47.

Expression screening and annotation of a zebrafish myoblast cDNA library.

Baxendale S, Chen CK, Tang H, Davison C, Hateren LV, Croning MD, Humphray SJ, Hubbard SJ, Ingham PW.

Gene Expr Patterns. 2009 Feb;9(2):73-82. doi: 10.1016/j.gep.2008.10.003. Epub 2008 Oct 25.

PMID:
19007914
48.

Analysis of the trypanosome flagellar proteome using a combined electron transfer/collisionally activated dissociation strategy.

Hart SR, Lau KW, Hao Z, Broadhead R, Portman N, Hühmer A, Gull K, McKean PG, Hubbard SJ, Gaskell SJ.

J Am Soc Mass Spectrom. 2009 Feb;20(2):167-75. doi: 10.1016/j.jasms.2008.08.014. Epub 2008 Sep 3.

49.

Sequence search algorithms for single pass sequence identification: does one size fit all?

Woodwark KC, Hubbard SJ, Oliver SG.

Comp Funct Genomics. 2001;2(1):4-9. doi: 10.1002/cfg.61.

50.

PepSeeker: mining information from proteomic data.

Siepen JA, Selley JN, Hubbard SJ.

Methods Mol Biol. 2008;484:319-32. doi: 10.1007/978-1-59745-398-1_21.

PMID:
18592189

Supplemental Content

Loading ...
Support Center