糖心TV

Mirela Domijan, Paul E.Brown, Boris V. Shulgin and David A. Rand

Ward, D.G., Baxter, L., Gordon, N.S., Ott, S., Savage, R.S., Beggs, A.D., James, J.D., Lickiss, J., Green, S., Wallis, Y. and Wei, W.

Richard S. Savage, Yinyin Yuan

Jim Skinner, Nicola O’Connell, Subiatu Wurie, Samantha Chambers, Chuka Nwokolo, Karna Bardhan, Richard Savage, James Covington

J. A. Covington, M.P. van. der Schee, A.S.L. Edge, B. Boyle, R.S. Savage and R. P. Arasaradnam

Usman Ahmed, Attia Anwar, Richard S. Savage, Matthew L. Costa, Nicola Mackay, Andrew Filer, Karim Raza, Richard A. Watts, Paul G. Winyard, Joanna Tarr, Richard C. Haigh, Paul J. Thornalley & Naila Rabbani

Ramesh P. Arasaradnam^1,4, Michael McFarlane¹, Emma Daulton², Erik Westenbrink², Nicola O'Connell¹, Subiatu Wurie¹, Chuka U. Nwokolo¹, Karna D. Bardhan³, Richard S. Savage^{5, 6}, James A. Covington²

Marije K Bomers, Frederik P Menke, Richard S Savage, Christina M J E Vandenbroucke-Grauls, Michiel A van Agtmael, James A Covington, and Yvo M Smulders

Lloyd, Katherine L., Ian A. Cree, and Richard S. Savage

• Ramesh P. Arasaradnam mail,

• Eric Westenbrink,
• Michael J. McFarlane,
• Ruth Harbord,
• Samantha Chambers,
• Nicola O’Connell,
• Catherine Bailey,
• Chuka U. Nwokolo,
• Karna D. Bardhan,
• Richard Savage,
• James A. Covington

Richard A. Tyson, Evgeny Zatulovskiy, Robert R. Kay, and Till Bretschneider
PNAS 2014 ; published ahead of print July 29, 2014, doi:10.1073/pnas.1322291111

We present a methodology for dealing with recent challenges in testing global hypotheses using multivariate observations. The proposed tests target situations, often arising in emerging applications of neuroimaging, where the sample size n is relatively small compared with the observations’ dimension K. We employ adaptive designs allowing for sequential modifications of the test statistics adapting to accumulated data. The adaptations are optimal in the sense of maximizing the predictive power of the test at each interim analysis while still controlling the Type I error. Optimality is obtained by a general result applicable to typical adaptive design settings. Further, we prove that the potentially high-dimensional design space of the tests can be reduced to a low-dimensional projection space enabling us to perform simpler power analysis studies, including comparisons to alternative tests. We illustrate the substantial improvement in efficiency that the proposed tests can make over standard tests, especially in the case ofn smaller or slightly larger than K. The methods are also studied empirically using both simulated data and data from an EEG study, where the use of prior knowledge substantially increases the power of the test.

The proliferating cell hypothesis: a metabolic framework for Plasmodium growth and development

J. Enrique Salcedo-Sora, , Stephen A. Ward, Giancarlo A. Biagini

• Liverpool School of Tropical Medicine, Pembroke Place, Liverpool , L3 5QA, UK
• 糖心TV Systems Biology Centre, Senate House, University of 糖心TV, Coventry , CV4 7AL, UK

Evgeny Zatulovskiy, Richard Tyson, Till Bretschneider, and Robert R. Kay

CF Nellist, W Qian, CE Jenner, JD Moore, S Zhang, X Wang, WH Briggs, GC Barker, R Sun and JA Walsh
.

J.D. Sidda, L. Song, V. Poon, M. Al-Bassam, O. Lazos, M.J. Buttner, G.L. Challis and C. Corre

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Sirinukunwattana K., , Bari M., Snead D., Rajpoot N., 2013, PLOS ONE

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data.

The implementation of GBHC is available at

P.D. Gould*, N. Ugarte*, M. Domijan*, M.J. Costa, J. Foreman, D. McGregor, S. Penfield, D.A. Rand, A. Hall, K. Halliday, A.J. Millar, (2013) , Mol. Syst. Biol. 9(650). DOI:10.1038/msb.2013.7 ()

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(2012) 40 (12): 5227-39. doi:

Determining transcriptional regulator activities is a major focus of systems biology, providing key insight into regulatory mechanisms and co-regulators. For organisms such as Escherichia coli, transcriptional regulator binding site data can be integrated with expression data to infer transcriptional regulator activities. However, for most organisms there is only sparse data on their transcriptional regulators, while their associated binding motifs are largely unknown. Here, we address the challenge of inferring activities of unknown regulators by generating de novo (binding) motifs and integrating with expression data. We identify a number of key regulators active in the metabolic switch, including PhoP with its associated directed repeat PHO box, candidate motifs for two SARPs, a CRP family regulator, an iron response regulator and that for LexA. Experimental validation for some of our predictions was obtained using gel-shift assays. Our analysis is applicable to any organism for which there is a reasonable amount of complementary expression data and for which motifs (either over represented or evolutionary conserved) can be identified in the genome.

Leonelli, S., Smirnoff, N., Moore, J., Cook, C. and Bastow, R. (2013)

Brown, P., Baxter, L., Hickman, R., Beynon, J., Moore, J.D. and Ott, S. (2013) .

Hickman, R., Hill, C., Penfold, C.A., Breeze, E., Bowden, L., Moore, J.D., Zhang, P., Jackson, A., Cooke, E., Bewicke-Copley, F., Mead, A., Beynon, J., Wild, D.L., Denby, K.J., Ott, S. and Buchanan-Wollaston, V. (2013). .

Bond M, Croft W, Tyson R, Bretschneider T, Davey J, Ladds G.
Yeast. 2013 Apr;30(4):145-56. doi: 10.1002/yea.2949. Epub 2013 Mar 20.

Traka, M. H., Saha, S., Huseby, S., Kopriva, S., Walley, P. G., Barker, G., Moore, J., Mero, G., van den Bosch, F., Constant, H., Kelly, L., Schepers, H., Boddupalli, S., and Mithen, R. F. (2013). Genetic regulation of glucoraphanin accumulation in Beneforté® broccoli.

Robert Darkins, Emma J Cooke, Zoubin Ghahramani, Paul D.W. Kirk, David L. Wild, Richard S. Savage, PLOS ONE (2013)

Dafyd J. Jenkins, Barbel Finkenstadt and David A. Rand. (2013)

Kirk, P., Griffin, J.E., Savage, R.S., Ghahramani, Z. and Wild, D.L. Bioinformatics (2012) 28 (24): 3290-3297.

Nikolas S. Burkoff, Csilla Várnai and David L. Wild. (2013) .

Laura Baxter, Aleksey Jironkin, Richard Hickman, Jay Moore, Christopher Barrington, Peter Krusche, Nigel P. Dyer, Vicky Buchanan-Wollaston, Alexander Tiskin, Jim Beynon, Katherine Denby, Sascha Ott (2012), Plant Cell, 24: 3949-3965

Kitchen, J.L., Moore, J.D., Palmer, S.A. and Allaby, R.G. (2012) MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling.

Windram, O., Madhou, P., McHattie, S., Hill, C., Hickman, R., Cooke, E., Jenkins, D.J., Penfold, C.A., Baxter, L., Breeze, E., Kiddle, S.J., Rhodes, J., Atwell, S., Klieberstein, D.J., Kim Y-S., Stegle, O., Borgwardt, K., Zhang, C., Tabrett, A., Legaie, R., Moore, J., Finkenstadt, B., Wild, D.L., Mead, A., Rand, D., Beynon, J., Ott, S, Buchanan-Wollaston, V. and Denby, K. (2012) Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis. .

Palmer, S.A., Clapham, A.J., Rose, P., Freitas, F.O., Owen, B.D., Beresford-Jones, D., , Kitchen, J.L. and Allaby, R.G. (2012) Archaeogenomic evidence of punctuated genome evolution in Gossypium. .

Christopher A. Penfold, Paul E. Brown, Neil D. Lawrence, Alastair S. H. Goldman, 2012, PLoS Comput Biol 8(5): e1002496. doi:10.1371/journal.pcbi.1002496

Thomas, L., Hodgson, D.A., Wentzel, A., Nieselt, K., Ellingsen, T.E., Moore. J., Morrissey, E.R., Legaie, R; The STREAM Consortium, Wohleben, W., Rodriguez-Garcia, A., Martin, J.F., Burroughs, N.J., Wellington, E.M., Smith, M.C. (2012) Mol. Cell Proteomics, 11(2): M111.013797.

Mike J. Downey, Danuta M. Jeziorska, Sascha Ott, T. Katherine Tamai, Georgy Koentges, Keith W. Vance, Till Bretschneider, 2011, PLoS ONE 6(12): e27886.

Yuan Y, (joint 1st author), Markowetz F, 2011 PLoS Computational Biology 7(10)

Mukhtar, M.S., Carvunis, A-R., Dreze, M., Epple, P., Steinbrenner, J., Moore, J., Tasan, M., Galli, M., Hao, T., Nishimura, M.T., Pevzner, S.J., Donovan, S.E., Ghamsari, L., Santhanam, B., Romero, V., Poulin, M.M., Gebreab, F., Gutierrez, B.J., Tam, S., Monachello, D., Boxem, M., Harbort, C.J., McDonald, N., Gai, L., Chen, H., He, Y., European Union Effectoromics Consrtium, Vandenhaute, J., Roth, F.P., Hill, D.E., Ecker, J.R., Vidal, M., Beynon, J., Braun, P., Dangl, J.L. (2011) Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science 333(6042): 596-601.

Arabidopsis Interactome Mapping Consortium (2011) Evidence for network evolution in an Arabidopsis interactome map. Science 333(6042): 601-607.

Breeze, E., Harrison, E., McHattie, S., Hughes, L., Hickman, R., Hill, C., Kiddle, S., Kim, Y-S., Penfold, C.A., Jenkins, D., Zhang, C., Morris, K., Jenner, C., Jackson, S., Thomas, B., Tabrett, A., Legaie, R., Moore, J.D., Wild, D.L., Ott, S., Rand, D., Beynon, J., Denby, K., Mead, A. and Buchanan-Wollaston, V. (2011) The Plant Cell 23 (3): 873-894.

Céline Granier, Vasily Gurchenkov, Aitana Perea-Gomez, Anne Camus, , Costis Papanayotou, Julian Iranzo, Anne Moreau, John Reid, , Délara Sabéran-Djoneidi, Jérôme Collignon (2011) Developmental Biology, 349: 350-362

Moore, J., Jironkin, A., Chandler, D., Burroughs, N., Evans, D.J. and Ryabov, E.V. (2011) Recombinants between Deformed wing virus and Varroa destructor virus-1 may prevail in Varroa destructor-infested honeybee colonies. .

, Sucheta Tripathy, Naveed Ishaque, Nico Boot, Adriana Cabra, Eric Kemen, Marco Thines, Audrey Ah-Fong, Ryan Anderson, Wole Badejoko, Peter Bittner-Eddy, Jeffrey L. Boore, Marcus C. Chibucos, , Paramvir Dehal, Kim Delehaunty, Suomeng Dong, Polly Downton, Bernard Dumas, Georgina Fabro, Catrina Fronick, Susan I. Fuerstenberg, Lucinda Fulton, Elodie Gaulin, Francine Govers, Linda Hughes, Sean Humphray, Rays H. Y. Jiang, Howard Judelson, Sophien Kamoun, Kim Kyung, Harold Meijer, Patrick Minx, Paul Morris, Joanne Nelson, Vipa Phuntumart, Dinah Qutob, Anne Rehmany, Alejandra Rougon-Cardoso, Peter Ryden, Trudy Torto-Alalibo, David Studholme, Yuanchao Wang, Joe Win, Jo Wood, Sandra W. Clifton, Jane Rogers, Guido Van den Ackerveken, Jonathan D. G. Jones, John M. McDowell, , and Brett M. Tyler.  Signatures of Adaptation to Obligate Biotrophy in the Hyaloperonospora arabidopsidis Genome.  

Du, C.-J., Marcello, M., Spiller, D. G., White, M. R. H. and Bretschneider, T. , Interactive segmentation of clustered cells via geodesic commute distance and constrained density weighted Nyström method. Cytometry Part A, n/a. doi: 10.1002/cyto.a.20993, 2010

Edwards, K. D., Akman, O. E., Knox, K., Lumsden, P. J., Thomson, A. W., Brown, P. E.,  Pokhilko, A., Kozma-Bognar, L., Nagy, F., Rand, D. A.,  & Millar, A. J. (2010). Molecular Systems Biology 6:424  doi:10.1038/msb.2010.81

Badel L, Lefort S, Brette R, Petersen CC, Gerstner W and Richardson MJE. Journal of Neurophysiology 99: 656-666 (2008)

Hassani-Pak, K., Legaie, R., Canevet, C., van den Berg, H.A., Moore, J.D. and Rawlings, C.  (2010)  Journal of Integrative Bioinformatics 7:3.

Mohammad T Alam, Maria E Merlo, The STREAM Consortium, David A Hodgson, Elizabeth MH Wellington, Eriko Takano, Rainer Breitling (2010). BMC Genomics 11:202.

Nieselt, K., Battke, F., Herbig, A., Bruheim, P., Wentzel, A., Jakobsen, O.M., Sletta, H., Alam, M.T., Merlo, M.E., Moore, J., Omara,W., Morrissey, E.R., Juarez-Hermosillo, M., Rodríguez-García, A., Nentwich, M., Thomas, L., Legaie, R., Gaze, W.H., Challis, G.L., Jansen, R.C., Dijkhuizen, L., Rand, D.A., Wild, D.L., Bonin, M., Reuther, J., Wohlleben, W., Smith, M.C.M., Burroughs, N.J., Martín, J.F., Hodgson, D.A., Takano, E., Breitling, R., Ellingsen, T.E., Elizabeth M. H. Wellington, E.M.H. (2010). BMC Genomics 11:10. (Highly Accessed Paper)

Alix, K., Joets, J., Ryder, C.D., Moore, J., Barker, G.C., Bailey, J.P., King, G.J. and Heslop-Harrison, J.S. (2008). The Plant Journal 56: 1030-1044. 

R.A. Tyson, D.B.A. Epstein, K.I. Anderson, T. Bretschneider, Math. Model. Nat. Phenom., 5(1):34-55, 2010.

S. Whitelam, T. Bretschneider, N.J. Burroughs, Physical Review Letters, 102:198103, 2009.

L. Bosgraaf, P.J.M. van Haastert, T. Bretschneider, Cell Motility and the Cytoskeleton, 66(3):156-165, 2009. 

T. Bretschneider, K. Anderson, M. Ecke, A. Müller-Taubenberger, B. Schroth-Diez, H.C. Ishikawa-Ankerhold, G. Gerisch, Biophys. J., 96, 2888-2900, 2009.

B. Smith, C. Hill, L. Godfrey, D A Rand, H van den Berg, S Thornton, M Hodgkin, J Davey and G Ladds.  Cellular Signalling 21 (209)1151-1160 doi:10.1016/j.cellsig.2009.03.00

Steven J. Kiddle, Oliver P. F. Windram, Stuart McHattie, Andrew Mead, Jim Beynon, Vicky Buchanan-Wollaston, Katherine J. Denby and Sach Mukherjee (2010) Bioinformatics 26 (3):355

Moore, J.D., Kell, S.P., Iriondo J, Ford-Lloyd, B. and Maxted, N. (2008) CWRML: representing crop wild relative conservation and use data in XML. BMC Bioinformatics 9:116.

J. Dalous, E. Burghardt, A. Müller-Taubenberger, F. Bruckert, G. Gerisch, T. Bretschneider. Reversal of cell polarity and actin-myosin cytoskeleton reorganization under mechanical and chemical stimulation.  Biophys. J., 94(3):1063-1074, 2008.

Moore, J.D. and Allaby, R.G. (2008) TreeMos: a high-throughput phylogenomic approach to find and visualise phylogenetic mosaicism. Bioinformatics 24(5):717-718.

TreeMos is a novel high-throughput graphical analysis application that allows the user to search for phylogenetic mosaicism among one or more DNA or protein sequence multiple alignments and additional unaligned sequences.

R. Pattaranit, , D. Spanswick.  The development of insulin resistance in Type 2 diabetes: insights from knockout studies.  Sci Prog 91 (2008) 285-316.