Genome-wide BigData analytics: Case of yeast stress signature detection

1. O’Driscoll A, Daugelaite J, Sleator RD. “Big data” Hadoop and cloud computing in genomics. J Biomed Inform 2013; 46(5): 774-781.10.1016/j.jbi.2013.07.001Search in Google Scholar

2. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, Springer, New York, 2016.Search in Google Scholar

3. Efron B, Hastie T. Computer Age Inference: Algorithms, Evidence, and Data Science, Cambridge University Press, New York, 2016.Search in Google Scholar

4. Prajapati V, Big Data Analytics With R and Hadoop, Packt Publishing Limited, Birmigham, UK, 2013.Search in Google Scholar

5. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Austria, 2017, URL https://www.R-project.org/.Search in Google Scholar

6. Chen H, Chiang RHL, Storey VC. Business intelligence and analytics: From big data to big impact. MIS Managment Information Systems Q 2012; 36(4): 1165-1188.10.2307/41703503Search in Google Scholar

7. Robinson SW, Fernandes M, Husi H. Current advances in systems and integrative biology. CSBJ, Computational and Structural Biotechnology Journal 2014; 11(18): 35-46.10.1016/j.csbj.2014.08.007Search in Google Scholar

8. Clare A, “Machine learning and data mining for yeast functional genomics”, PhD Thesis, 2003, University of Wales, Aberystwyth, UK.Search in Google Scholar

9. Huttenhower C., Mutungu K.M., Indik N., Yang W., Schroeder M., Forman J.J., Troyanskaya O.G., Coller H. Detailing regulatory networks through large scale data integration. Bioinformatics 2009; 25(24): 3267-3274.10.1093/bioinformatics/btp588Open DOISearch in Google Scholar

10. Taymaz-Nikerel H, Cankorur-Cetinkaya A, Kirdar B. Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations. Front Bioeng Biotechnol 2016; 4(17)10.3389/fbioe.2016.00017Search in Google Scholar

11. Goncalves E, Nakic ZR, Zampieri M, Wagih O, Ochoa D, Sauer U, Beltrao P, Saez Rodriguez J. Systemic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast. PLOS Computational Biology 2017; 13(1)10.1371/journal.pcbi.1005297Search in Google Scholar

12. Brauer MJ, Huttenohwer C, Airoldi M, Rosenstein R, Matese C, Gresham D, Boer VM, Troyanskaya OG, Botstein F. Coordination of Growth Rate, Cell Cycle, Stress Response and Metabolic Activity in Yeast. MBoC, Molecular Biology of the Cell 2008; 19: 352-367.10.1091/mbc.e07-08-0779Search in Google Scholar

13. van Dijken JP et al. An interlaboratory comparison of physiological and genetic properties of four Saccharomyces cerevisiae strains. EMT, Enzyme Microb Technol 2000; 26(9-10): 706-714.10.1016/S0141-0229(00)00162-9Search in Google Scholar

14. Funspec, Yeast Data Base, http://funspec.med.utoronto.ca/Search in Google Scholar

15. Liaw A, Wiener M, Classification and Regression by random Forest. R News 2002;. 2(3); 18-22.Search in Google Scholar

16. Chen T, Tong H, Benesty M, Khotilovich V. ,Yuan Tang (2017). xgboost: Extreme Gradient Boosting. https://CRAN.Rproject.org/package=xgboostSearch in Google Scholar

17. Simon N, Friedman J, Hastie T, Tibshirani R, Journal of Statistical Software, 2011, 39(5), 1-13. URL http://www.jstatsoft.org/v39/i05/.10.18637/jss.v039.i05482440827065756Search in Google Scholar

18. Meinshausen N., Quantile Regression Forests, 2016; https://CRAN.R-project.org/package=quantregForestSearch in Google Scholar

19. McGill R, Tukey JW, Larsen WA. Variations of Box Plots, AM STAT. The American Statistician 1978; (32): 12-16.10.1080/00031305.1978.10479236Search in Google Scholar

20. Gregory R. Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, Lumley T, Maechler M, Magnusson R, Moeller S, Schwartz M, Venables B, 2016, URL https://CRAN.R-project.org/package=gplotsSearch in Google Scholar

21. Zhang J, Vemuri G, Nielsen J, Systems biology of energy homeostasis in yeast, Curr Opin Microbiol 2010; 13(3); 382-388.10.1016/j.mib.2010.04.00420439164Open DOISearch in Google Scholar

22. Hayat S, Hayat Q, Alyemeni MN, Wani AS, Pichtel J, Ahmad A. Role of proline under changing environment, Plant Signal Behav 2012; 7(11); 1456-1466.10.4161/psb.21949354887122951402Search in Google Scholar

23. Liang X, Zhang L, Natarajan SK, Becker DF, Proline mechanism of stress survival. Antioxid Redox Signal 2013; 19(9); 998-1011.10.1089/ars.2012.5074376322323581681Search in Google Scholar

24. Morosan M, Al Hassan M, Naranjo MA, López-Gresa MP, Boscaiu M, Vicente O. Comparative analysis of drought responses in Phaseolus vulgaris (common bean) and P. coccineus (runner bean) cultivars The EuroBiotech Journal 2017; 1(3); 247-253.10.24190/ISSN2564-615X/2017/03.09Search in Google Scholar

25. Liu W, Phang JM, Proline dehydrogenase (oxidase) in cancer. Biofactors 2012 ; 38(6): 398-406.10.1002/biof.1036747954122886911Open DOISearch in Google Scholar

26. Shima J, Takagi H, A New Simple Method for Isolating Multistress- Tolerant Semidominant Mutants of Saccharomyces cerevisiae by One-Step Selection under Lethal Hydrogen Peroxide Stress Condition; Biotechnol Appl Biochem 2009; 53; 155-164.Search in Google Scholar

27. Kaino T, Takagi H. Proline as a Stress Protectant in the Yeast Saccharomyces cerevisiae, Biosci Biotechnol Biochem 2009: 73(9); 2131-2135.10.1271/bbb.9029919734662Search in Google Scholar

28. Tsolmonbaatar A, Hashida K, Sugimoto Y, Furukawa S, Takagi H. Isolation of baker’s yeast mutants with proline accumulation that showed enhanced tolerance to baking associated stresses, Int J Food Microbiol 2016; 238; 233-240.10.1016/j.ijfoodmicro.2016.09.01527672730Search in Google Scholar

29. Phang JM, Pandhare J, Liu Y, The metabolism of proline as microenvironmental stress substrate, J Nutr 2008; 138(10); 2008S-2015S.10.1093/jn/138.10.2008S269227618806116Search in Google Scholar