Browsing by Subject "genetic analysis"
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Item Open Access Heterotrophic ammonium removal by a novel hatchery isolate Acinetobacter calcoaceticus STB1(2012) Sarioglu O.F.; Suluyayla, R.; Tekinay, T.A novel bacterial strain, STB1, was isolated from a commercial sea bass hatchery and found to display high heterotrophic ammonium removal characteristics at different concentrations of ammonium (NH4+-N). The species identity of STB1 was determined via 16S rRNA gene sequence analysis to be Acinetobacter calcoaceticus. We evaluated ammonium removal characteristics of STB1 at varying ammonium concentrations, and observed that STB1 can almost completely remove ammonium at low (50 mg l -1), and medium (100 mg l -1) concentrations within 72 h, while 45% ammonium removal was observed at a higher concentration (210 mg l -1) during the same period. Trace amount of the metabolized ammonium was converted to nitrite or nitrate and 22.16% of total nitrogen was incorporated into cell biomass, while 4.34% of total nitrogen was initially incorporated into cell biomass and subsequently released to the supernatant fraction in the 100 mg l -1 sample. Most of the remaining conversion products are expected to be gaseous denitrification products. Toxicological studies with Artemia salina (brine shrimp) nauplii revealed that STB1 strain is non-toxic to Artemia larvae, which suggests that STB1 can be safely and efficiently utilized in water quality enrichment in aquatic ecosystems. © 2012 Elsevier Ltd.Item Open Access Identification of endogenous reference genes for qRT-PCR analysis in normal matched breast tumor tissues(Cognizant Communication Corporation, 2009) Gur-Dedeoglu, B.; Konu, O.; Bozkurt, B.; Ergul, G.; Seckin, S.; Yulug, I. G.Quantitative gene expression measurements from tumor tissue are frequently compared with matched normal and/or adjacent tumor tissue expression for diagnostic marker gene selection as well as assessment of the degree of transcriptional deregulation in cancer. Selection of an appropriate reference gene (RG) or an RG panel, which varies depending on cancer type, molecular subtypes, and the normal tissues used for interindividual calibration, is crucial for the accurate quantification of gene expression. Several RG panels have been suggested in breast cancer for making comparisons among tumor subtypes, cell lines, and benign/malignant tumors. In this study, expression patterns of 15 widely used endogenous RGs (ACTB, TBP, GAPDH, SDHA, HPRT, HMBS, B2M, PPIA, GUSB, YWHAZ2, PGK1, RPLP0, PUM1, MRPL19, and RPL41), and three candidate genes that were selected through analysis of two independent microarray datasets (IL22RA1, TTC22, ZNF224) were determined in 23 primary breast tumors and their matched normal tissues using qRTPCR. Additionally, 18S rRNA, ACTB, and SDHA were tested using randomly primed cDNAs from 13 breast tumor pairs to assess the rRNA/mRNA ratio. The tumors exhibited significantly lower rRNA/mRNA ratio when compared to their normals, on average. The expression of the studied RGs in breast tumors did not exhibit differences in terms of grade, ER, or PR status. The stability of RGs was examined based on two different statistical models, namely GeNorm and NormFinder. Among the 18 tested endogenous reference genes, ACTB and SDHA were identified as the most suitable reference genes for the normalization of qRTPCR data in the analysis of normal matched tumor breast tissue pairs by both programs. In addition, the expression of the gelsolin (GSN) gene, a well-known downregulated target in breast tumors, was analyzed using the two most suitable genes and different RG combinations to validate their effectiveness as a normalization factor (NF). The GSN expression of the tumors used in this study was significantly lower than that of normals showing the effectivity of using ACTB and SDHA as suitable RGs in this set of tumor–normal tissue panel. The combinational use of the best performing two RGs (ACTB and SDHA) as a normalization factor can be recommended to minimize sample variability and to increase the accuracy and resolution of gene expression normalization in tumor–normal paired breast cancer qRT-PCR studies.Item Open Access Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel(Nature Publishing Group, 2014) Delaneau O.; Marchini J.; McVeanh G.A.; Donnelly P.; Lunter G.; Marchini J.L.; Myers, S.; Gupta-Hinch, A.; Iqbal, Z.; Mathieson I.; Rimmer, A.; Xifara, D.K.; Kerasidou, A.; Churchhouse, C.; Altshuler, D.M.; Gabriel, S.B.; Lander, E.S.; Gupta, N.; Daly, M.J.; DePristo, M.A.; Banks, E.; Bhatia G.; Carneiro, M.O.; Del Angel G.; Genovese G.; Handsaker, R.E.; Hartl, C.; McCarroll, S.A.; Nemesh J.C.; Poplin, R.E.; Schaffner, S.F.; Shakir, K.; Sabeti P.C.; Grossman, S.R.; Tabrizi, S.; Tariyal, R.; Li H.; Reich, D.; Durbin, R.M.; Hurles, M.E.; Balasubramaniam, S.; Burton J.; Danecek P.; Keane, T.M.; Kolb-Kokocinski, A.; McCarthy, S.; Stalker J.; Quail, M.; Ayub Q.; Chen, Y.; Coffey, A.J.; Colonna V.; Huang, N.; Jostins L.; Scally, A.; Walter, K.; Xue, Y.; Zhang, Y.; Blackburne, B.; Lindsay, S.J.; Ning, Z.; Frankish, A.; Harrow J.; Chris, T.-S.; Abecasis G.R.; Kang H.M.; Anderson P.; Blackwell, T.; Busonero F.; Fuchsberger, C.; Jun G.; Maschio, A.; Porcu, E.; Sidore, C.; Tan, A.; Trost, M.K.; Bentley, D.R.; Grocock, R.; Humphray, S.; James, T.; Kingsbury, Z.; Bauer, M.; Cheetham, R.K.; Cox, T.; Eberle, M.; Murray L.; Shaw, R.; Chakravarti, A.; Clark, A.G.; Keinan, A.; Rodriguez-Flores J.L.; De LaVega F.M.; Degenhardt J.; Eichler, E.E.; Flicek P.; Clarke L.; Leinonen, R.; Smith, R.E.; Zheng-Bradley X.; Beal, K.; Cunningham F.; Herrero J.; McLaren W.M.; Ritchie G.R.S.; Barker J.; Kelman G.; Kulesha, E.; Radhakrishnan, R.; Roa, A.; Smirnov, D.; Streeter I.; Toneva I.; Gibbs, R.A.; Dinh H.; Kovar, C.; Lee, S.; Lewis L.; Muzny, D.; Reid J.; Wang, M.; Yu F.; Bainbridge, M.; Challis, D.; Evani, U.S.; Lu J.; Nagaswamy, U.; Sabo, A.; Wang, Y.; Yu J.; Fowler G.; Hale W.; Kalra, D.; Green, E.D.; Knoppers, B.M.; Korbel J.O.; Rausch, T.; Sttz, A.M.; Lee, C.; Griffin L.; Hsieh, C.-H.; Mills, R.E.; Von Grotthuss, M.; Zhang, C.; Shi X.; Lehrach H.; Sudbrak, R.; Amstislavskiy V.S.; Lienhard, M.; Mertes F.; Sultan, M.; Timmermann, B.; Yaspo, M.L.; Herwig, S.R.; Mardis, E.R.; Wilson, R.K.; Fulton L.; Fulton, R.; Weinstock G.M.; Chinwalla, A.; Ding L.; Dooling, D.; Koboldt, D.C.; McLellan, M.D.; Wallis J.W.; Wendl, M.C.; Zhang Q.; Marth G.T.; Garrison, E.P.; Kural, D.; Lee W.-P.; Leong W.F.; Ward, A.N.; Wu J.; Zhang, M.; Nickerson, D.A.; Alkan, C.; Hormozdiari F.; Ko, A.; Sudmant P.H.; Schmidt J.P.; Davies, C.J.; Gollub J.; Webster, T.; Wong, B.; Zhan, Y.; Sherry, S.T.; Xiao, C.; Church, D.; Ananiev V.; Belaia, Z.; Beloslyudtsev, D.; Bouk, N.; Chen, C.; Cohen, R.; Cook, C.; Garner J.; Hefferon, T.; Kimelman, M.; Liu, C.; Lopez J.; Meric P.; Ostapchuk, Y.; Phan L.; Ponomarov, S.; Schneider V.; Shekhtman, E.; Sirotkin, K.; Slotta, D.; Zhang H.; Wang J.; Fang X.; Guo X.; Jian, M.; Jiang H.; Jin X.; Li G.; Li J.; Li, Y.; Liu X.; Lu, Y.; Ma X.; Tai, S.; Tang, M.; Wang, B.; Wang G.; Wu H.; Wu, R.; Yin, Y.; Zhang W.; Zhao J.; Zhao, M.; Zheng X.; Lachlan H.; Fang L.; Li Q.; Li, Z.; Lin H.; Liu, B.; Luo, R.; Shao H.; Wang, B.; Xie, Y.; Ye, C.; Yu, C.; Zheng H.; Zhu H.; Cai H.; Cao H.; Su, Y.; Tian, Z.; Yang H.; Yang L.; Zhu J.; Cai, Z.; Wang J.; Albrecht, M.W.; Borodina, T.A.; Auton, A.; Yoon, S.C.; Lihm J.; Makarov V.; Jin H.; Kim W.; Kim, K.C.; Gottipati, S.; Jones, D.; Cooper, D.N.; Ball, E.V.; Stenson P.D.; Barnes, B.; Kahn, S.; Ye, K.; Batzer, M.A.; Konkel, M.K.; Walker J.A.; MacArthur, D.G.; Lek, M.; Shriver, M.D.; Bustamante, C.D.; Gravel, S.; Kenny, E.E.; Kidd J.M.; Lacroute P.; Maples, B.K.; Moreno-Estrada, A.; Zakharia F.; Henn, B.; Sandoval, K.; Byrnes J.K.; Halperin, E.; Baran, Y.; Craig, D.W.; Christoforides, A.; Izatt, T.; Kurdoglu, A.A.; Sinari, S.A.; Homer, N.; Squire, K.; Sebat J.; Bafna V.; Ye, K.; Burchard, E.G.; Hernandez, R.D.; Gignoux, C.R.; Haussler, D.; Katzman, S.J.; Kent W.J.; Howie, B.; Ruiz-Linares, A.; Dermitzakis, E.T.; Lappalainen, T.; Devine, S.E.; Liu X.; Maroo, A.; Tallon L.J.; Rosenfeld J.A.; Michelson L.P.; Angius, A.; Cucca F.; Sanna, S.; Bigham, A.; Jones, C.; Reinier F.; Li, Y.; Lyons, R.; Schlessinger, D.; Awadalla P.; Hodgkinson, A.; Oleksyk, T.K.; Martinez-Cruzado J.C.; Fu, Y.; Liu X.; Xiong, M.; Jorde L.; Witherspoon, D.; Xing J.; Browning, B.L.; Hajirasouliha I.; Chen, K.; Albers, C.A.; Gerstein, M.B.; Abyzov, A.; Chen J.; Fu, Y.; Habegger L.; Harmanci, A.O.; Mu X.J.; Sisu, C.; Balasubramanian, S.; Jin, M.; Khurana, E.; Clarke, D.; Michaelson J.J.; OSullivan, C.; Barnes, K.C.; Gharani, N.; Toji L.H.; Gerry, N.; Kaye J.S.; Kent, A.; Mathias, R.; Ossorio P.N.; Parker, M.; Rotimi, C.N.; Royal, C.D.; Tishkoff, S.; Via, M.; Bodmer W.; Bedoya G.; Yang G.; You, C.J.; Garcia-Montero, A.; Orfao, A.; Dutil J.; Brooks L.D.; Felsenfeld, A.L.; McEwen J.E.; Clemm, N.C.; Guyer, M.S.; Peterson J.L.; Duncanson, A.; Dunn, M.; Peltonen L.A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved.