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1. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Author

Su Zhenqiang, Shippy Richard, Puri Raj K, Peterson Ron L, Mei Nan, Ma Yunqing, Luo Yuling, Li Quan-Zhen, Kawasaki Ernest S, Hong Huixiao, Herman Damir, Han Jing, Guo Xu, Fuscoe James C, Frueh Felix W, Fan Xiao-hui, Collins Patrick J, Chu Tzu-Ming, Bertholet Vincent, Cao Xiaoxi, Bao Wenjun, Barbacioru Catalin C, Amur Shashi, Qian Feng, Fang Hong, Boysen Cecilie, Croner Lisa J, Guo Lei, Goodsaid Federico M, Perkins Roger G, Harris Stephen C, Jensen Roderick V, Jones Wendell D, Shi Leming, Sun Yongming, Sun Hongmei, Thorn Brett, Turpaz Yaron, Wang Charles, Wang Sue, Warrington Janet A, Willey James C, Wu Jie, Xie Qian, Zhang Liang, Zhang Lu, Zhong Sheng, Wolfinger Russell D, and Tong Weida

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. Results Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan – the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. Conclusion We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

Published
2008
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2. Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations

Author

Kaysser-Kranich Tamma, Palaniappan Chockalingam, Lockner Randall, Sendera Timothy J, Shippy Richard, Watts George, and Alsobrook John

Subjects
Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations. Results In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips® and Amersham CodeLink™ UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively. Conclusions As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.

Published
2004
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4. Using RNA sample titrations to assess microarray platform performance and normalization techniques

Author

Shippy, Richard, Fulmer-Smentek, Stephanie, Jensen, Roderick V, Jones, Wendell D, Wolber, Paul K, Johnson, Charles D, Pine, P Scott, Boysen, Cecilie, Guo, Xu, Chudin, Eugene, Sun, Yongming Andrew, Willey, James C, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Setterquist, Robert A, Wilson, Mike, Lucas, Anne Bergstrom, Novoradovskaya, Natalia, Papallo, Adam, Turpaz, Yaron, Baker, Shawn C, Warrington, Janet A, Shi, Leming, and Herman, Damir

Published
2006
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9. Mutational analysis of loops 1 and 5 of the hairpin ribozyme

Author

Shippy, Richard, Siwkowski, Andrew, and Hampel, Arnold

Subjects
Catalytic RNA -- Research, Gene mutations -- Research, Scission (Chemistry) -- Research, Biological sciences, Chemistry
Abstract

Research was conducted to examine the mutational analysis of loops 1 and 5 of the hairpin ribozyme. The experiment was performed using a cis-ribozyme tethered to substrate by a pentapyrimidine loop. Ribozyme-substrate molecules were mutated and catalytic activity was measured for every mutant. A comparison of results with the activity of the original native sequence revealed several effects including positions that led to cleavage rates that are lower than the limit of accurate quantification of all non-wild-type base substitutions.

Published
1998

10. Analysis of hairpin ribozyme base mutations in loops 2 and 4 and their effects on cis-cleavage in vitro

Author

Siwkowski, Andrew, Shippy, Richard, and Hampel, Arnold

Subjects
Catalytic RNA -- Analysis, DNA damage -- Analysis, Biochemical genetics -- Research, Amino acid sequence -- Analysis, Biological sciences, Chemistry
Abstract

The direct point mutation approach was utilized to determine the effects of comprehensive base mutations in the loops two and four of the wild type sequence of the hairpin ribozyme. Extensive base mutations of the wild type sequence of the hairpin ribozyme indicated the presence of a non-canonical A26:G36 base pair in the catalytic conformation of the hairpin ribozyme. Data from the direct point mutation approach were also utilized to develop a two dimension model of the hairpin ribozyme which was composed of four helices that were interspersed by five single strands.

Published
1997

12. The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies

Author

Shi, Leming, Jones, Wendell, Jensen, Roderick, Harris, Stephen, Perkins, Roger, Goodsaid, Federico, Guo, Lei, Croner, Lisa, Boysen, Cecilie, Fang, Hong, Amur, Shashi, Bao, Wenjun, Barbacioru, Catalin, Bertholet, Vincent, Cao, Xiaoxi Megan, Chu, Tzu-Ming, Collins, Patrick, Fan, Xiao-hui, Frueh, Felix, Fuscoe, James, Guo, Xu, Han, Jing, Herman, Damir, Hong, Huixiao, Kawasaki, Ernest, Li, Quan-Zhen, Luo, Yuling, Ma, Yunqing, Mei, Nan, Peterson, Ron, Puri, Raj, Qian, Feng, Shippy, Richard, Su, Zhenqiang, Sun, Yongming Andrew, Sun, Hongmei, Thorn, Brett, Turpaz, Yaron, Wang, Charles, Wang, Sue-Jane, Warrington, Janet, Willey, James, Wu, Jie, Xie, Qian, Zhang, Liang, Zhang, Lu, Zhong, Sheng, Chen, James, Wolfinger, Russell, and Tong, Weida

Published
2007
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13. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Author

Shi, Leming, Jones, Wendell D., Jensen, Roderick V., Harris, Stephen C., Perkins, Roger G., Goodsaid, Federico M., Guo, Lei, Croner, Lisa J., Boysen, Cecilie, Fang, Hong, Qian, Feng, Amur, Shashi, Bao, Wenjun, Barbacioru, Catalin C., Bertholet, Vincent, Cao, Xiaoxi M., Chu, Tzu-Ming, Collins, Patrick J., Fan, Xiao-hui, Frueh, Felix W., Fuscoe, James C., Guo, Xu, Han, Jing, Herman, Damir, Hong, Huixiao, Kawasaki, Ernest S., Li, Quan-Zhen, Luo, Yuling, Ma, Yunqing, Mei, Nan, Peterson, Ron L., Puri, Raj K., Shippy, Richard, Su, Zhenqiang, Sun, Yongming A., Sun, Hongmei, Thorn, Brett, Turpaz, Yaron, Wang, Charles, Wang, Sue J., Warrington, Janet A., Willey, James C., Wu, Jie, Xie, Qian, Zhang, Liang, Zhang, Lu, Zhong, Sheng, Wolfinger, Russell D., Tong, Weida, and Biological Sciences

Subjects
genetic structures
Abstract

Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. Results Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. Conclusion We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity. Published version

Published
2008

14. The External RNA Controls Consortium: a progress report

Author

Baker, Shawn C, Bauer, Steven R, Beyer, Richard P, Brenton, James D, Bromley, Bud, Burrill, John, Causton, Helen, Conley, Michael P, Elespuru, Rosalie, Fero, Michael, Foy, Carole, Fuscoe, James, Gao, Xiaolian, Gerhold, David Lee, Gilles, Patrick, Goodsaid, Federico, Guo, Xu, Hackett, Joe, Hockett, Richard D, Ikonomi, Pranvera, Irizarry, Rafael A, Kawasaki, Ernest S, Kaysser-Kranich, Tamma, Kerr, Kathleen, Kiser, Gretchen, Koch, Walter H, Lee, Kathy Y, Liu, Chunmei, Liu, Z Lewis, Lucas, Anne, Manohar, Chitra F, Miyada, Garry, Modrusan, Zora, Parkes, Helen, Puri, Raj K, Reid, Laura, Ryder, Thomas B, Salit, Marc, Samaha, Raymond R, Scherf, Uwe, Sendera, Timothy J, Setterquist, Robert A, Shi, Leming, Shippy, Richard, Soriano, Jesus V, Wagar, Elizabeth A, Warrington, Janet A, Williams, Mickey, Wilmer, Frederike, Wilson, Mike, Wolber, Paul K, Wu, Xiaoning, and Zadro, Renata

Subjects
external RNA controls
Abstract

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

Published
2005

15. Design, Targeting, and Initial Screening of sTRSV- Derived Hairpin Ribozymes for Optimum Helix 1 Length and Catalytic Efficiency In Vitro

Author

Richardson, Max W., primary, Hostalek, Linda, additional, Michellebson, Michellebson, additional, Hu, Jason, additional, Shippy, Richard, additional, Siwkowski, Andrew, additional, Marmur, Jonathan D., additional, Khalili, Kamel, additional, Klotman, Paul E., additional, Hampel, Arnold, additional, and Rappaport, Jay, additional

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16. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Author

Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjö, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjö, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, and Wolfinger, Russell D

Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Published
2010
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17. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Author

DNAVision SA, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjo, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, MAQC Consortium, DNAVision SA, Shi, Leming, Campbell, Gregory, Jones, Wendell D, Campagne, Fabien, Wen, Zhining, Walker, Stephen J, Su, Zhenqiang, Chu, Tzu-Ming, Goodsaid, Federico M, Pusztai, Lajos, Shaughnessy, John D, Oberthuer, André, Thomas, Russell S, Paules, Richard S, Fielden, Mark, Barlogie, Bart, Chen, Weijie, Du, Pan, Fischer, Matthias, Furlanello, Cesare, Gallas, Brandon D, Ge, Xijin, Megherbi, Dalila B, Symmans, W Fraser, Wang, May D, Zhang, John, Bitter, Hans, Brors, Benedikt, Bushel, Pierre R, Bylesjo, Max, Chen, Minjun, Cheng, Jie, Cheng, Jing, Chou, Jeff, Davison, Timothy S, Delorenzi, Mauro, Deng, Youping, Devanarayan, Viswanath, Dix, David J, Dopazo, Joaquin, Dorff, Kevin C, Elloumi, Fathi, Fan, Jianqing, Fan, Shicai, Fan, Xiaohui, Fang, Hong, Gonzaludo, Nina, Hess, Kenneth R, Hong, Huixiao, Huan, Jun, Irizarry, Rafael A, Judson, Richard, Juraeva, Dilafruz, Lababidi, Samir, Lambert, Christophe G, Li, Li, Li, Yanen, Li, Zhen, Lin, Simon M, Liu, Guozhen, Lobenhofer, Edward K, Luo, Jun, Luo, Wen, McCall, Matthew N, Nikolsky, Yuri, Pennello, Gene A, Perkins, Roger G, Philip, Reena, Popovici, Vlad, Price, Nathan D, Qian, Feng, Scherer, Andreas, Shi, Tieliu, Shi, Weiwei, Sung, Jaeyun, Thierry-Mieg, Danielle, Thierry-Mieg, Jean, Thodima, Venkata, Trygg, Johan, Vishnuvajjala, Lakshmi, Wang, Sue Jane, Wu, Jianping, Wu, Yichao, Xie, Qian, Yousef, Waleed A, Zhang, Liang, Zhang, Xuegong, Zhong, Sheng, Zhou, Yiming, Zhu, Sheng, Arasappan, Dhivya, Bao, Wenjun, Lucas, Anne Bergstrom, Berthold, Frank, Brennan, Richard J, Buness, Andreas, Catalano, Jennifer G, Chang, Chang, Chen, Rong, Cheng, Yiyu, Cui, Jian, Czika, Wendy, Demichelis, Francesca, Deng, Xutao, Dosymbekov, Damir, Eils, Roland, Feng, Yang, Fostel, Jennifer, Fulmer-Smentek, Stephanie, Fuscoe, James C, Gatto, Laurent, Ge, Weigong, Goldstein, Darlene R, Guo, Li, Halbert, Donald N, Han, Jing, Harris, Stephen C, Hatzis, Christos, Herman, Damir, Huang, Jianping, Jensen, Roderick V, Jiang, Rui, Johnson, Charles D, Jurman, Giuseppe, Kahlert, Yvonne, Khuder, Sadik A, Kohl, Matthias, Li, Jianying, Li, Menglong, Li, Quan-Zhen, Li, Shao, Li, Zhiguang, Liu, Jie, Liu, Ying, Liu, Zhichao, Meng, Lu, Madera, Manuel, Martinez-Murillo, Francisco, Medina, Ignacio, Meehan, Joseph, Miclaus, Kelci, Moffitt, Richard A, Montaner, David, Mukherjee, Piali, Mulligan, George J, Neville, Padraic, Nikolskaya, Tatiana, Ning, Baitang, Page, Grier P, Parker, Joel, Parry, R Mitchell, Peng, Xuejun, Peterson, Ron L, Phan, John H, Quanz, Brian, Ren, Yi, Riccadonna, Samantha, Roter, Alan H, Samuelson, Frank W, Schumacher, Martin M, Shambaugh, Joseph D, Shi, Qiang, Shippy, Richard, Si, Shengzhu, Smalter, Aaron, Sotiriou, Christos, Soukup, Mat, Staedtler, Frank, Steiner, Guido, Stokes, Todd H, Sun, Qinglan, Tan, Pei-Yi, Tang, Rong, Tezak, Zivana, Thorn, Brett, Tsyganova, Marina, Turpaz, Yaron, Vega, Silvia C, Visintainer, Roberto, von Frese, Juergen, Wang, Charles, Wang, Eric, Wang, Junwei, Wang, Wei, Westermann, Frank, Willey, James C, Woods, Matthew, Wu, Shujian, Xiao, Nianqing, Xu, Joshua, Xu, Lei, Yang, Lun, Zeng, Xiao, Zhang, Jialu, Zhang, Li, Zhang, Min, Zhao, Chen, Puri, Raj K, Scherf, Uwe, Tong, Weida, Wolfinger, Russell D, and MAQC Consortium

Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Published
2010

18. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Author

Biological Sciences, Shi, Leming, Jones, Wendell D., Jensen, Roderick V., Harris, Stephen C., Perkins, Roger G., Goodsaid, Federico M., Guo, Lei, Croner, Lisa J., Boysen, Cecilie, Fang, Hong, Qian, Feng, Amur, Shashi, Bao, Wenjun, Barbacioru, Catalin C., Bertholet, Vincent, Cao, Xiaoxi M., Chu, Tzu-Ming, Collins, Patrick J., Fan, Xiao-hui, Frueh, Felix W., Fuscoe, James C., Guo, Xu, Han, Jing, Herman, Damir, Hong, Huixiao, Kawasaki, Ernest S., Li, Quan-Zhen, Luo, Yuling, Ma, Yunqing, Mei, Nan, Peterson, Ron L., Puri, Raj K., Shippy, Richard, Su, Zhenqiang, Sun, Yongming A., Sun, Hongmei, Thorn, Brett, Turpaz, Yaron, Wang, Charles, Wang, Sue J., Warrington, Janet A., Willey, James C., Wu, Jie, Xie, Qian, Zhang, Liang, Zhang, Lu, Zhong, Sheng, Wolfinger, Russell D., Tong, Weida, Biological Sciences, Shi, Leming, Jones, Wendell D., Jensen, Roderick V., Harris, Stephen C., Perkins, Roger G., Goodsaid, Federico M., Guo, Lei, Croner, Lisa J., Boysen, Cecilie, Fang, Hong, Qian, Feng, Amur, Shashi, Bao, Wenjun, Barbacioru, Catalin C., Bertholet, Vincent, Cao, Xiaoxi M., Chu, Tzu-Ming, Collins, Patrick J., Fan, Xiao-hui, Frueh, Felix W., Fuscoe, James C., Guo, Xu, Han, Jing, Herman, Damir, Hong, Huixiao, Kawasaki, Ernest S., Li, Quan-Zhen, Luo, Yuling, Ma, Yunqing, Mei, Nan, Peterson, Ron L., Puri, Raj K., Shippy, Richard, Su, Zhenqiang, Sun, Yongming A., Sun, Hongmei, Thorn, Brett, Turpaz, Yaron, Wang, Charles, Wang, Sue J., Warrington, Janet A., Willey, James C., Wu, Jie, Xie, Qian, Zhang, Liang, Zhang, Lu, Zhong, Sheng, Wolfinger, Russell D., and Tong, Weida

Abstract

Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. Results Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. Conclusion We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more repro

Published
2008

19. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

Author

Shi, Leming, primary, Jones, Wendell D, additional, Jensen, Roderick V, additional, Harris, Stephen C, additional, Perkins, Roger G, additional, Goodsaid, Federico M, additional, Guo, Lei, additional, Croner, Lisa J, additional, Boysen, Cecilie, additional, Fang, Hong, additional, Qian, Feng, additional, Amur, Shashi, additional, Bao, Wenjun, additional, Barbacioru, Catalin C, additional, Bertholet, Vincent, additional, Cao, Xiaoxi Megan, additional, Chu, Tzu-Ming, additional, Collins, Patrick J, additional, Fan, Xiao-hui, additional, Frueh, Felix W, additional, Fuscoe, James C, additional, Guo, Xu, additional, Han, Jing, additional, Herman, Damir, additional, Hong, Huixiao, additional, Kawasaki, Ernest S, additional, Li, Quan-Zhen, additional, Luo, Yuling, additional, Ma, Yunqing, additional, Mei, Nan, additional, Peterson, Ron L, additional, Puri, Raj K, additional, Shippy, Richard, additional, Su, Zhenqiang, additional, Sun, Yongming Andrew, additional, Sun, Hongmei, additional, Thorn, Brett, additional, Turpaz, Yaron, additional, Wang, Charles, additional, Wang, Sue Jane, additional, Warrington, Janet A, additional, Willey, James C, additional, Wu, Jie, additional, Xie, Qian, additional, Zhang, Liang, additional, Zhang, Lu, additional, Zhong, Sheng, additional, Wolfinger, Russell D, additional, and Tong, Weida, additional

Published
2008
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21. Design, Targeting, and Initial Screening of sTRSV-Derived Hairpin Ribozymes for Optimum Helix 1 Length and Catalytic Efficiency In Vitro.

Author

Walker, John M., Sioud, Mouldy, Richardson, Max W., Hostalek, Linda, Dobson, Michelle, Hu, Jason, Shippy, Richard, Siwkowski, Andrew, Marmur, Jonathan D., Khalili, Kamel, Klotman, Paul E., Hampel, Arnold, and Rappaport, Jay

Abstract

Hairpin ribozymes derived from the negative strand of satellite RNAs from the tobacco ringspot virus (sTRSV) can be engineered to target and cleave a variety of heterologous RNAs from both cellular and viral transcripts. Attention to design and targeting rules and optimization of helix 1 length and catalytic efficiency in vitro may increase the efficacy of hairpin ribozymes in reducing the expression of targeted transcripts. Here, principles for the design and targeting of sTRSV-derived hairpin ribozymes are described, as well as methods and materials for optimizing helix 1 length, and for conducting an initial screen of catalytic efficiency to identify promising candidates for further evaluation. Examples are provided for hairpin ribozymes that target human and mouse transforming growth-factor beta (TGF-β), as well as human polycystic kidney disease gene 1 (PKD1) and JC virus large T-antigen. The tetraloop modification of the sTRSV hairpin ribozyme is considered superior to designs based on the native sTRSV hairpin ribozyme, given its potential to yeild considerable improvements in stability and catalytic efficiency. [ABSTRACT FROM AUTHOR]

Published
2004
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22. Evaluation of external RNA controls for the assessment of microarray performance.

Author

Weida Tong, Lucas, Anne Bergstrom, Shippy, Richard, Xiaohui Fan, Hong Fang, Huixiao Hong, Orr, Michael S., Tzu-Ming Chu, Xu Guo, Collins, Patrick J., Sun, Yongming Andrew, Sue-Jane Wang, Wenjun Bao, Wolfinger, Russell D., Shchegrova, Svetlana, Lei Guo, Warrington, Janet A., and Leming Shi

Subjects
RNA, NUCLEIC acids, DNA microarrays, MOLECULAR genetics, GENE expression
Abstract

External RNA controls (ERCs), although important for microarray assay performance assessment, have yet to be fully implemented in the research community. As part of the MicroArray Quality Control (MAQC) study, two types of ERCs were implemented and evaluated; one was added to the total RNA in the samples before amplification and labeling; the other was added to the copyRNAs (cRNAs) before hybridization. ERC concentration-response curves were used across multiple commercial microarray platforms to identify problematic assays and potential sources of variation in the analytical process. In addition, the behavior of different ERC types was investigated, resulting in several important observations, such as the sample-dependent attributes of performance and the potential of using these control RNAs in a combinatorial fashion. This multiplatform investigation of the behavior and utility of ERCs provides a basis for articulating specific recommendations for their future use in evaluating assay performance across multiple platforms. [ABSTRACT FROM AUTHOR]

Published
2006
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23. Rat toxicogenomic study reveals analytical consistency across microarray platforms.

Author

Lei Guo, Lobenhofer, Edward K., Wang, Charles, Shippy, Richard, Harris, Stephen C., Lu Zhang, Nan Mei, Tao Chen, Herman, Damir, Goodsaid, Federico M., Hurban, Patrick, Phillips, Kenneth L., Jun Xu, Xutao Deng, Sun, Yongming Andrew, Weida Tong, Yvonne P.Dragan, and Leming Shi

Subjects
RATS, RNA, DNA microarrays, MOLECULAR genetics, GENE expression, GENETIC toxicology
Abstract

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed. [ABSTRACT FROM AUTHOR]

Published
2006
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24. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

Author

Shi, Leming, Reid, Laura H., Jones, Wendell D., Shippy, Richard, Warrington, Janet A., Baker, Shawn C., Collins, Patrick J., de Longueville, Francoise, Kawasaki, Ernest S., Kathleen Y.Lee, Yuling Luo, Sun, Yongming Andrew, Willey, James C., Setterquist, Robert A., Fischer, Gavin M., Tong, Weida, Dragan, Yvonne P., Dix, David J., Frueh, Felix W., and Goodsaid, Federico M.

Subjects
GENE expression, GENETIC regulation, DNA microarrays, RNA, MOLECULAR genetics, DATA analysis, EXPERIMENTAL design
Abstract

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings. [ABSTRACT FROM AUTHOR]

Published
2006
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25. The hairpin ribozyme.

Author

Shippy, Richard, Lockner, Randy, Farnsworth, Margaret, and Hampel, Arnold

Abstract

The hairpin ribozyme is a member of a family of small RNA endonucleases, which includes hammerhead, human hepatitis delta virus, Neurospora VS, and the lead-dependent catalytic RNAs. All these catalytic RNAs reversibly cleave the phosphodiester bond of substrate RNA to generate 5′-hydroxyl and 2′,3′-cyclic phosphate termini. Whereas the reaction products from family members are similar, large structural and mechanistic differences exist. Structurally the hairpin ribozyme has two principal domains that interact to facilitate catalysis. The hairpin ribozyme uses a catalytic mechanism that does not require metals for cleavage or ligation of substrate RNA. In this regard it is presently unique among RNA catalysts. Targeting rules for cleavage of substrate have been determined and required bases for catalysis have been identified. The hairpin ribozyme has been developed and used for gene therapy and was the first ribozyme to be approved for human clinical trials. [ABSTRACT FROM AUTHOR]

Published
1999
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26. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Author

Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ, Su Z, Chu TM, Goodsaid FM, Pusztai L, Shaughnessy JD Jr, Oberthuer A, Thomas RS, Paules RS, Fielden M, Barlogie B, Chen W, Du P, Fischer M, Furlanello C, Gallas BD, Ge X, Megherbi DB, Symmans WF, Wang MD, Zhang J, Bitter H, Brors B, Bushel PR, Bylesjo M, Chen M, Cheng J, Cheng J, Chou J, Davison TS, Delorenzi M, Deng Y, Devanarayan V, Dix DJ, Dopazo J, Dorff KC, Elloumi F, Fan J, Fan S, Fan X, Fang H, Gonzaludo N, Hess KR, Hong H, Huan J, Irizarry RA, Judson R, Juraeva D, Lababidi S, Lambert CG, Li L, Li Y, Li Z, Lin SM, Liu G, Lobenhofer EK, Luo J, Luo W, McCall MN, Nikolsky Y, Pennello GA, Perkins RG, Philip R, Popovici V, Price ND, Qian F, Scherer A, Shi T, Shi W, Sung J, Thierry-Mieg D, Thierry-Mieg J, Thodima V, Trygg J, Vishnuvajjala L, Wang SJ, Wu J, Wu Y, Xie Q, Yousef WA, Zhang L, Zhang X, Zhong S, Zhou Y, Zhu S, Arasappan D, Bao W, Lucas AB, Berthold F, Brennan RJ, Buness A, Catalano JG, Chang C, Chen R, Cheng Y, Cui J, Czika W, Demichelis F, Deng X, Dosymbekov D, Eils R, Feng Y, Fostel J, Fulmer-Smentek S, Fuscoe JC, Gatto L, Ge W, Goldstein DR, Guo L, Halbert DN, Han J, Harris SC, Hatzis C, Herman D, Huang J, Jensen RV, Jiang R, Johnson CD, Jurman G, Kahlert Y, Khuder SA, Kohl M, Li J, Li L, Li M, Li QZ, Li S, Li Z, Liu J, Liu Y, Liu Z, Meng L, Madera M, Martinez-Murillo F, Medina I, Meehan J, Miclaus K, Moffitt RA, Montaner D, Mukherjee P, Mulligan GJ, Neville P, Nikolskaya T, Ning B, Page GP, Parker J, Parry RM, Peng X, Peterson RL, Phan JH, Quanz B, Ren Y, Riccadonna S, Roter AH, Samuelson FW, Schumacher MM, Shambaugh JD, Shi Q, Shippy R, Si S, Smalter A, Sotiriou C, Soukup M, Staedtler F, Steiner G, Stokes TH, Sun Q, Tan PY, Tang R, Tezak Z, Thorn B, Tsyganova M, Turpaz Y, Vega SC, Visintainer R, von Frese J, Wang C, Wang E, Wang J, Wang W, Westermann F, Willey JC, Woods M, Wu S, Xiao N, Xu J, Xu L, Yang L, Zeng X, Zhang J, Zhang L, Zhang M, Zhao C, Puri RK, Scherf U, Tong W, and Wolfinger RD

Subjects
Animals, Breast Neoplasms diagnosis, Breast Neoplasms genetics, Disease Models, Animal, Female, Gene Expression Profiling methods, Gene Expression Profiling standards, Guidelines as Topic, Humans, Liver Diseases etiology, Liver Diseases pathology, Lung Diseases etiology, Lung Diseases pathology, Multiple Myeloma diagnosis, Multiple Myeloma genetics, Neoplasms diagnosis, Neuroblastoma diagnosis, Neuroblastoma genetics, Predictive Value of Tests, Quality Control, Rats, Survival Analysis, Liver Diseases genetics, Lung Diseases genetics, Neoplasms genetics, Neoplasms mortality, Oligonucleotide Array Sequence Analysis methods, Oligonucleotide Array Sequence Analysis standards
Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Published
2010
Full Text
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27. The External RNA Controls Consortium: a progress report.

Author

Baker SC, Bauer SR, Beyer RP, Brenton JD, Bromley B, Burrill J, Causton H, Conley MP, Elespuru R, Fero M, Foy C, Fuscoe J, Gao X, Gerhold DL, Gilles P, Goodsaid F, Guo X, Hackett J, Hockett RD, Ikonomi P, Irizarry RA, Kawasaki ES, Kaysser-Kranich T, Kerr K, Kiser G, Koch WH, Lee KY, Liu C, Liu ZL, Lucas A, Manohar CF, Miyada G, Modrusan Z, Parkes H, Puri RK, Reid L, Ryder TB, Salit M, Samaha RR, Scherf U, Sendera TJ, Setterquist RA, Shi L, Shippy R, Soriano JV, Wagar EA, Warrington JA, Williams M, Wilmer F, Wilson M, Wolber PK, Wu X, and Zadro R

Subjects
Animals, Guidelines as Topic, Humans, Mice, Quality Control, Rats, Gene Expression Profiling standards, Oligonucleotide Array Sequence Analysis standards, RNA, Messenger analysis
Abstract

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

Published
2005
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28. Design, targeting, and initial screening of sTRSV-derived hairpin ribozymes for optimum helix 1 length and catalytic efficiency in vitro.

Author

Richardson MW, Hostalek L, Dobson M, Hu J, Shippy R, Siwkowski A, Marmur JD, Khalili K, Klotman PE, Hampel A, and Rappaport J

Subjects
Base Sequence, Catalysis, Genetic Engineering methods, Genetic Markers, Humans, Molecular Sequence Data, Nucleic Acid Conformation, RNA, Catalytic chemistry, RNA, Catalytic genetics, RNA, Viral chemistry, RNA, Viral genetics, Substrate Specificity, Templates, Genetic, Transcription, Genetic, Transforming Growth Factor beta genetics, Nepovirus genetics, RNA, Catalytic metabolism, RNA, Viral metabolism
Abstract

Hairpin ribozymes derived from the negative strand of satellite RNAs from the tobacco ringspot virus (sTRSV) can be engineered to target and cleave a variety of heterologous RNAs from both cellular and viral transcripts. Attention to design and targeting rules and optimization of helix 1 length and catalytic efficiency in vitro may increase the efficacy of hairpin ribozymes in reducing the expression of targeted transcripts. Here, principles for the design and targeting of sTRSV-derived hairpin ribozymes are described, as well as methods and materials for optimizing helix 1 length, and for conducting an initial screen of catalytic efficiency to identify promising candidates for further evaluation. Examples are provided for hairpin ribozymes that target human and mouse transforming growth-factor beta (TGF-beta), as well as human polycystic kidney disease gene 1 (PKD1) and JC virus large T-antigen. The tetraloop modification of the sTRSV hairpin ribozyme is considered superior to designs based on the native sTRSV hairpin ribozyme, given its potential to yield considerable improvements in stability and catalytic efficiency.

Published
2004
Full Text
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