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27 results on '"Haudenschild, Christian D."'

5. BAP1 loss defines a new class of renal cell carcinoma

Author

Peña-Llopis, Samuel, Vega-Rubín-de-Celis, Silvia, Liao, Arnold, Leng, Nan, Pavía-Jiménez, Andrea, Wang, Shanshan, Yamasaki, Toshinari, Zhrebker, Leah, Sivanand, Sharanya, Spence, Patrick, Kinch, Lisa, Hambuch, Tina, Jain, Suneer, Lotan, Yair, Margulis, Vitaly, Sagalowsky, Arthur I, Summerour, Pia Banerji, Kabbani, Wareef, Wong, S W Wendy, Grishin, Nick, Laurent, Marc, Xie, Xian-Jin, Haudenschild, Christian D, Ross, Mark T, Bentley, David R, Kapur, Payal, and Brugarolas, James

Published
2012
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6. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning

Author

Cokus, Shawn J., Feng, Suhua, Zhang, Xiaoyu, Chen, Zugen, Merriman, Barry, Haudenschild, Christian D., Pradhan, Sriharsa, Nelson, Stanley F., Pellegrini, Matteo, and Jacobsen, Steven E.

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Cytosine DNA methylation is important in regulating gene expression and in silencing transposons and other repetitive sequences (1,2). Recent genomic studies in Arabidopsis thaliana have revealed that many endogenous genes [...]

Published
2008

7. A comprehensive catalogue of somatic mutations from a human cancer genome

Author

Pleasance, Erin D., Cheetham, Keira R., Stephens, Philip J., McBride, David J., Humphray, Sean J., Greenman, Chris D., Varela, Ignacio, Lin, Meng-Lay, Ordóñez, Gonzalo R., Bignell, Graham R., Ye, Kai, Alipaz, Julie, Bauer, Markus J., Beare, David, Butler, Adam, Carter, Richard J., Chen, Lina, Cox, Anthony J., Edkins, Sarah, Kokko-Gonzales, Paula I., Gormley, Niall A., Grocock, Russell J., Haudenschild, Christian D., Hims, Matthew M., James, Terena, Jia, Mingming, Kingsbury, Zoya, Leroy, Catherine, Marshall, John, Menzies, Andrew, Mudie, Laura J., Ning, Zemin, Royce, Tom, Schulz-Trieglaff, Ole B., Spiridou, Anastassia, Stebbings, Lucy A., Szajkowski, Lukasz, Teague, Jon, Williamson, David, Chin, Lynda, Ross, Mark T., Campbell, Peter J., Bentley, David R., Futreal, Andrew P., and Stratton, Michael R.

Published
2010
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8. Accurate whole human genome sequencing using reversible terminator chemistry

Author

Bentley, David R., Balasubramanian, Shankar, Swerdlow, Harold P., Smith, Geoffrey P., Milton, John, Brown, Clive G., Hall, Kevin P., Evers, Dirk J., Barnes, Colin L., Bignell, Helen R., Boutell, Jonathan M., Bryant, Jason, Carter, Richard J., Cheetham, R. Keira, Cox, Anthony J., Ellis, Darren J., Flatbush, Michael R., Gormley, Niall A., Humphray, Sean J., Irving, Leslie J., Karbelashvili, Mirian S., Kirk, Scott M., Li, Heng, Liu, Xiaohai, Maisinger, Klaus S., Murray, Lisa J., Obradovic, Bojan, Ost, Tobias, Parkinson, Michael L., Pratt, Mark R., Rasolonjatovo, Isabelle M. J., Reed, Mark T., Rigatti, Roberto, Rodighiero, Chiara, Ross, Mark T., Sabot, Andrea, Sankar, Subramanian V., Scally, Aylwyn, Schroth, Gary P., Smith, Mark E., Smith, Vincent P., Spiridou, Anastassia, Torrance, Peta E., Tzonev, Svilen S., Vermaas, Eric H., Walter, Klaudia, Wu, Xiaolin, Zhang, Lu, Alam, Mohammed D., Anastasi, Carole, Aniebo, Ify C., Bailey, David M. D., Bancarz, Iain R., Banerjee, Saibal, Barbour, Selena G., Baybayan, Primo A., Benoit, Vincent A., Benson, Kevin F., Bevis, Claire, Black, Phillip J., Boodhun, Asha, Brennan, Joe S., Bridgham, John A., Brown, Rob C., Brown, Andrew A., Buermann, Dale H., Bundu, Abass A., Burrows, James C., Carter, Nigel P., Castillo, Nestor, Catenazzi, Maria Chiara E., Chang, Simon, Cooley, R. Neil, Crake, Natasha R., Dada, Olubunmi O., Diakoumakos, Konstantinos D., Dominguez-Fernandez, Belen, Earnshaw, David J., Egbujor, Ugonna C., Elmore, David W., Etchin, Sergey S, Ewan, Mark R., Fedurco, Milan, Fraser, Louise J., Fuentes Fajardo, Karin V., Furey, W. Scott, George, David, Gietzen, Kimberley J., Goddard, Colin P., Golda, George S., Granieri, Philip A., Green, David E., Gustafson, David L., Hansen, Nancy F., Harnish, Kevin, Haudenschild, Christian D., Heyer, Narinder I, Hims, Matthew M., Ho, Johnny T., Horgan, Adrian M., Hoschler, Katya, Hurwitz, Steve, Ivanov, Denis V., Johnson, Maria Q., James, Terena, Huw Jones, T. A., Kang, Gyoung-Dong, Kerelska, Tzvetana H., Kersey, Alan D., Khrebtukova, Irina, Kindwall, Alex P., Kingsbury, Zoya, Kokko-Gonzales, Paula I., Kumar, Anil, Laurent, Marc A., Lawley, Cynthia T., Lee, Sarah E., Lee, Xavier, Liao, Arnold K., Loch, Jennifer A., Lok, Mitch, Luo, Shujun, Mammen, Radhika M., Martin, John W., McCauley, Patrick G., McNitt, Paul, Mehta, Parul, Moon, Keith W., Mullens, Joe W., Newington, Taksina, Ning, Zemin, Ling Ng, Bee, Novo, Sonia M., O'Neill, Michael J., Osborne, Mark A., Osnowski, Andrew, Ostadan, Omead, Paraschos, Lambros L., Pickering, Lea, Pike, Andrew C., Pike, Alger C., Pinkard, D. Chris, Pliskin, Daniel P., Podhasky, Joe, Quijano, Victor J., Raczy, Come, Rae, Vicki H., Rawlings, Stephen R., Rodriguez, Ana Chiva, Roe, Phyllida M., Rogers, John, Rogert Bacigalupo, Maria C., Romanov, Nikolai, Romieu, Anthony, Roth, Rithy K., Rourke, Natalie J., Ruediger, Silke T., Rusman, Eli, Sanches-Kuiper, Raquel M., Schenker, Martin R., Seoane, Josefina M., Shaw, Richard J., Shiver, Mitch K., Short, Steven W., Sizto, Ning L., Sluis, Johannes P., Smith, Melanie A., Sohna Sohna, Jean Ernest, Spence, Eric J., Stevens, Kim, Sutton, Neil, Szajkowski, Lukasz, Tregidgo, Carolyn L., Turcatti, Gerardo, vandeVondele, Stephanie, Verhovsky, Yuli, Virk, Selene M., Wakelin, Suzanne, Walcott, Gregory C., Wang, Jingwen, Worsley, Graham J., Yan, Juying, Yau, Ling, Zuerlein, Mike, Rogers, Jane, Mullikin, James C., Hurles, Matthew E., McCooke, Nick J., West, John S., Oaks, Frank L., Lundberg, Peter L., Klenerman, David, Durbin, Richard, and Smith, Anthony J.

Published
2008
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10. Deep and comparative analysis of the mycelium and appressorium transcriptomes of Magnaporthe grisea using MPSS, RL-SAGE, and oligoarray methods

Author

Dean Ralph, Haudenschild Christian D, Couglan Sean, Stahlberg Eric, Wing Rod, Li Huameng, Nobuta Kan, Raghupathy Mohan B, Venu RC, Gowda Malali, Nahm Baek-Hie, Meyers Blake C, and Wang Guo-Liang

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

Abstract Background Rice blast, caused by the fungal pathogen Magnaporthe grisea, is a devastating disease causing tremendous yield loss in rice production. The public availability of the complete genome sequence of M. grisea provides ample opportunities to understand the molecular mechanism of its pathogenesis on rice plants at the transcriptome level. To identify all the expressed genes encoded in the fungal genome, we have analyzed the mycelium and appressorium transcriptomes using massively parallel signature sequencing (MPSS), robust-long serial analysis of gene expression (RL-SAGE) and oligoarray methods. Results The MPSS analyses identified 12,531 and 12,927 distinct significant tags from mycelia and appressoria, respectively, while the RL-SAGE analysis identified 16,580 distinct significant tags from the mycelial library. When matching these 12,531 mycelial and 12,927 appressorial significant tags to the annotated CDS, 500 bp upstream and 500 bp downstream of CDS, 6,735 unique genes in mycelia and 7,686 unique genes in appressoria were identified. A total of 7,135 mycelium-specific and 7,531 appressorium-specific significant MPSS tags were identified, which correspond to 2,088 and 1,784 annotated genes, respectively, when matching to the same set of reference sequences. Nearly 85% of the significant MPSS tags from mycelia and appressoria and 65% of the significant tags from the RL-SAGE mycelium library matched to the M. grisea genome. MPSS and RL-SAGE methods supported the expression of more than 9,000 genes, representing over 80% of the predicted genes in M. grisea. About 40% of the MPSS tags and 55% of the RL-SAGE tags represent novel transcripts since they had no matches in the existing M. grisea EST collections. Over 19% of the annotated genes were found to produce both sense and antisense tags in the protein-coding region. The oligoarray analysis identified the expression of 3,793 mycelium-specific and 4,652 appressorium-specific genes. A total of 2,430 mycelial genes and 1,886 appressorial genes were identified by both MPSS and oligoarray. Conclusion The comprehensive and deep transcriptome analysis by MPSS and RL-SAGE methods identified many novel sense and antisense transcripts in the M. grisea genome at two important growth stages. The differentially expressed transcripts that were identified, especially those specifically expressed in appressoria, represent a genomic resource useful for gaining a better understanding of the molecular basis of M. grisea pathogenicity. Further analysis of the novel antisense transcripts will provide new insights into the regulation and function of these genes in fungal growth, development and pathogenesis in the host plants.

Published
2006
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11. Erratum: BAP1 loss defines a new class of renal cell carcinoma

Author

Peña-Llopis, Samuel, Vega-Rubín-de-Celis, Silvia, Liao, Arnold, Leng, Nan, Pavía-Jiménez, Andrea, Wang, Shanshan, Yamasaki, Toshinari, Zhrebker, Leah, Sivanand, Sharanya, Spence, Patrick, Kinch, Lisa, Hambuch, Tina, Jain, Suneer, Lotan, Yair, Margulis, Vitaly, Sagalowsky, Arthur I, Summerour, Pia Banerji, Kabbani, Wareef, Wong, S W Wendy, Grishin, Nick, Laurent, Marc, Xie, Xian-Jin, Haudenschild, Christian D, Ross, Mark T, Bentley, David R, Kapur, Payal, and Brugarolas, James

Published
2012
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12. An atlas of human gene expression from massively parallel signature sequencing (MPSS)

Author

Stevenson, Brian J., Iseli, Christian, Zhou, Diaxing, Kuznetsov, Dmitry, Haudenschild, Christian D., Khrebtukova, Irina, Jongneel, C. Victor, Simpson, Andrew J.G., Delorenzi, Mauro, Strausberg, Robert L, and Vasicek, Thomas J.

Subjects
Gene expression -- Research, Genetic transcription -- Research, Genetic research, Health
Abstract

Massively parallel signature sequencing (MPSS) is used to sample the transcriptomes of 32 normal human tissues to an unprecedented depth, thus documenting the patterns of expression of almost 20,000 genes with high sensitivity and specificity. The data confirm the widely held belief that differences in gene expression between cell and transcripts derived from a limited number of tissue-specific genes largely determine tissue types.

Published
2005

13. Elucidation of gene interaction networks through time-lagged correlation analysis of transciptional data

Author

Schmitt, William A., Jr., Raab, R. Michael, Stephanopoulos, Gregory, Haudenschild, Christian D., Agrawal, Vikas, Decola, Shannon, Ghazal, Hassan, and Edberg, Steve B.

Subjects
Genes -- Research, Genetic transcription -- Research, Genetic research, Health
Abstract

Time-series experiments were conducted by exposing Synechocystis sp. to serial perturbations in light intensity. Time-lagged correlation analysis on data identifies genetic interaction networks by constructing correlations between time-shifted transcription profiles.

Published
2004

14. The use of MPSS for whole-genome transcriptional analysis in Arabidopsis

Author

Meyers, Blake C., Tej, Shivakundan Singh, Tam H. Vu, Haudenschild, Christian D., Agrawal, Vikas, Decola, Shannon, Ghazal, Hassan, and Edberg, Steve B.

Subjects
Genomes -- Research, Genetic transcription -- Research, Arabidopsis -- Genetic aspects, Genetic research, Health
Abstract

Genomic and expressed signatures were generated representing transcripts from model plant Arabidopsis. A comparison of the signatures suggests that these genomic signatures contained one of 20 four-base words consistently associated with reduced massively parallel signature sequencing (MPSS) abundances.

Published
2004

16. Massively Parallel Signature Sequencing

Author

Zhou, Daixing, primary, Rao, Mahendra S., additional, Walker, Roger, additional, Khrebtukova, Irina, additional, Haudenschild, Christian D., additional, Miura, Takumi, additional, Decola, Shannon, additional, Vermaas, Eric, additional, Moon, Keith, additional, and Vasicek, Thomas J., additional

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17. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals

Author

Battle, Alexis, primary, Mostafavi, Sara, additional, Zhu, Xiaowei, additional, Potash, James B., additional, Weissman, Myrna M., additional, McCormick, Courtney, additional, Haudenschild, Christian D., additional, Beckman, Kenneth B., additional, Shi, Jianxin, additional, Mei, Rui, additional, Urban, Alexander E., additional, Montgomery, Stephen B., additional, Levinson, Douglas F., additional, and Koller, Daphne, additional

Published
2013
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18. A comprehensive catalogue of somatic mutations from a human cancer genome

Author

Pleasance, Erin D., primary, Cheetham, R. Keira, additional, Stephens, Philip J., additional, McBride, David J., additional, Humphray, Sean J., additional, Greenman, Chris D., additional, Varela, Ignacio, additional, Lin, Meng-Lay, additional, Ordóñez, Gonzalo R., additional, Bignell, Graham R., additional, Ye, Kai, additional, Alipaz, Julie, additional, Bauer, Markus J., additional, Beare, David, additional, Butler, Adam, additional, Carter, Richard J., additional, Chen, Lina, additional, Cox, Anthony J., additional, Edkins, Sarah, additional, Kokko-Gonzales, Paula I., additional, Gormley, Niall A., additional, Grocock, Russell J., additional, Haudenschild, Christian D., additional, Hims, Matthew M., additional, James, Terena, additional, Jia, Mingming, additional, Kingsbury, Zoya, additional, Leroy, Catherine, additional, Marshall, John, additional, Menzies, Andrew, additional, Mudie, Laura J., additional, Ning, Zemin, additional, Royce, Tom, additional, Schulz-Trieglaff, Ole B., additional, Spiridou, Anastassia, additional, Stebbings, Lucy A., additional, Szajkowski, Lukasz, additional, Teague, Jon, additional, Williamson, David, additional, Chin, Lynda, additional, Ross, Mark T., additional, Campbell, Peter J., additional, Bentley, David R., additional, Futreal, P. Andrew, additional, and Stratton, Michael R., additional

Published
2009
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20. Deep and comparative analysis of the mycelium and appressorium transcriptomes of Magnaporthe grisea using MPSS, RL-SAGE, and oligoarray methods

Author

Gowda, Malali, primary, Venu, RC, additional, Raghupathy, Mohan B, additional, Nobuta, Kan, additional, Li, Huameng, additional, Wing, Rod, additional, Stahlberg, Eric, additional, Couglan, Sean, additional, Haudenschild, Christian D, additional, Dean, Ralph, additional, Nahm, Baek-Hie, additional, Meyers, Blake C, additional, and Wang, Guo-Liang, additional

Published
2006
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23. Use of Massively Parallel Signature Sequencing to Study Genes Expressed During the Plant Defense Response.

Author

Walker, John M., Ronald, Pamela C., Meyers, Blake C., Haudenschild, Christian D., and Vemaraju, Kalyan

Abstract

Massively parallel signature sequencing is a sequencing-based method that provides quantitative gene expression data for nearly all transcripts in a particular ribonucleic acid sample. Although the sequencing technology is practiced as a service by a California-based company, we have developed methods for the handling and analysis of these data. This chapter describes the steps involved in obtaining data from massively parallel signature sequencing, aligning the signatures to genomic sequence, identifying novel transcripts, and performing quantitative analyses of genes expressed under conditions such as disease treatments. [ABSTRACT FROM AUTHOR]

Published
2007
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24. Massively Parallel Signature Sequencing.

Author

Walker, John M., Turksen, Kursad, Zhou, Daixing, Rao, Mahendra S., Walker, Roger, Khrebtukova, Irina, Haudenschild, Christian D., Miura, Takumi, Decola, Shannon, Vermaas, Eric, Moon, Keith, and Vasicek, Thomas J.

Abstract

Massively parallel signature sequencing is an ultra-high throughput sequencing technology. It can simultaneously sequence millions of sequence tags, and, therefore, is ideal for whole genome analysis. When applied to expression profiling, it reveals almost every transcript in the sample and provides its accurate expression level. This chapter describes the technology and its application in establishing stem cell transcriptome databases. [ABSTRACT FROM AUTHOR]

Published
2006
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25. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals.

Author

Battle, Alexis, Mostafavi, Sara, Xiaowei Zhu, Potash, James B., Weissman, Myrna M., McCormick, Courtney, Haudenschild, Christian D., Beckman, Kenneth B., Jianxin Shi, Rui Mei, Urban, Alexander E., Montgomery, Stephen B., Levinson, Douglas F., and Koller, Daphne

Subjects
*HUMAN genome, *NUCLEOTIDE sequence, *HUMAN genetic variation, *RNA, *GENES, *CHROMOSOMES
Abstract

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation--by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation. [ABSTRACT FROM AUTHOR]

Published
2014
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26. Use of massively parallel signature sequencing to study genes expressed during the plant defense response.

Author

Meyers BC, Haudenschild CD, and Vemaraju K

Subjects
Databases, Genetic, Gene Library, Plant Diseases immunology, RNA, Messenger analysis, RNA, Messenger genetics, RNA, Plant analysis, RNA, Plant genetics, User-Computer Interface, Arabidopsis genetics, Arabidopsis immunology, Gene Expression Regulation, Plant, Genes, Plant genetics, Plant Diseases genetics, Sequence Analysis, DNA methods
Abstract

Massively parallel signature sequencing is a sequencing-based method that provides quantitative gene expression data for nearly all transcripts in a particular ribonucleic acid sample. Although the sequencing technology is practiced as a service by a California-based company, we have developed methods for the handling and analysis of these data. This chapter describes the steps involved in obtaining data from massively parallel signature sequencing, aligning the signatures to genomic sequence, identifying novel transcripts, and performing quantitative analyses of genes expressed under conditions such as disease treatments.

Published
2007
Full Text
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27. Massively parallel signature sequencing.

Author

Zhou D, Rao MS, Walker R, Khrebtukova I, Haudenschild CD, Miura T, Decola S, Vermaas E, Moon K, and Vasicek TJ

Subjects
Cell Culture Techniques methods, Gene Library, Genome, Human, Humans, Pluripotent Stem Cells cytology, Sequence Analysis, DNA methods, Databases, Genetic, Gene Expression Profiling methods, Genomics methods, Pluripotent Stem Cells physiology, Transcription, Genetic
Abstract

Massively parallel signature sequencing is an ultra-high throughput sequencing technology. It can simultaneously sequence millions of sequence tags, and, therefore, is ideal for whole genome analysis. When applied to expression profiling, it reveals almost every transcript in the sample and provides its accurate expression level. This chapter describes the technology and its application in establishing stem cell transcriptome databases.

Published
2006
Full Text
View/download PDF

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