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35 results on '"Supper, Jochen"'

1. Reconstructing Linear Gene Regulatory Networks

Author

Supper, Jochen, Spieth, Christian, Zell, Andreas, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Marchiori, Elena, editor, Moore, Jason H., editor, and Rajapakse, Jagath C., editor

Published
2007
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2. Analysis of MHC-Peptide Binding Using Amino Acid Property-Based Decision Rules

Author

Supper, Jochen, Dönnes, Pierre, Kohlbacher, Oliver, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Singh, Sameer, editor, Singh, Maneesha, editor, Apte, Chid, editor, and Perner, Petra, editor

Published
2005
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3. A Two-Step Clustering for 3-D Gene Expression Data Reveals the Main Features of the Arabidopsis Stress Response

Author

Strauch Martin, Supper Jochen, Spieth Christian, Wanke Dierk, Kilian Joachim, Harter Klaus, and Zell Andreas

Subjects
Biotechnology, TP248.13-248.65
Abstract

We developed an integrative approach for discovering gene modules, i.e. genes that are tightly correlated under several experimental conditions and applied it to a threedimensional Arabidopsis thaliana microarray dataset. The dataset consists of approximately 23000 genes responding to 9 abiotic stress conditions at 6-9 different points in time. Our approach aims at finding relatively small and dense modules lending themselves to a specific biological interpretation. In order to detect gene modules within this dataset, we employ a two-step clustering process. In the first step, a k-means clustering on one condition is performed, which is subsequently used in the second step as a seed for the clustering of the remaining conditions. To validate the significance of the obtained modules, we performed a permutation analysis and determined a null hypothesis to compare the module scores against, providing a p-value for each module. Significant modules were mapped to the Gene Ontology (GO) in order to determine the participating biological processes.

Published
2007
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6. Optimal selection of epitopes for TXP-immunoaffinity mass spectrometry

Author

Joos Thomas, Stoll Dieter, Poetz Oliver, Supper Jochen, Planatscher Hannes, Templin Markus F, and Zell Andreas

Subjects
Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background Mass spectrometry (MS) based protein profiling has become one of the key technologies in biomedical research and biomarker discovery. One bottleneck in MS-based protein analysis is sample preparation and an efficient fractionation step to reduce the complexity of the biological samples, which are too complex to be analyzed directly with MS. Sample preparation strategies that reduce the complexity of tryptic digests by using immunoaffinity based methods have shown to lead to a substantial increase in throughput and sensitivity in the proteomic mass spectrometry approach. The limitation of using such immunoaffinity-based approaches is the availability of the appropriate peptide specific capture antibodies. Recent developments in these approaches, where subsets of peptides with short identical terminal sequences can be enriched using antibodies directed against short terminal epitopes, promise a significant gain in efficiency. Results We show that the minimal set of terminal epitopes for the coverage of a target protein list can be found by the formulation as a set cover problem, preceded by a filtering pipeline for the exclusion of peptides and target epitopes with undesirable properties. Conclusions For small datasets (a few hundred proteins) it is possible to solve the problem to optimality with moderate computational effort using commercial or free solvers. Larger datasets, like full proteomes require the use of heuristics.

Published
2010
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7. EDISA: extracting biclusters from multiple time-series of gene expression profiles

Author

Harter Klaus, Wanke Dierk, Strauch Martin, Supper Jochen, and Zell Andreas

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

Abstract Background Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets. Results In this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities. Analysis of microarray datasets has lead us to define three biologically relevant module types: 1) We found modules with independent response profiles to be the most prevalent ones. These modules comprise genes which are co-regulated under several conditions, yet with a different response pattern under each condition. 2) Coherent modules with similar responses under all conditions occurred frequently, too, and were often contained within these modules. 3) A third module type, which covers a response specific to a single condition was also detected, but rarely. All of these modules are essentially different types of biclusters. Conclusion We successfully applied the EDISA to different 3D datasets. While previous studies were mostly aimed at detecting coherent modules only, our results show that coherent responses are often part of a more general module type with independent response profiles under different conditions. Our approach thus allows for a more comprehensive view of the gene expression response. After subsequent analysis of the resulting modules, the EDISA helped to shed light on the global organization of transcriptional control. An implementation of the algorithm is available at http://www-ra.informatik.uni-tuebingen.de/software/IAGEN/.

Published
2007
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9. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

Author

Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie, David, Jr., Szolovits, Peter, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzynski, Piotr, Fairbrother, William, Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. Ann, Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrecic, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., San Lucas, F. Anthony, Gonzalez-Garay, Manuel L., Caskey, C. Thomas, Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., Gonzalez-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De la Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. Micheil, Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac S., Margulies, David M., Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie, David, Jr., Szolovits, Peter, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzynski, Piotr, Fairbrother, William, Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. Ann, Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrecic, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., San Lucas, F. Anthony, Gonzalez-Garay, Manuel L., Caskey, C. Thomas, Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., Gonzalez-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De la Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. Micheil, Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac S., and Margulies, David M.

Abstract

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups., QC 20140819

Published
2014
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10. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

Author

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Szolovits, Peter, Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie Jr., David, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzyński, Piotr, Fairbrother, William G., Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. A., Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrečić, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., Lucas, F Anthony S., Gonzalez-Garay, Manuel L., Caskey, C. T., Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., González-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De La Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. M., Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac, Margulies, David M., Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Szolovits, Peter, Brownstein, Catherine A., Beggs, Alan H., Homer, Nils, Merriman, Barry, Yu, Timothy W., Flannery, Katherine C., DeChene, Elizabeth T., Towne, Meghan C., Savage, Sarah K., Price, Emily N., Holm, Ingrid A., Luquette, Lovelace J., Lyon, Elaine, Majzoub, Joseph, Neupert, Peter, McCallie Jr., David, Willard, Huntington F., Mendelsohn, Nancy J., Temme, Renee, Finkel, Richard S., Yum, Sabrina W., Medne, Livija, Sunyaev, Shamil R., Adzhubey, Ivan, Cassa, Christopher A., de Bakker, Paul I. W., Duzkale, Hatice, Dworzyński, Piotr, Fairbrother, William G., Francioli, Laurent, Funke, Birgit H., Giovanni, Monica A., Handsaker, Robert E., Lage, Kasper, Lebo, Matthew S., Lek, Monkol, Leshchiner, Ignaty, MacArthur, Daniel G., McLaughlin, Heather M., Murray, Michael F., Pers, Tune H., Polak, Paz P., Raychaudhuri, Soumya, Rehm, Heidi L., Soemedi, Rachel, Stitziel, Nathan O., Vestecka, Sara, Supper, Jochen, Gugenmus, Claudia, Klocke, Bernward, Hahn, Alexander, Schubach, Max, Menzel, Mortiz, Biskup, Saskia, Freisinger, Peter, Deng, Mario, Braun, Martin, Perner, Sven, Smith, Richard J. H., Andorf, Janeen L., Huang, Jian, Ryckman, Kelli, Sheffield, Val C., Stone, Edwin M., Bair, Thomas, Black-Ziegelbein, E. A., Braun, Terry A., Darbro, Benjamin, DeLuca, Adam P., Kolbe, Diana L., Scheetz, Todd E., Shearer, Aiden E., Sompallae, Rama, Wang, Kai, Bassuk, Alexander G., Edens, Erik, Mathews, Katherine, Moore, Steven A., Shchelochkov, Oleg A., Trapane, Pamela, Bossler, Aaron, Campbell, Colleen A., Heusel, Jonathan W., Kwitek, Anne, Maga, Tara, Panzer, Karin, Wassink, Thomas, Van Daele, Douglas, Azaiez, Hela, Booth, Kevin, Meyer, Nic, Segal, Michael M., Williams, Marc S., Tromp, Gerard, White, Peter, Corsmeier, Donald, Fitzgerald-Butt, Sara, Herman, Gail, Lamb-Thrush, Devon, McBride, Kim L., Newsom, David, Pierson, Christopher R., Rakowsky, Alexander T., Maver, Ales, Lovrečić, Luca, Palandacic, Anja, Peterlin, Borut, Torkamani, Ali, Wedell, Anna, Huss, Mikael, Alexeyenko, Andrey, Lindvall, Jessica M., Magnusson, Mans, Nilsson, Daniel, Stranneheim, Henrik, Taylan, Fulya, Gilissen, Christian, Hoischen, Alexander, van Bon, Bregje, Yntema, Helger, Nelen, Marcel, Zhang, Weidong, Sager, Jason, Zhang, Lu, Blair, Kathryn, Kural, Deniz, Cariaso, Michael, Lennon, Greg G., Javed, Asif, Agrawal, Saloni, Ng, Pauline C., Sandhu, Komal S., Krishna, Shuba, Veeramachaneni, Vamsi, Isakov, Ofer, Halperin, Eran, Friedman, Eitan, Shomron, Noam, Glusman, Gustavo, Roach, Jared C., Caballero, Juan, Cox, Hannah C., Mauldin, Denise, Ament, Seth A., Rowen, Lee, Richards, Daniel R., Lucas, F Anthony S., Gonzalez-Garay, Manuel L., Caskey, C. T., Bai, Yu, Huang, Ying, Fang, Fang, Zhang, Yan, Wang, Zhengyuan, Barrera, Jorge, Garcia-Lobo, Juan M., González-Lamuno, Domingo, Llorca, Javier, Rodriguez, Maria C., Varela, Ignacio, Reese, Martin G., De La Vega, Francisco M., Kiruluta, Edward, Cargill, Michele, Hart, Reece K., Sorenson, Jon M., Lyon, Gholson J., Stevenson, David A., Bray, Bruce E., Moore, Barry M., Eilbeck, Karen, Yandell, Mark, Zhao, Hongyu, Hou, Lin, Chen, Xiaowei, Yan, Xiting, Chen, Mengjie, Li, Cong, Yang, Can, Gunel, Murat, Li, Peining, Kong, Yong, Alexander, Austin C., Albertyn, Zayed I., Boycott, Kym M., Bulman, Dennis E., Gordon, Paul M. K., Innes, A. M., Knoppers, Bartha M., Majewski, Jacek, Marshall, Christian R., Parboosingh, Jillian S., Sawyer, Sarah L., Samuels, Mark E., Schwartzentruber, Jeremy, Kohane, Isaac, and Margulies, David M.

Abstract

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups., Boston Children's Hospital (Manton Center for Orphan Disease Research), Harvard Medical School (Center for Biomedical Infomatics)

Published
2014

11. A Systems Biology Markup Language Model of a T-Cell Receptor Activated Signal Transduction Network

Author

Tušek, Ana, Lorenz Müller, Christian, Supper, Jochen, Zell, Andreas, Kurtanjek Želimir, Sbalzarini, Ivo, Editor Vesna Luzar-Stiffler, Vesna Hljuz-Dobrić, Luzar-Stiffler, Vesna, and Hljuz-Dobrić, Vesna

Subjects
SBML, T-cell receptor, signal transduction
Abstract

Application of system theory in biology has led to development of computer languages for mathematical modeling which are in open source and apply common standards for model transplatform exchangeability and integration with extensive model banks, databases, numerical and statistical methods and graphical tools. Standardization has led to Systems Biology Markup Language (SBML) based on XML format, while open source policy enables integration of computer resources and provide synergistic integration of scientific results and methodologies from various scientific fields. Different software projects are have been ini-tiated resulting in software such as: SBToolbox, JDesigner, SBWorkbench (SBW), and CellDesigner. The main use of the systems biology software is to investigate biochemical networks, gene regulatory networks, signal transduction, cell communication, dynamical complex behavior, and most importantly to lead to understanding of life regulation. In this work is presented use of CellDesigner language and modeling tools applied for analysis of signal transduction in T-cell as a part of a human immunity system.

Published
2007

14. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

Author

Brownstein, Catherine A, primary, Beggs, Alan H, additional, Homer, Nils, additional, Merriman, Barry, additional, Yu, Timothy W, additional, Flannery, Katherine C, additional, DeChene, Elizabeth T, additional, Towne, Meghan C, additional, Savage, Sarah K, additional, Price, Emily N, additional, Holm, Ingrid A, additional, Luquette, Lovelace J, additional, Lyon, Elaine, additional, Majzoub, Joseph, additional, Neupert, Peter, additional, McCallie Jr, David, additional, Szolovits, Peter, additional, Willard, Huntington F, additional, Mendelsohn, Nancy J, additional, Temme, Renee, additional, Finkel, Richard S, additional, Yum, Sabrina W, additional, Medne, Livija, additional, Sunyaev, Shamil R, additional, Adzhubey, Ivan, additional, Cassa, Christopher A, additional, de Bakker, Paul IW, additional, Duzkale, Hatice, additional, Dworzyński, Piotr, additional, Fairbrother, William, additional, Francioli, Laurent, additional, Funke, Birgit H, additional, Giovanni, Monica A, additional, Handsaker, Robert E, additional, Lage, Kasper, additional, Lebo, Matthew S, additional, Lek, Monkol, additional, Leshchiner, Ignaty, additional, MacArthur, Daniel G, additional, McLaughlin, Heather M, additional, Murray, Michael F, additional, Pers, Tune H, additional, Polak, Paz P, additional, Raychaudhuri, Soumya, additional, Rehm, Heidi L, additional, Soemedi, Rachel, additional, Stitziel, Nathan O, additional, Vestecka, Sara, additional, Supper, Jochen, additional, Gugenmus, Claudia, additional, Klocke, Bernward, additional, Hahn, Alexander, additional, Schubach, Max, additional, Menzel, Mortiz, additional, Biskup, Saskia, additional, Freisinger, Peter, additional, Deng, Mario, additional, Braun, Martin, additional, Perner, Sven, additional, Smith, Richard JH, additional, Andorf, Janeen L, additional, Huang, Jian, additional, Ryckman, Kelli, additional, Sheffield, Val C, additional, Stone, Edwin M, additional, Bair, Thomas, additional, Black-Ziegelbein, E, additional, Braun, Terry A, additional, Darbro, Benjamin, additional, DeLuca, Adam P, additional, Kolbe, Diana L, additional, Scheetz, Todd E, additional, Shearer, Aiden E, additional, Sompallae, Rama, additional, Wang, Kai, additional, Bassuk, Alexander G, additional, Edens, Erik, additional, Mathews, Katherine, additional, Moore, Steven A, additional, Shchelochkov, Oleg A, additional, Trapane, Pamela, additional, Bossler, Aaron, additional, Campbell, Colleen A, additional, Heusel, Jonathan W, additional, Kwitek, Anne, additional, Maga, Tara, additional, Panzer, Karin, additional, Wassink, Thomas, additional, Van Daele, Douglas, additional, Azaiez, Hela, additional, Booth, Kevin, additional, Meyer, Nic, additional, Segal, Michael M, additional, Williams, Marc S, additional, Tromp, Gerard, additional, White, Peter, additional, Corsmeier, Donald, additional, Fitzgerald-Butt, Sara, additional, Herman, Gail, additional, Lamb-Thrush, Devon, additional, McBride, Kim L, additional, Newsom, David, additional, Pierson, Christopher R, additional, Rakowsky, Alexander T, additional, Maver, Aleš, additional, Lovrečić, Luca, additional, Palandačić, Anja, additional, Peterlin, Borut, additional, Torkamani, Ali, additional, Wedell, Anna, additional, Huss, Mikael, additional, Alexeyenko, Andrey, additional, Lindvall, Jessica M, additional, Magnusson, Måns, additional, Nilsson, Daniel, additional, Stranneheim, Henrik, additional, Taylan, Fulya, additional, Gilissen, Christian, additional, Hoischen, Alexander, additional, van Bon, Bregje, additional, Yntema, Helger, additional, Nelen, Marcel, additional, Zhang, Weidong, additional, Sager, Jason, additional, Zhang, Lu, additional, Blair, Kathryn, additional, Kural, Deniz, additional, Cariaso, Michael, additional, Lennon, Greg G, additional, Javed, Asif, additional, Agrawal, Saloni, additional, Ng, Pauline C, additional, Sandhu, Komal S, additional, Krishna, Shuba, additional, Veeramachaneni, Vamsi, additional, Isakov, Ofer, additional, Halperin, Eran, additional, Friedman, Eitan, additional, Shomron, Noam, additional, Glusman, Gustavo, additional, Roach, Jared C, additional, Caballero, Juan, additional, Cox, Hannah C, additional, Mauldin, Denise, additional, Ament, Seth A, additional, Rowen, Lee, additional, Richards, Daniel R, additional, Lucas, F Anthony, additional, Gonzalez-Garay, Manuel L, additional, Caskey, C, additional, Bai, Yu, additional, Huang, Ying, additional, Fang, Fang, additional, Zhang, Yan, additional, Wang, Zhengyuan, additional, Barrera, Jorge, additional, Garcia-Lobo, Juan M, additional, González-Lamuño, Domingo, additional, Llorca, Javier, additional, Rodriguez, Maria C, additional, Varela, Ignacio, additional, Reese, Martin G, additional, De La Vega, Francisco M, additional, Kiruluta, Edward, additional, Cargill, Michele, additional, Hart, Reece K, additional, Sorenson, Jon M, additional, Lyon, Gholson J, additional, Stevenson, David A, additional, Bray, Bruce E, additional, Moore, Barry M, additional, Eilbeck, Karen, additional, Yandell, Mark, additional, Zhao, Hongyu, additional, Hou, Lin, additional, Chen, Xiaowei, additional, Yan, Xiting, additional, Chen, Mengjie, additional, Li, Cong, additional, Yang, Can, additional, Gunel, Murat, additional, Li, Peining, additional, Kong, Yong, additional, Alexander, Austin C, additional, Albertyn, Zayed I, additional, Boycott, Kym M, additional, Bulman, Dennis E, additional, Gordon, Paul MK, additional, Innes, A, additional, Knoppers, Bartha M, additional, Majewski, Jacek, additional, Marshall, Christian R, additional, Parboosingh, Jillian S, additional, Sawyer, Sarah L, additional, Samuels, Mark E, additional, Schwartzentruber, Jeremy, additional, Kohane, Isaac S, additional, and Margulies, David M, additional

Published
2014
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15. Detecting and visualizing gene fusions

Author

Supper, Jochen, primary, Gugenmus, Claudia, additional, Wollnik, Johannes, additional, Drueke, Tanja, additional, Scherf, Matthias, additional, Hahn, Alexander, additional, Grote, Korbinian, additional, Bretschneider, Nancy, additional, Klocke, Bernward, additional, Zinser, Christian, additional, Cartharius, Kerstin, additional, and Seifert, Martin, additional

Published
2013
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20. Biomarkers and molecular tumour classification for non-genotoxic carcinogenesis

Author

Moggs, Jonathan, primary, Wolf, Roland, additional, Friedberg, Thomas, additional, Stewart, Neil, additional, Elcombe, Cliff, additional, Plummer, Simon, additional, Grasl-Kraupp, Bettina, additional, Schulte-Hermann, Rolf, additional, Gerner, Christopher, additional, Meehan, Richard, additional, Schwarz, Michael, additional, Zell, Andreas, additional, Schroeder, Adrian, additional, Supper, Jochen, additional, Pichler, Bernd, additional, Templin, Markus, additional, Maurel, Patrick, additional, Daujat-Chavanieu, Martine, additional, Pacquet, Jean-Luc, additional, Kalkuhl, Arno, additional, Matheis, Katja, additional, Ahr, Hans-Juergen, additional, Dhalluin, Stephane, additional, and Ostenfeldt, Nina, additional

Published
2009
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32. Reconstructing Linear Gene Regulatory Networks.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Marchiori, Elena, Moore, Jason H., Rajapakse, Jagath C., Supper, Jochen, and Spieth, Christian

Abstract

The ability to measure the transcriptional response after a stimulus has drawn much attention to the underlying gene regulatory networks. Here, we evaluate the application of methods to reconstruct gene regulatory networks by applying them to the SOS response of E. coli, the budding yeast cell cycle and in silico models. For each network we define an a priori validation network, where each interaction is justified by at least one publication. In addition to the existing methods, we propose a SVD based method (NSS). Overall, most reconstruction methods perform well on in silico data sets, both in terms of topological reconstruction and predictability. For biological data sets the application of reconstruction methods is suitable to predict the expression of genes, whereas the topological reconstruction is only satisfactory with steady-state measurements. Surprisingly, the performance measured on in silico data does not correspond with the performance measured on biological data. [ABSTRACT FROM AUTHOR]

Published
2007
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33. Analysis of MHC-Peptide Binding Using Amino Acid Property-Based Decision Rules.

Author

Singh, Sameer, Singh, Maneesha, Apte, Chid, Perner, Petra, Supper, Jochen, Dönnes, Pierre, and Kohlbacher, Oliver

Abstract

The human immune system is a highly complex machinery tuned to recognize specific molecular patterns in order to distinguish self from non-self proteins. Specialized immune cells can recognize major histocompatibility (MHC) molecules with bound protein fragments (peptides) on the surface of other cells. If these peptides originate from virus or cancer proteins, the immune cells can induce controlled cell death. In silico vaccine design typically starts with the identification of peptides that might induce an immune response as a first step. This is typically done by searching for specific amino acid patterns obtained from peptides known to be recognized by the immune system. We propose a new method for deriving decision rules based on the physiochemical properties of such peptides. The rulesets generated give insights into the underlying mechanism of MHC-peptide interaction. Furthermore, we show that these rulesets can be used for high accuracy prediction of MHC binding peptides. [ABSTRACT FROM AUTHOR]

Published
2005
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34. Optimal selection of epitopes for TXPimmunoaffinitymass spectrometry.

Author

Planatscher, Hannes, Supper, Jochen, Poetz, Oliver, Stoll, Dieter, Joos, Thomas, Templin, Markus F., and Zell, Andreas

Subjects
*MASS spectrometry, *MEDICINE, *IMMUNOGLOBULINS, *PEPTIDES, *PROTEINS
Abstract

Background: Mass spectrometry (MS) based protein profiling has become one of the key technologies in biomedical research and biomarker discovery. One bottleneck in MS-based protein analysis is sample preparation and an efficient fractionation step to reduce the complexity of the biological samples, which are too complex to be analyzed directly with MS. Sample preparation strategies that reduce the complexity of tryptic digests by using immunoaffinity based methods have shown to lead to a substantial increase in throughput and sensitivity in the proteomic mass spectrometry approach. The limitation of using such immunoaffinity-based approaches is the availability of the appropriate peptide specific capture antibodies. Recent developments in these approaches, where subsets of peptides with short identical terminal sequences can be enriched using antibodies directed against short terminal epitopes, promise a significant gain in efficiency. Results: We show that the minimal set of terminal epitopes for the coverage of a target protein list can be found by the formulation as a set cover problem, preceded by a filtering pipeline for the exclusion of peptides and target epitopes with undesirable properties. Conclusions: For small datasets (a few hundred proteins) it is possible to solve the problem to optimality with moderate computational effort using commercial or free solvers. Larger datasets, like full proteomes require the use of heuristics. [ABSTRACT FROM AUTHOR]

Published
2010
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35. Frequency Dependencies of Power Noise.

Author

Garben, Bernd, Frech, Roland, Supper, Jochen, and McAllister, Michael F.

Subjects
NOISE generators (Electronics), FREQUENCY discriminators, CAPACITORS, ELECTRIC impedance
Abstract

Analyzes the frequency dependencies of delta-I noise caused by variations of the on-chip switching activity. Variations in the noise source waveform; Description of decoupling capacitors; Definition of impedance as a function of frequency.

Published
2002
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