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1. Ahmad M, Helms V, Kalinina OV, Lengauer T: Relative Principal Components Analysis: Application to Analyzing Biomolecular Conformational Changes. Journal of Chemical Theory and Computation 2019, 15.
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@article{Ahmad2019, TITLE = {Relative Principal Components Analysis: {A}pplication to Analyzing Biomolecular Conformational Changes}, AUTHOR = {Ahmad, Mazen and Helms, Volkhard and Kalinina, Olga V. and Lengauer, Thomas}, LANGUAGE = {eng}, ISSN = {1549-9618}, DOI = {10.1021/acs.jctc.8b01074}, PUBLISHER = {ACM}, ADDRESS = {Washington, D.C.}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Journal of Chemical Theory and Computation}, VOLUME = {15}, NUMBER = {4}, PAGES = {2166--2178}, }
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%0 Journal Article %A Ahmad, Mazen %A Helms, Volkhard %A Kalinina, Olga V. %A Lengauer, Thomas %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Relative Principal Components Analysis: Application to Analyzing Biomolecular Conformational Changes : %G eng %U http://hdl.handle.net/21.11116/0000-0003-8671-6 %R 10.1021/acs.jctc.8b01074 %7 2019 %D 2019 %J Journal of Chemical Theory and Computation %O J. Chem. Theory Comput. %V 15 %N 4 %& 2166 %P 2166 - 2178 %I ACM %C Washington, D.C. %@ false
2. Blum A, Khalifa S, Nordstroem K, Simon M, Schulz MH, Schmitt MJ: Transcriptomics of a KDELR1 Knockout Cell Line Reveals Modulated Cell Adhesion Properties. Scientific Reports 2019, 9.
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@article{Blum2019, TITLE = {Transcriptomics of a {KDELR1} Knockout Cell Line Reveals Modulated Cell Adhesion Properties}, AUTHOR = {Blum, Andrea and Khalifa, Saleem and Nordstroem, Karl and Simon, Martin and Schulz, Marcel Holger and Schmitt, Manfred J.}, LANGUAGE = {eng}, ISSN = {2045-2322}, DOI = {10.1038/s41598-019-47027-5}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London, UK}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Scientific Reports}, VOLUME = {9}, EID = {10611}, }
Endnote
%0 Journal Article %A Blum, Andrea %A Khalifa, Saleem %A Nordstroem, Karl %A Simon, Martin %A Schulz, Marcel Holger %A Schmitt, Manfred J. %+ External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T Transcriptomics of a KDELR1 Knockout Cell Line Reveals Modulated Cell Adhesion Properties : %G eng %U http://hdl.handle.net/21.11116/0000-0004-8320-3 %R 10.1038/s41598-019-47027-5 %7 2019 %D 2019 %J Scientific Reports %O Sci. Rep. %V 9 %Z sequence number: 10611 %I Nature Publishing Group %C London, UK %@ false
3. Chaisson MJP, Sanders AD, Zhao X, Malhotra A, Porubsky D, Rausch T, Gardner EJ, Rodriguez OL, Guo L, Collins RL, Fan X, Wen J, Handsaker RE, Fairley S, Kronenberg ZN, Kong X, Hormozdiari F, Lee D, Wenger AM, Hastie AR, Antaki D, Anantharaman T, Audano PA, Brand H, Cantsilieris S, Cao H, Cerveira E, Chen C, Chen X, Chin C-S, et al.: Multi-platform Discovery of Haplotype-resolved Structural Variation in Human Genomes. Nature Communications 2019, 10.
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@article{Chaisson2019, TITLE = {Multi-platform Discovery of Haplotype-resolved Structural Variation in Human Genomes}, AUTHOR = {Chaisson, Mark J. P. and Sanders, Ashley D. and Zhao, Xuefang and Malhotra, Ankit and Porubsky, David and Rausch, Tobias and Gardner, Eugene J. and Rodriguez, Oscar L. and Guo, Li and Collins, Ryan L. and Fan, Xian and Wen, Jia and Handsaker, Robert E. and Fairley, Susan and Kronenberg, Zev N. and Kong, Xiangmeng and Hormozdiari, Fereydoun and Lee, Dillon and Wenger, Aaron M. and Hastie, Alex R. and Antaki, Danny and Anantharaman, Thomas and Audano, Peter A. and Brand, Harrison and Cantsilieris, Stuart and Cao, Han and Cerveira, Eliza and Chen, Chong and Chen, Xintong and Chin, Chen-Shan and Chong, Zechen and Chuang, Nelson T. and Lambert, Christine C. and Church, Deanna M. and Clarke, Laura and Farrell, Andrew and Flores, Joey and Galeev, Timur and Gorkin, David U. and Gujral, Madhusudan and Guryev, Victor and Heaton, William Haynes and Korlach, Jonas and Kumar, Sushant and Kwon, Jee Young and Lam, Ernest T. and Lee, Jong Eun and Lee, Joyce and Lee, Wan-Ping and Lee, Sau Peng and Li, Shantao and Marks, Patrick and Viaud-Martinez, Karine and Meiers, Sascha and Munson, Katherine M. and Navarro, Fabio C. P. and Nelson, Bradley J. and Nodzak, Conor and Noor, Amina and Kyriazopoulou-Panagiotopoulou, Sofia and Pang, Andy W. C. and Qiu, Yunjiang and Rosanio, Gabriel and Ryan, Mallory and Stuetz, Adrian and Spierings, Diana C. J. and Ward, Alistair and Welch, AnneMarie E. and Xiao, Ming and Xu, Wei and Zhang, Chengsheng and Zhu, Qihui and Zheng-Bradley, Xiangqun and Lowy, Ernesto and Yakneen, Sergei and McCarroll, Steven and Jun, Goo and Ding, Li and Koh, Chong Lek and Ren, Bing and Flicek, Paul and Chen, Ken and Gerstein, Mark B. and Kwok, Pui-Yan and Lansdorp, Peter M. and Marth, Gabor T. and Sebat, Jonathan and Shi, Xinghua and Bashir, Ali and Ye, Kai and Devine, Scott E. and Talkowski, Michael E. and Mills, Ryan E. and Marschall, Tobias and Korbel, Jan O. and Eichler, Evan E. and Lee, Charles}, LANGUAGE = {eng}, ISSN = {2041-1723}, DOI = {10.1038/s41467-018-08148-z}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Nature Communications}, VOLUME = {10}, EID = {1784}, }
Endnote
%0 Journal Article %A Chaisson, Mark J. P. %A Sanders, Ashley D. %A Zhao, Xuefang %A Malhotra, Ankit %A Porubsky, David %A Rausch, Tobias %A Gardner, Eugene J. %A Rodriguez, Oscar L. %A Guo, Li %A Collins, Ryan L. %A Fan, Xian %A Wen, Jia %A Handsaker, Robert E. %A Fairley, Susan %A Kronenberg, Zev N. %A Kong, Xiangmeng %A Hormozdiari, Fereydoun %A Lee, Dillon %A Wenger, Aaron M. %A Hastie, Alex R. %A Antaki, Danny %A Anantharaman, Thomas %A Audano, Peter A. %A Brand, Harrison %A Cantsilieris, Stuart %A Cao, Han %A Cerveira, Eliza %A Chen, Chong %A Chen, Xintong %A Chin, Chen-Shan %A Chong, Zechen %A Chuang, Nelson T. %A Lambert, Christine C. %A Church, Deanna M. %A Clarke, Laura %A Farrell, Andrew %A Flores, Joey %A Galeev, Timur %A Gorkin, David U. %A Gujral, Madhusudan %A Guryev, Victor %A Heaton, William Haynes %A Korlach, Jonas %A Kumar, Sushant %A Kwon, Jee Young %A Lam, Ernest T. %A Lee, Jong Eun %A Lee, Joyce %A Lee, Wan-Ping %A Lee, Sau Peng %A Li, Shantao %A Marks, Patrick %A Viaud-Martinez, Karine %A Meiers, Sascha %A Munson, Katherine M. %A Navarro, Fabio C. P. %A Nelson, Bradley J. %A Nodzak, Conor %A Noor, Amina %A Kyriazopoulou-Panagiotopoulou, Sofia %A Pang, Andy W. C. %A Qiu, Yunjiang %A Rosanio, Gabriel %A Ryan, Mallory %A Stuetz, Adrian %A Spierings, Diana C. J. %A Ward, Alistair %A Welch, AnneMarie E. %A Xiao, Ming %A Xu, Wei %A Zhang, Chengsheng %A Zhu, Qihui %A Zheng-Bradley, Xiangqun %A Lowy, Ernesto %A Yakneen, Sergei %A McCarroll, Steven %A Jun, Goo %A Ding, Li %A Koh, Chong Lek %A Ren, Bing %A Flicek, Paul %A Chen, Ken %A Gerstein, Mark B. %A Kwok, Pui-Yan %A Lansdorp, Peter M. %A Marth, Gabor T. %A Sebat, Jonathan %A Shi, Xinghua %A Bashir, Ali %A Ye, Kai %A Devine, Scott E. %A Talkowski, Michael E. %A Mills, Ryan E. %A Marschall, Tobias %A Korbel, Jan O. %A Eichler, Evan E. %A Lee, Charles %+ External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Multi-platform Discovery of Haplotype-resolved Structural Variation in Human Genomes : %G eng %U http://hdl.handle.net/21.11116/0000-0003-865E-D %R 10.1038/s41467-018-08148-z %7 2019 %D 2019 %J Nature Communications %O Nat. Commun. %V 10 %Z sequence number: 1784 %I Nature Publishing Group %C London %@ false
4. Doncheva NT, Domingues FS, McGivern DR, Shimakami T, Zeuzem S, Lengauer T, Lange CM, Albrecht M, Welsch C: Near-Neighbor Interactions in the NS3-4A Protease of HCV Impact Replicative Fitness of Drug-Resistant Viral Variants. Journal of Molecular Biology 2019, 431.
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@article{Doncheva2019, TITLE = {Near-Neighbor Interactions in the {NS3}-{4A} Protease of {HCV} Impact Replicative Fitness of Drug-Resistant Viral Variants}, AUTHOR = {Doncheva, Nadezhda Tsankova and Domingues, Francisco S. and McGivern, David R. and Shimakami, Tetsuro and Zeuzem, Stefan and Lengauer, Thomas and Lange, Christian M. and Albrecht, Mario and Welsch, Christoph}, LANGUAGE = {eng}, ISSN = {0022-2836}, DOI = {10.1016/j.jmb.2019.04.034}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Journal of Molecular Biology}, VOLUME = {431}, NUMBER = {12}, PAGES = {2354--2368}, }
Endnote
%0 Journal Article %A Doncheva, Nadezhda Tsankova %A Domingues, Francisco S. %A McGivern, David R. %A Shimakami, Tetsuro %A Zeuzem, Stefan %A Lengauer, Thomas %A Lange, Christian M. %A Albrecht, Mario %A Welsch, Christoph %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Near-Neighbor Interactions in the NS3-4A Protease of HCV Impact Replicative Fitness of Drug-Resistant Viral Variants : %G eng %U http://hdl.handle.net/21.11116/0000-0004-3FC9-4 %R 10.1016/j.jmb.2019.04.034 %7 2019 %D 2019 %J Journal of Molecular Biology %O J Mol Biol %V 431 %N 12 %& 2354 %P 2354 - 2368 %I Elsevier %C Amsterdam %@ false
5. Döring M: Computational Approaches for Improving Treatment and Prevention of Viral Infections. Universität des Saarlandes; 2019.
Abstract
The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptorhiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.
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@phdthesis{Doringphd2013, TITLE = {Computational Approaches for Improving Treatment and Prevention of Viral Infections}, AUTHOR = {D{\"o}ring, Matthias}, LANGUAGE = {eng}, DOI = {10.22028/D291-27946}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptorhiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.}, }
Endnote
%0 Thesis %A Döring, Matthias %Y Pfeifer, Nico %A referee: Lengauer, Thomas %A referee: Kalinina, Olga V. %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Computational Approaches for Improving Treatment and Prevention of Viral Infections : %G eng %U http://hdl.handle.net/21.11116/0000-0003-AEBA-8 %R 10.22028/D291-27946 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 337 p. %V phd %9 phd %X The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptorhiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27443
6. Döring M, Kreer C, Lehnen N, Klein F, Pfeifer N: Modeling the Amplification of Immunoglobulins through Machine Learning on Sequence-Specific Features. Scientific Reports 2019, 9.
Abstract
Successful primer design for polymerase chain reaction (PCR) hinges on the ability to identify primers that efciently amplify template sequences. Here, we generated a novel Taq PCR data set that reports the amplifcation status for pairs of primers and templates from a reference set of 47 immunoglobulin heavy chain variable sequences and 20 primers. Using logistic regression, we developed TMM, a model for predicting whether a primer amplifes a template given their nucleotide sequences. The model suggests that the free energy of annealing, ΔG, is the key driver of amplifcation (p=7.35e-12) and that 3′ mismatches should be considered in dependence on ΔG and the mismatch closest to the 3′ terminus (p=1.67e-05). We validated TMM by comparing its estimates with those from the thermodynamic model of DECIPHER (DE) and a model based solely on the free energy of annealing (FE). TMM outperformed the other approaches in terms of the area under the receiver operating characteristic curve (TMM: 0.953, FE: 0.941, DE: 0.896). TMM can improve primer design and is freely available via openPrimeR (http://openPrimeR.mpi-inf.mpg.de).
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@article{DoringPfeiferModel, TITLE = {Modeling the Amplification of Immunoglobulins through Machine Learning on Sequence-Specific Features}, AUTHOR = {D{\"o}ring, Matthias and Kreer, Christoph and Lehnen, Nathalie and Klein, Florian and Pfeifer, Nico}, LANGUAGE = {eng}, ISSN = {2045-2322}, DOI = {10.1038/s41598-019-47173-w}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London, UK}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Successful primer design for polymerase chain reaction (PCR) hinges on the ability to identify primers that efciently amplify template sequences. Here, we generated a novel Taq PCR data set that reports the amplifcation status for pairs of primers and templates from a reference set of 47 immunoglobulin heavy chain variable sequences and 20 primers. Using logistic regression, we developed TMM, a model for predicting whether a primer amplifes a template given their nucleotide sequences. The model suggests that the free energy of annealing, $\Delta$G, is the key driver of amplifcation (p=7.35e-12) and that 3′ mismatches should be considered in dependence on $\Delta$G and the mismatch closest to the 3′ terminus (p=1.67e-05). We validated TMM by comparing its estimates with those from the thermodynamic model of DECIPHER (DE) and a model based solely on the free energy of annealing (FE). TMM outperformed the other approaches in terms of the area under the receiver operating characteristic curve (TMM: 0.953, FE: 0.941, DE: 0.896). TMM can improve primer design and is freely available via openPrimeR (http://openPrimeR.mpi-inf.mpg.de).}, JOURNAL = {Scientific Reports}, VOLUME = {9}, EID = {10748}, }
Endnote
%0 Journal Article %A Döring, Matthias %A Kreer, Christoph %A Lehnen, Nathalie %A Klein, Florian %A Pfeifer, Nico %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Modeling the Amplification of Immunoglobulins through Machine Learning on Sequence-Specific Features : %G eng %U http://hdl.handle.net/21.11116/0000-0004-5F56-2 %R 10.1038/s41598-019-47173-w %7 2019-07-24 %D 2019 %8 24.07.2019 %X Successful primer design for polymerase chain reaction (PCR) hinges on the ability to identify primers that efciently amplify template sequences. Here, we generated a novel Taq PCR data set that reports the amplifcation status for pairs of primers and templates from a reference set of 47 immunoglobulin heavy chain variable sequences and 20 primers. Using logistic regression, we developed TMM, a model for predicting whether a primer amplifes a template given their nucleotide sequences. The model suggests that the free energy of annealing, ΔG, is the key driver of amplifcation (p=7.35e-12) and that 3′ mismatches should be considered in dependence on ΔG and the mismatch closest to the 3′ terminus (p=1.67e-05). We validated TMM by comparing its estimates with those from the thermodynamic model of DECIPHER (DE) and a model based solely on the free energy of annealing (FE). TMM outperformed the other approaches in terms of the area under the receiver operating characteristic curve (TMM: 0.953, FE: 0.941, DE: 0.896). TMM can improve primer design and is freely available via openPrimeR (http://openPrimeR.mpi-inf.mpg.de). %J Scientific Reports %O Sci. Rep. %V 9 %Z sequence number: 10748 %I Nature Publishing Group %C London, UK %@ false
7. Durai DA, Schulz MH: Improving in-silico Normalization using Read Weights. Scientific Reports 2019, 9.
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@article{Durai2019, TITLE = {Improving in-silico Normalization using Read Weights}, AUTHOR = {Durai, Dilip Ariyur and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {2045-2322}, DOI = {10.1038/s41598-019-41502-9}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London, UK}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Scientific Reports}, VOLUME = {9}, EID = {5133}, }
Endnote
%0 Journal Article %A Durai, Dilip Ariyur %A Schulz, Marcel Holger %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Improving in-silico Normalization using Read Weights : %G eng %U http://hdl.handle.net/21.11116/0000-0003-5F5F-A %R 10.1038/s41598-019-41502-9 %7 2019 %D 2019 %J Scientific Reports %O Sci. Rep. %V 9 %Z sequence number: 5133 %I Nature Publishing Group %C London, UK %@ false %U https://doi.org/10.1038/s41598-019-41502-9
8. Ebert P: What we leave behind : reproducibility in chromatin analysis within and across species. Universität des Saarlandes; 2019.
Abstract
Epigenetics is the field of biology that investigates heritable factors regulating gene expression without being directly encoded in the genome of an organism. The human genome is densely packed inside a cell's nucleus in the form of chromatin. Certain constituents of chromatin play a vital role as epigenetic factors in the dynamic regulation of gene expression. Epigenetic changes on the chromatin level are thus an integral part of the mechanisms governing the development of the functionally diverse cell types in multicellular species such as human. Studying these mechanisms is not only important to understand the biology of healthy cells, but also necessary to comprehend the epigenetic component in the formation of many complex diseases. Modern wet lab technology enables scientists to probe the epigenome with high throughput and in extensive detail. The fast generation of epigenetic datasets burdens computational researchers with the challenge of rapidly performing elaborate analyses without compromising on the scientific reproducibility of the reported findings. To facilitate reproducible computational research in epigenomics, this thesis proposes a task-oriented metadata model, relying on web technology and supported by database engineering, that aims at consistent and human-readable documentation of standardized computational workflows. The suggested approach features, e.g., computational validation of metadata records, automatic error detection, and progress monitoring of multi-step analyses, and was successfully field-tested as part of a large epigenome research consortium. This work leaves aside theoretical considerations, and intentionally emphasizes the realistic need of providing scientists with tools that assist them in performing reproducible research. Irrespective of the technological progress, the dynamic and cell-type specific nature of the epigenome commonly requires restricting the number of analyzed samples due to resource limitations. The second project of this thesis introduces the software tool SCIDDO, which has been developed for the differential chromatin analysis of cellular samples with potentially limited availability. By combining statistics, algorithmics, and best practices for robust software development, SCIDDO can quickly identify biologically meaningful regions of differential chromatin marking between cell types. We demonstrate SCIDDO's usefulness in an exemplary study in which we identify regions that establish a link between chromatin and gene expression changes. SCIDDO's quantitative approach to differential chromatin analysis is user-customizable, providing the necessary flexibility to adapt SCIDDO to specific research tasks. Given the functional diversity of cell types and the dynamics of the epigenome in response to environmental changes, it is hardly realistic to map the complete epigenome even for a single organism like human or mouse. For non-model organisms, e.g., cow, pig, or dog, epigenome data is particularly scarce. The third project of this thesis investigates to what extent bioinformatics methods can compensate for the comparatively little effort that is invested in charting the epigenome of non-model species. This study implements a large integrative analysis pipeline, including state-of-the-art machine learning, to transfer chromatin data for predictive modeling between 13 species. The evidence presented here indicates that a partial regulatory epigenetic signal is stably retained even over millions of years of evolutionary distance between the considered species. This finding suggests complementary and cost-effective ways for bioinformatics to contribute to comparative epigenome analysis across species boundaries.
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@phdthesis{Ebertphd2019, TITLE = {What we leave behind : reproducibility in chromatin analysis within and across species}, AUTHOR = {Ebert, Peter}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291--ds-278311}, DOI = {doi.org/10.22028/D291-27831}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {Epigenetics is the field of biology that investigates heritable factors regulating gene expression without being directly encoded in the genome of an organism. The human genome is densely packed inside a cell's nucleus in the form of chromatin. Certain constituents of chromatin play a vital role as epigenetic factors in the dynamic regulation of gene expression. Epigenetic changes on the chromatin level are thus an integral part of the mechanisms governing the development of the functionally diverse cell types in multicellular species such as human. Studying these mechanisms is not only important to understand the biology of healthy cells, but also necessary to comprehend the epigenetic component in the formation of many complex diseases. Modern wet lab technology enables scientists to probe the epigenome with high throughput and in extensive detail. The fast generation of epigenetic datasets burdens computational researchers with the challenge of rapidly performing elaborate analyses without compromising on the scientific reproducibility of the reported findings. To facilitate reproducible computational research in epigenomics, this thesis proposes a task-oriented metadata model, relying on web technology and supported by database engineering, that aims at consistent and human-readable documentation of standardized computational workflows. The suggested approach features, e.g., computational validation of metadata records, automatic error detection, and progress monitoring of multi-step analyses, and was successfully field-tested as part of a large epigenome research consortium. This work leaves aside theoretical considerations, and intentionally emphasizes the realistic need of providing scientists with tools that assist them in performing reproducible research. Irrespective of the technological progress, the dynamic and cell-type specific nature of the epigenome commonly requires restricting the number of analyzed samples due to resource limitations. The second project of this thesis introduces the software tool SCIDDO, which has been developed for the differential chromatin analysis of cellular samples with potentially limited availability. By combining statistics, algorithmics, and best practices for robust software development, SCIDDO can quickly identify biologically meaningful regions of differential chromatin marking between cell types. We demonstrate SCIDDO's usefulness in an exemplary study in which we identify regions that establish a link between chromatin and gene expression changes. SCIDDO's quantitative approach to differential chromatin analysis is user-customizable, providing the necessary flexibility to adapt SCIDDO to specific research tasks. Given the functional diversity of cell types and the dynamics of the epigenome in response to environmental changes, it is hardly realistic to map the complete epigenome even for a single organism like human or mouse. For non-model organisms, e.g., cow, pig, or dog, epigenome data is particularly scarce. The third project of this thesis investigates to what extent bioinformatics methods can compensate for the comparatively little effort that is invested in charting the epigenome of non-model species. This study implements a large integrative analysis pipeline, including state-of-the-art machine learning, to transfer chromatin data for predictive modeling between 13 species. The evidence presented here indicates that a partial regulatory epigenetic signal is stably retained even over millions of years of evolutionary distance between the considered species. This finding suggests complementary and cost-effective ways for bioinformatics to contribute to comparative epigenome analysis across species boundaries.}, }
Endnote
%0 Thesis %A Ebert, Peter %Y Lengauer, Thomas %A referee: Lenhof, Hans-Peter %A referee: Weikum, Gerhard %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Algorithms and Complexity, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T What we leave behind : reproducibility in chromatin analysis within and across species : %G eng %U http://hdl.handle.net/21.11116/0000-0003-9ADF-5 %R doi.org/10.22028/D291-27831 %U urn:nbn:de:bsz:291--ds-278311 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 152 p. %V phd %9 phd %X Epigenetics is the field of biology that investigates heritable factors regulating gene expression without being directly encoded in the genome of an organism. The human genome is densely packed inside a cell's nucleus in the form of chromatin. Certain constituents of chromatin play a vital role as epigenetic factors in the dynamic regulation of gene expression. Epigenetic changes on the chromatin level are thus an integral part of the mechanisms governing the development of the functionally diverse cell types in multicellular species such as human. Studying these mechanisms is not only important to understand the biology of healthy cells, but also necessary to comprehend the epigenetic component in the formation of many complex diseases. Modern wet lab technology enables scientists to probe the epigenome with high throughput and in extensive detail. The fast generation of epigenetic datasets burdens computational researchers with the challenge of rapidly performing elaborate analyses without compromising on the scientific reproducibility of the reported findings. To facilitate reproducible computational research in epigenomics, this thesis proposes a task-oriented metadata model, relying on web technology and supported by database engineering, that aims at consistent and human-readable documentation of standardized computational workflows. The suggested approach features, e.g., computational validation of metadata records, automatic error detection, and progress monitoring of multi-step analyses, and was successfully field-tested as part of a large epigenome research consortium. This work leaves aside theoretical considerations, and intentionally emphasizes the realistic need of providing scientists with tools that assist them in performing reproducible research. Irrespective of the technological progress, the dynamic and cell-type specific nature of the epigenome commonly requires restricting the number of analyzed samples due to resource limitations. The second project of this thesis introduces the software tool SCIDDO, which has been developed for the differential chromatin analysis of cellular samples with potentially limited availability. By combining statistics, algorithmics, and best practices for robust software development, SCIDDO can quickly identify biologically meaningful regions of differential chromatin marking between cell types. We demonstrate SCIDDO's usefulness in an exemplary study in which we identify regions that establish a link between chromatin and gene expression changes. SCIDDO's quantitative approach to differential chromatin analysis is user-customizable, providing the necessary flexibility to adapt SCIDDO to specific research tasks. Given the functional diversity of cell types and the dynamics of the epigenome in response to environmental changes, it is hardly realistic to map the complete epigenome even for a single organism like human or mouse. For non-model organisms, e.g., cow, pig, or dog, epigenome data is particularly scarce. The third project of this thesis investigates to what extent bioinformatics methods can compensate for the comparatively little effort that is invested in charting the epigenome of non-model species. This study implements a large integrative analysis pipeline, including state-of-the-art machine learning, to transfer chromatin data for predictive modeling between 13 species. The evidence presented here indicates that a partial regulatory epigenetic signal is stably retained even over millions of years of evolutionary distance between the considered species. This finding suggests complementary and cost-effective ways for bioinformatics to contribute to comparative epigenome analysis across species boundaries. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27387
9. Ebler J, Haukness M, Pesout T, Marschall T, Paten B: Haplotype-aware Diplotyping from Noisy Long Reads. Genome Biology 2019, 20.
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@article{Ebler2019, TITLE = {Haplotype-aware Diplotyping from Noisy Long Reads}, AUTHOR = {Ebler, Jana and Haukness, Marina and Pesout, Trevor and Marschall, Tobias and Paten, Benedict}, LANGUAGE = {eng}, ISSN = {1465-6906}, DOI = {10.1186/s13059-019-1709-0}, PUBLISHER = {BioMed Central Ltd.}, ADDRESS = {London}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Genome Biology}, VOLUME = {20}, EID = {116}, }
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%0 Journal Article %A Ebler, Jana %A Haukness, Marina %A Pesout, Trevor %A Marschall, Tobias %A Paten, Benedict %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T Haplotype-aware Diplotyping from Noisy Long Reads : %G eng %U http://hdl.handle.net/21.11116/0000-0003-D417-4 %R 10.1186/s13059-019-1709-0 %2 PMC6547545 %7 2019 %D 2019 %J Genome Biology %V 20 %Z sequence number: 116 %I BioMed Central Ltd. %C London %@ false
10. Gérard D, Schmidt F, Ginolhac A, Schmitz M, Halder R, Ebert P, Schulz MH, Sauter T, Sinkkonen L: Temporal Enhancer Profiling of Parallel Lineages Identifies AHR and GLIS1 as Regulators of Mesenchymal Multipotency. Nucleic Acids Research 2019, 47.
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@article{Gerard2019, TITLE = {Temporal enhancer profiling of parallel lineages identifies {AHR} and {GLIS1} as regulators of mesenchymal multipotency}, AUTHOR = {G{\'e}rard, Deborah and Schmidt, Florian and Ginolhac, Aur{\'e}lien and Schmitz, Martine and Halder, Rashi and Ebert, Peter and Schulz, Marcel Holger and Sauter, Thomas and Sinkkonen, Lasse}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {10.1093/nar/gky1240}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Nucleic Acids Research}, VOLUME = {47}, NUMBER = {3}, PAGES = {1141--1163}, }
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%0 Journal Article %A Gérard, Deborah %A Schmidt, Florian %A Ginolhac, Aurélien %A Schmitz, Martine %A Halder, Rashi %A Ebert, Peter %A Schulz, Marcel Holger %A Sauter, Thomas %A Sinkkonen, Lasse %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Temporal Enhancer Profiling of Parallel Lineages Identifies AHR and GLIS1 as Regulators of Mesenchymal Multipotency : %G eng %U http://hdl.handle.net/21.11116/0000-0003-1AFD-4 %R 10.1093/nar/gky1240 %7 2018 %D 2019 %J Nucleic Acids Research %O Nucleic Acids Res %V 47 %N 3 %& 1141 %P 1141 - 1163 %I Oxford University Press %C Oxford %@ false
11. Ghaffaari A, Marschall T: Fully-sensitive Seed Finding in Sequence Graphs Using a Hybrid Index. Bioinformatics (Proc ISMB/ECCB 2019) 2019, 35.
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@article{Ghaffaari2019, TITLE = {Fully-sensitive Seed Finding in Sequence Graphs Using a Hybrid Index}, AUTHOR = {Ghaffaari, Ali and Marschall, Tobias}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/btz341}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Bioinformatics (Proc. ISMB/ECCB)}, VOLUME = {35}, NUMBER = {14}, PAGES = {i81--i89}, BOOKTITLE = {ISMB/ECCB 2019 Proceedings}, }
Endnote
%0 Journal Article %A Ghaffaari, Ali %A Marschall, Tobias %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Fully-sensitive Seed Finding in Sequence Graphs Using a Hybrid Index : %G eng %U http://hdl.handle.net/21.11116/0000-0004-7BC8-1 %R 10.1093/bioinformatics/btz341 %7 2019 %D 2019 %J Bioinformatics %V 35 %N 14 %& i81 %P i81 - i89 %I Oxford University Press %C Oxford %@ false %B ISMB/ECCB 2019 Proceedings %O ISMB/ECCB 2019 The biennial joint meeting of ISMB (27th Annual Conference on Intelligent Systems for Molecular Biology) and ECCB (18th European Conference on Computational Biology) ; Basel, Switzerland, July 21–25, 2019
12. Gier S, Simon M, Nordstroem K, Khalifa S, Schulz MH, Schmitt MJ, Breinig F: Transcriptome Kinetics of Saccharomyces cerevisiae in Response to Viral Killer Toxin K1. Frontiers in Microbiology 2019, 10.
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@article{Gier2019, TITLE = {Transcriptome Kinetics of Saccharomyces cerevisiae in Response to Viral Killer Toxin {K1}}, AUTHOR = {Gier, Stefanie and Simon, Martin and Nordstroem, Karl and Khalifa, Salem and Schulz, Marcel Holger and Schmitt, Manfred J. and Breinig, Frank}, LANGUAGE = {eng}, ISSN = {1664-302X}, DOI = {10.3389/fmicb.2019.01102}, PUBLISHER = {Frontiers Media}, ADDRESS = {Lausanne}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Frontiers in Microbiology}, VOLUME = {10}, EID = {1102}, }
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%0 Journal Article %A Gier, Stefanie %A Simon, Martin %A Nordstroem, Karl %A Khalifa, Salem %A Schulz, Marcel Holger %A Schmitt, Manfred J. %A Breinig, Frank %+ External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Transcriptome Kinetics of Saccharomyces cerevisiae in Response to Viral Killer Toxin K1 : %G eng %U http://hdl.handle.net/21.11116/0000-0003-B2F8-C %R 10.3389/fmicb.2019.01102 %7 2019 %D 2019 %J Frontiers in Microbiology %V 10 %Z sequence number: 1102 %I Frontiers Media %C Lausanne %@ false
13. Hamdane N, Juhling F, Crouchet E, El Saghire H, Thumann C, Oudot MA, Bandiera S, Saviano A, Ponsolles C, Suarez AAR, Li S, Fujiwara N, Ono A, Davidson I, Bardeesy N, Schmidl C, Bock C, Schuster C, Lupberger J, Habersetzer F, Doffoel M, Piardi T, Sommacale D, Imamura M, Uchida T, Ohdan H, Aikata H, Chayama K, Boldanova T, Pessaux P, et al.: HCV-Induced Epigenetic Changes Associated With Liver Cancer Risk Persist After Sustained Virologic Response. Gastroenterology 2019, 156.
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@article{Hamdane2019, TITLE = {{HCV}-Induced Epigenetic Changes Associated With Liver Cancer Risk Persist After Sustained Virologic Response}, AUTHOR = {Hamdane, Nourdine and Juhling, Frank and Crouchet, Emilie and El Saghire, Houssein and Thumann, Christine and Oudot, Marine A. and Bandiera, Simonetta and Saviano, Antonio and Ponsolles, Clara and Suarez, Armando Andres Roca and Li, Shen and Fujiwara, Naoto and Ono, Atsushi and Davidson, Irwin and Bardeesy, Nabeel and Schmidl, Christian and Bock, Christoph and Schuster, Catherine and Lupberger, Joachim and Habersetzer, Francois and Doffoel, Michel and Piardi, Tullio and Sommacale, Daniele and Imamura, Michio and Uchida, Takuro and Ohdan, Hideki and Aikata, Hiroshi and Chayama, Kazuaki and Boldanova, Tujana and Pessaux, Patrick and Fuchs, Bryan C. and Hoshida, Yujin and Zeisel, Mirjam B. and Duong, Francois H. T. and Baumert, Thomas F.}, LANGUAGE = {eng}, ISSN = {0016-5085}, DOI = {10.1053/j.gastro.2019.02.038}, PUBLISHER = {W.B. Saunders}, ADDRESS = {Philadelphia, Pa}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Gastroenterology}, VOLUME = {156}, NUMBER = {8}, PAGES = {2313--2329}, EID = {e7}, }
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%0 Journal Article %A Hamdane, Nourdine %A Juhling, Frank %A Crouchet, Emilie %A El Saghire, Houssein %A Thumann, Christine %A Oudot, Marine A. %A Bandiera, Simonetta %A Saviano, Antonio %A Ponsolles, Clara %A Suarez, Armando Andres Roca %A Li, Shen %A Fujiwara, Naoto %A Ono, Atsushi %A Davidson, Irwin %A Bardeesy, Nabeel %A Schmidl, Christian %A Bock, Christoph %A Schuster, Catherine %A Lupberger, Joachim %A Habersetzer, Francois %A Doffoel, Michel %A Piardi, Tullio %A Sommacale, Daniele %A Imamura, Michio %A Uchida, Takuro %A Ohdan, Hideki %A Aikata, Hiroshi %A Chayama, Kazuaki %A Boldanova, Tujana %A Pessaux, Patrick %A Fuchs, Bryan C. %A Hoshida, Yujin %A Zeisel, Mirjam B. %A Duong, Francois H. T. %A Baumert, Thomas F. %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T HCV-Induced Epigenetic Changes Associated With Liver Cancer Risk Persist After Sustained Virologic Response : %G eng %U http://hdl.handle.net/21.11116/0000-0003-C353-3 %R 10.1053/j.gastro.2019.02.038 %7 2019 %D 2019 %J Gastroenterology %O Gastroenterology %V 156 %N 8 %& 2313 %P 2313 - 2329 %Z sequence number: e7 %I W.B. Saunders %C Philadelphia, Pa %@ false
14. Handl L, Jalali A, Scherer M, Eggeling R, Pfeifer N: Weighted Elastic Net for Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation Data. Bioinformatics (Proc ISMB/ECCB 2019) 2019, 35.
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@article{Handl2019, TITLE = {Weighted Elastic Net for Unsupervised Domain Adaptation with Application to Age Prediction from {DNA} Methylation Data}, AUTHOR = {Handl, Lisa and Jalali, Adrin and Scherer, Michael and Eggeling, Ralf and Pfeifer, Nico}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/btz338}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Bioinformatics (Proc. ISMB/ECCB)}, VOLUME = {35}, NUMBER = {14}, PAGES = {i154--i163}, BOOKTITLE = {ISMB/ECCB 2019 Proceedings}, }
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%0 Journal Article %A Handl, Lisa %A Jalali, Adrin %A Scherer, Michael %A Eggeling, Ralf %A Pfeifer, Nico %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Weighted Elastic Net for Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation Data : %G eng %U http://hdl.handle.net/21.11116/0000-0004-7BC6-3 %R 10.1093/bioinformatics/btz338 %7 2019 %D 2019 %J Bioinformatics %V 35 %N 14 %& i154 %P i154 - i163 %I Oxford University Press %C Oxford %@ false %B ISMB/ECCB 2019 Proceedings %O ISMB/ECCB 2019 The biennial joint meeting of ISMB (27th Annual Conference on Intelligent Systems for Molecular Biology) and ECCB (18th European Conference on Computational Biology) ; Basel, Switzerland, July 21–25, 2019.
15. Kanduri C, Bock C, Gundersen S, Hovig E, Sandve GK: Colocalization Analyses of Genomic Elements: Approaches, Recommendations and Challenges. Bioinformatics 2019, 35.
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@article{Kanduri2019, TITLE = {Colocalization Analyses of Genomic Elements: Approaches, Recommendations and Challenges}, AUTHOR = {Kanduri, Chakravarthi and Bock, Christoph and Gundersen, Sveinung and Hovig, Eivind and Sandve, Geir Kjetil}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/bty835}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford, UK}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Bioinformatics}, VOLUME = {35}, NUMBER = {9}, PAGES = {1615--1624}, }
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%0 Journal Article %A Kanduri, Chakravarthi %A Bock, Christoph %A Gundersen, Sveinung %A Hovig, Eivind %A Sandve, Geir Kjetil %+ External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Colocalization Analyses of Genomic Elements: Approaches, Recommendations and Challenges : %G eng %U http://hdl.handle.net/21.11116/0000-0003-C2A6-6 %R 10.1093/bioinformatics/bty835 %2 PMC6499241 %7 2019 %D 2019 %J Bioinformatics %V 35 %N 9 %& 1615 %P 1615 - 1624 %I Oxford University Press %C Oxford, UK %@ false
16. Karunanithi S, Simon M, Schulz MH: Automated Analysis of Small RNA Datasets with RAPID. PeerJ 2019, 7.
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@article{Karunanithi2019, TITLE = {Automated analysis of small {RNA} datasets with {RAPID}}, AUTHOR = {Karunanithi, Sivarajan and Simon, Martin and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {2167-8359}, DOI = {10.7717/peerj.6710}, PUBLISHER = {PeerJ Inc.}, ADDRESS = {San Francisco, USA}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {PeerJ}, VOLUME = {7}, EID = {e6710}, }
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%0 Journal Article %A Karunanithi, Sivarajan %A Simon, Martin %A Schulz, Marcel Holger %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Automated Analysis of Small RNA Datasets with RAPID : %G eng %U http://hdl.handle.net/21.11116/0000-0003-7D5F-8 %R 10.7717/peerj.6710 %7 2019 %D 2019 %J PeerJ %O PeerJ %V 7 %Z sequence number: e6710 %I PeerJ Inc. %C San Francisco, USA %@ false
17. Karunanithi S, Oruganti V, Marker S, Rodriguez-Viana AM, Drews F, Pirritano M, Nordström K, Simon M, Schulz MH: Exogenous RNAi Mechanisms Contribute to Transcriptome Adaptation by Phased siRNA Clusters in Paramecium. Nucleic Acids Research (London) 2019.
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@article{Karunanithi2019, TITLE = {Exogenous {RNAi} mechanisms contribute to transcriptome adaptation by phased {siRNA} clusters in {Paramecium}}, AUTHOR = {Karunanithi, Sivarajan and Oruganti, Vidya and Marker, Simone and Rodriguez-Viana, Angela M. and Drews, Franziska and Pirritano, Marcello and Nordstr{\"o}m, Karl and Simon, Martin and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {10.1093/nar/gkz553}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Nucleic Acids Research (London)}, EID = {gkz553}, }
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%0 Journal Article %A Karunanithi, Sivarajan %A Oruganti, Vidya %A Marker, Simone %A Rodriguez-Viana, Angela M. %A Drews, Franziska %A Pirritano, Marcello %A Nordström, Karl %A Simon, Martin %A Schulz, Marcel Holger %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Exogenous RNAi Mechanisms Contribute to Transcriptome Adaptation by Phased siRNA Clusters in Paramecium : %G eng %U http://hdl.handle.net/21.11116/0000-0003-E818-D %R 10.1093/nar/gkz553 %7 2019 %D 2019 %J Nucleic Acids Research (London) %O Nucleic Acids Res %Z sequence number: gkz553 %I Oxford University Press %C Oxford %@ false
18. Kosack L, Wingelhofer B, Popa A, Orlova A, Agerer B, Vilagos B, Majek P, Parapatics K, Lercher A, Ringler A, Klughammer J, Smyth M, Khamina K, Baazim H, de Araujo ED, Rosa DA, Park J, Tin G, Ahmar S, Gunning PT, Bock C, Siddle HV, Woods GM, Kubicek S, Murchison EP, Bennett KL, Moriggl R, Bergthaler A: The ERBB-STAT3 Axis Drives Tasmanian Devil Facial Tumor Disease. Cancer Cell 2019, 35.
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@article{Kosack2019, TITLE = {The {ERBB}-{STAT3} Axis Drives {T}asmanian Devil Facial Tumor Disease}, AUTHOR = {Kosack, Lindsay and Wingelhofer, Bettina and Popa, Alexandra and Orlova, Anna and Agerer, Benedikt and Vilagos, Bojan and Majek, Peter and Parapatics, Katja and Lercher, Alexander and Ringler, Anna and Klughammer, Johanna and Smyth, Mark and Khamina, Kseniya and Baazim, Hatoon and de Araujo, Elvin D. and Rosa, David A. and Park, Jisung and Tin, Gary and Ahmar, Siawash and Gunning, Patrick T. and Bock, Christoph and Siddle, Hannah V. and Woods, Gregory M. and Kubicek, Stefan and Murchison, Elizabeth P. and Bennett, Keiryn L. and Moriggl, Richard and Bergthaler, Andreas}, LANGUAGE = {eng}, ISSN = {1535-6108}, DOI = {10.1016/j.ccell.2018.11.018}, PUBLISHER = {Cell Press}, ADDRESS = {Cambridge, Mass.}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Cancer Cell}, VOLUME = {35}, NUMBER = {1}, PAGES = {125--139}, EID = {e9}, }
Endnote
%0 Journal Article %A Kosack, Lindsay %A Wingelhofer, Bettina %A Popa, Alexandra %A Orlova, Anna %A Agerer, Benedikt %A Vilagos, Bojan %A Majek, Peter %A Parapatics, Katja %A Lercher, Alexander %A Ringler, Anna %A Klughammer, Johanna %A Smyth, Mark %A Khamina, Kseniya %A Baazim, Hatoon %A de Araujo, Elvin D. %A Rosa, David A. %A Park, Jisung %A Tin, Gary %A Ahmar, Siawash %A Gunning, Patrick T. %A Bock, Christoph %A Siddle, Hannah V. %A Woods, Gregory M. %A Kubicek, Stefan %A Murchison, Elizabeth P. %A Bennett, Keiryn L. %A Moriggl, Richard %A Bergthaler, Andreas %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T The ERBB-STAT3 Axis Drives Tasmanian Devil Facial Tumor Disease : %G eng %U http://hdl.handle.net/21.11116/0000-0002-F715-0 %R 10.1016/j.ccell.2018.11.018 %7 2019 %D 2019 %J Cancer Cell %O Cancer Cell %V 35 %N 1 %& 125 %P 125 - 139 %Z sequence number: e9 %I Cell Press %C Cambridge, Mass. %@ false
19. List M, Dheghani Amirabad A, Kostka D, Schulz MH: Large-scale Inference of Competing Endogenous RNA Networks with Sparse Partial Correlation. Bioinformatics (Proc ISMB/ECCB 2019) 2019, 35.
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@article{List2019, TITLE = {Large-scale Inference of Competing Endogenous {RNA} Networks with Sparse Partial Correlation}, AUTHOR = {List, Markus and Dheghani Amirabad, Azim and Kostka, Dennis and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/btz314}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Bioinformatics (Proc. ISMB/ECCB)}, VOLUME = {35}, NUMBER = {14}, PAGES = {i596--i604}, BOOKTITLE = {ISMB/ECCB 2019 Proceedings}, }
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%0 Journal Article %A List, Markus %A Dheghani Amirabad, Azim %A Kostka, Dennis %A Schulz, Marcel Holger %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Large-scale Inference of Competing Endogenous RNA Networks with Sparse Partial Correlation : %G eng %U http://hdl.handle.net/21.11116/0000-0004-7CC2-6 %R 10.1093/bioinformatics/btz314 %7 2019 %D 2019 %J Bioinformatics %V 35 %N 14 %& i596 %P i596 - i604 %I Oxford University Press %C Oxford %@ false %B ISMB/ECCB 2019 Proceedings %O ISMB/ECCB 2019 The biennial joint meeting of ISMB (27th Annual Conference on Intelligent Systems for Molecular Biology) and ECCB (18th European Conference on Computational Biology) ; Basel, Switzerland, July 21–25, 2019
20. Li Z, Schulz MH, Look T, Begemann M, Zenke M, Costa IG: Identification of Transcription Factor Binding Sites using ATAC-seq. Genome Research 2019, 20.
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@article{Li2019, TITLE = {Identification of transcription factor binding sites using {ATAC}-seq}, AUTHOR = {Li, Zhijian and Schulz, Marcel Holger and Look, Thomas and Begemann, Matthias and Zenke, Martin and Costa, Ivan G.}, LANGUAGE = {eng}, ISSN = {1088-9051}, DOI = {10.1186/s13059-019-1642-2}, PUBLISHER = {Cold Spring Harbor Laboratory Press}, ADDRESS = {Cold Spring Harbor, N.Y.}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Genome Research}, VOLUME = {20}, EID = {45}, }
Endnote
%0 Journal Article %A Li, Zhijian %A Schulz, Marcel Holger %A Look, Thomas %A Begemann, Matthias %A Zenke, Martin %A Costa, Ivan G. %+ External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T Identification of Transcription Factor Binding Sites using ATAC-seq : %G eng %U http://hdl.handle.net/21.11116/0000-0003-2AF1-E %R 10.1186/s13059-019-1642-2 %7 2019 %D 2019 %J Genome Research %V 20 %Z sequence number: 45 %I Cold Spring Harbor Laboratory Press %C Cold Spring Harbor, N.Y. %@ false
21. Müller F, Scherer M, Assenov Y, Lutsik P, Walter J, Lengauer T, Bock C: RnBeads 2.0: Comprehensive Analysis of DNA Methylation Data. Genome Biology 2019, 20.
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@article{Mueller_GenomeBiology2019, TITLE = {{RnBeads} 2.0: {C}omprehensive analysis of {DNA} methylation data}, AUTHOR = {M{\"u}ller, Fabian and Scherer, Michael and Assenov, Yassen and Lutsik, Pavlo and Walter, J{\"o}rn and Lengauer, Thomas and Bock, Christoph}, LANGUAGE = {eng}, ISSN = {1465-6906}, DOI = {10.1186/s13059-019-1664-9}, PUBLISHER = {BioMed Central Ltd.}, ADDRESS = {London}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Genome Biology}, VOLUME = {20}, EID = {55}, }
Endnote
%0 Journal Article %A Müller, Fabian %A Scherer, Michael %A Assenov, Yassen %A Lutsik, Pavlo %A Walter, Jörn %A Lengauer, Thomas %A Bock, Christoph %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T RnBeads 2.0: Comprehensive Analysis of DNA Methylation Data : %G eng %U http://hdl.handle.net/21.11116/0000-0003-2DF3-9 %R 10.1186/s13059-019-1664-9 %7 2019 %D 2019 %J Genome Biology %V 20 %Z sequence number: 55 %I BioMed Central Ltd. %C London %@ false
22. Neininger K, Marschall T, Helms V: SNP and Indel Frequencies at Transcription Start Sites and at Canonical and Alternative Translation Initiation Sites in the Human Genome. PLoS One 2019, 14.
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@article{Neininger2019, TITLE = {{SNP} and indel frequencies at transcription start sites and at canonical and alternative translation initiation sites in the human genome}, AUTHOR = {Neininger, Kerstin and Marschall, Tobias and Helms, Volkhard}, LANGUAGE = {eng}, ISSN = {1932-6203}, DOI = {10.1371/journal.pone.0214816}, PUBLISHER = {Public Library of Science}, ADDRESS = {San Francisco, CA}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {PLoS One}, VOLUME = {14}, NUMBER = {4}, EID = {e0214816}, }
Endnote
%0 Journal Article %A Neininger, Kerstin %A Marschall, Tobias %A Helms, Volkhard %+ External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations %T SNP and Indel Frequencies at Transcription Start Sites and at Canonical and Alternative Translation Initiation Sites in the Human Genome : %G eng %U http://hdl.handle.net/21.11116/0000-0003-866B-E %R 10.1371/journal.pone.0214816 %7 2019-04-12 %D 2019 %8 12.04.2019 %J PLoS One %V 14 %N 4 %Z sequence number: e0214816 %I Public Library of Science %C San Francisco, CA %@ false
23. Nikumbh S: Interpretable Machine Learning Methods for Prediction and Analysis of Genome Regulation in 3D. Universität des Saarlandes; 2019.
Abstract
With the development of chromosome conformation capture-based techniques, we now know that chromatin is packed in three-dimensional (3D) space inside the cell nucleus. Changes in the 3D chromatin architecture have already been implicated in diseases such as cancer. Thus, a better understanding of this 3D conformation is of interest to help enhance our comprehension of the complex, multipronged regulatory mechanisms of the genome. The work described in this dissertation largely focuses on development and application of interpretable machine learning methods for prediction and analysis of long-range genomic interactions output from chromatin interaction experiments. In the first part, we demonstrate that the genetic sequence information at the ge- nomic loci is predictive of the long-range interactions of a particular locus of interest (LoI). For example, the genetic sequence information at and around enhancers can help predict whether it interacts with a promoter region of interest. This is achieved by building string kernel-based support vector classifiers together with two novel, in- tuitive visualization methods. These models suggest a potential general role of short tandem repeat motifs in the 3D genome organization. But, the insights gained out of these models are still coarse-grained. To this end, we devised a machine learning method, called CoMIK for Conformal Multi-Instance Kernels, capable of providing more fine-grained insights. When comparing sequences of variable length in the su- pervised learning setting, CoMIK can not only identify the features important for classification but also locate them within the sequence. Such precise identification of important segments of the whole sequence can help in gaining de novo insights into any role played by the intervening chromatin towards long-range interactions. Although CoMIK primarily uses only genetic sequence information, it can also si- multaneously utilize other information modalities such as the numerous functional genomics data if available. The second part describes our pipeline, pHDee, for easy manipulation of large amounts of 3D genomics data. We used the pipeline for analyzing HiChIP experimen- tal data for studying the 3D architectural changes in Ewing sarcoma (EWS) which is a rare cancer affecting adolescents. In particular, HiChIP data for two experimen- tal conditions, doxycycline-treated and untreated, and for primary tumor samples is analyzed. We demonstrate that pHDee facilitates processing and easy integration of large amounts of 3D genomics data analysis together with other data-intensive bioinformatics analyses.
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@phdthesis{Nikumbhphd2019, TITLE = {Interpretable Machine Learning Methods for Prediction and Analysis of Genome Regulation in {3D}}, AUTHOR = {Nikumbh, Sarvesh}, LANGUAGE = {eng}, DOI = {10.22028/D291-28153}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, ABSTRACT = {With the development of chromosome conformation capture-based techniques, we now know that chromatin is packed in three-dimensional (3D) space inside the cell nucleus. Changes in the 3D chromatin architecture have already been implicated in diseases such as cancer. Thus, a better understanding of this 3D conformation is of interest to help enhance our comprehension of the complex, multipronged regulatory mechanisms of the genome. The work described in this dissertation largely focuses on development and application of interpretable machine learning methods for prediction and analysis of long-range genomic interactions output from chromatin interaction experiments. In the first part, we demonstrate that the genetic sequence information at the ge- nomic loci is predictive of the long-range interactions of a particular locus of interest (LoI). For example, the genetic sequence information at and around enhancers can help predict whether it interacts with a promoter region of interest. This is achieved by building string kernel-based support vector classifiers together with two novel, in- tuitive visualization methods. These models suggest a potential general role of short tandem repeat motifs in the 3D genome organization. But, the insights gained out of these models are still coarse-grained. To this end, we devised a machine learning method, called CoMIK for Conformal Multi-Instance Kernels, capable of providing more fine-grained insights. When comparing sequences of variable length in the su- pervised learning setting, CoMIK can not only identify the features important for classification but also locate them within the sequence. Such precise identification of important segments of the whole sequence can help in gaining de novo insights into any role played by the intervening chromatin towards long-range interactions. Although CoMIK primarily uses only genetic sequence information, it can also si- multaneously utilize other information modalities such as the numerous functional genomics data if available. The second part describes our pipeline, pHDee, for easy manipulation of large amounts of 3D genomics data. We used the pipeline for analyzing HiChIP experimen- tal data for studying the 3D architectural changes in Ewing sarcoma (EWS) which is a rare cancer affecting adolescents. In particular, HiChIP data for two experimen- tal conditions, doxycycline-treated and untreated, and for primary tumor samples is analyzed. We demonstrate that pHDee facilitates processing and easy integration of large amounts of 3D genomics data analysis together with other data-intensive bioinformatics analyses.}, }
Endnote
%0 Thesis %A Nikumbh, Sarvesh %Y Pfeifer, Nico %A referee: Marschall, Tobias %A referee: Ebert, Peter %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Interpretable Machine Learning Methods for Prediction and Analysis of Genome Regulation in 3D : %G eng %U http://hdl.handle.net/21.11116/0000-0004-A5CE-A %R 10.22028/D291-28153 %I Universität des Saarlandes %C Saarbrücken %D 2019 %P 150 p. %V phd %9 phd %X With the development of chromosome conformation capture-based techniques, we now know that chromatin is packed in three-dimensional (3D) space inside the cell nucleus. Changes in the 3D chromatin architecture have already been implicated in diseases such as cancer. Thus, a better understanding of this 3D conformation is of interest to help enhance our comprehension of the complex, multipronged regulatory mechanisms of the genome. The work described in this dissertation largely focuses on development and application of interpretable machine learning methods for prediction and analysis of long-range genomic interactions output from chromatin interaction experiments. In the first part, we demonstrate that the genetic sequence information at the ge- nomic loci is predictive of the long-range interactions of a particular locus of interest (LoI). For example, the genetic sequence information at and around enhancers can help predict whether it interacts with a promoter region of interest. This is achieved by building string kernel-based support vector classifiers together with two novel, in- tuitive visualization methods. These models suggest a potential general role of short tandem repeat motifs in the 3D genome organization. But, the insights gained out of these models are still coarse-grained. To this end, we devised a machine learning method, called CoMIK for Conformal Multi-Instance Kernels, capable of providing more fine-grained insights. When comparing sequences of variable length in the su- pervised learning setting, CoMIK can not only identify the features important for classification but also locate them within the sequence. Such precise identification of important segments of the whole sequence can help in gaining de novo insights into any role played by the intervening chromatin towards long-range interactions. Although CoMIK primarily uses only genetic sequence information, it can also si- multaneously utilize other information modalities such as the numerous functional genomics data if available. The second part describes our pipeline, pHDee, for easy manipulation of large amounts of 3D genomics data. We used the pipeline for analyzing HiChIP experimen- tal data for studying the 3D architectural changes in Ewing sarcoma (EWS) which is a rare cancer affecting adolescents. In particular, HiChIP data for two experimen- tal conditions, doxycycline-treated and untreated, and for primary tumor samples is analyzed. We demonstrate that pHDee facilitates processing and easy integration of large amounts of 3D genomics data analysis together with other data-intensive bioinformatics analyses. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/27471
24. Pfitzer L, Moser C, Gegenfurtner F, Arner A, Foerster F, Atzberger C, Zisis T, Kubisch-Dohmen R, Busse J, Smith R, Timinszky G, Kalinina OV, Müller R, Wagner E, Vollmar AM, Zahler S: Targeting Actin Inhibits Repair of Doxorubicin-induced DNA Damage: A Novel Therapeutic Approach for Combination Therapy. Cell Death and Disease 2019, 10.
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@article{Pfitzer2019, TITLE = {Targeting actin inhibits repair of doxorubicin-induced {DNA} damage: {A} novel therapeutic approach for combination therapy}, AUTHOR = {Pfitzer, Lisa and Moser, Christina and Gegenfurtner, Florian and Arner, Anja and Foerster, Florian and Atzberger, Carina and Zisis, Themistoklis and Kubisch-Dohmen, Rebekka and Busse, Johanna and Smith, Rebecca and Timinszky, Gyula and Kalinina, Olga V. and M{\"u}ller, Rolf and Wagner, Ernst and Vollmar, Angelika M. and Zahler, Stefan}, LANGUAGE = {eng}, DOI = {10.1038/s41419-019-1546-9}, PUBLISHER = {Nature Publishing Group}, ADDRESS = {London}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Cell Death and Disease}, VOLUME = {10}, EID = {302}, }
Endnote
%0 Journal Article %A Pfitzer, Lisa %A Moser, Christina %A Gegenfurtner, Florian %A Arner, Anja %A Foerster, Florian %A Atzberger, Carina %A Zisis, Themistoklis %A Kubisch-Dohmen, Rebekka %A Busse, Johanna %A Smith, Rebecca %A Timinszky, Gyula %A Kalinina, Olga V. %A Müller, Rolf %A Wagner, Ernst %A Vollmar, Angelika M. %A Zahler, Stefan %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T Targeting Actin Inhibits Repair of Doxorubicin-induced DNA Damage: A Novel Therapeutic Approach for Combination Therapy : %G eng %U http://hdl.handle.net/21.11116/0000-0003-8A7E-5 %R 10.1038/s41419-019-1546-9 %7 2019 %D 2019 %J Cell Death and Disease %O Cell Death Dis %V 10 %Z sequence number: 302 %I Nature Publishing Group %C London
25. Roeder B, Kersten N, Herr M, Speicher NK, Pfeifer N: web-rMKL: A Web Server for Dimensionality Reduction and Sample Clustering of Multi-view Data Based on Unsupervised Multiple Kernel Learning. Nucleic Acids Research (London) 2019, 47.
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@article{Roeder2019, TITLE = {{web-rMKL}: {A} Web Server for Dimensionality Reduction and Sample Clustering of Multi-view Data Based on Unsupervised Multiple Kernel Learning}, AUTHOR = {Roeder, Benedict and Kersten, Nicolas and Herr, Marius and Speicher, Nora K. and Pfeifer, Nico}, LANGUAGE = {eng}, ISSN = {0305-1048}, DOI = {10.1093/nar/gkz422}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Nucleic Acids Research (London)}, VOLUME = {47}, NUMBER = {W1}, PAGES = {W605--W609}, }
Endnote
%0 Journal Article %A Roeder, Benedict %A Kersten, Nicolas %A Herr, Marius %A Speicher, Nora K. %A Pfeifer, Nico %+ External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T web-rMKL: A Web Server for Dimensionality Reduction and Sample Clustering of Multi-view Data Based on Unsupervised Multiple Kernel Learning : %G eng %U http://hdl.handle.net/21.11116/0000-0004-7AE0-6 %R 10.1093/nar/gkz422 %7 2019 %D 2019 %J Nucleic Acids Research (London) %O Nucleic Acids Res %V 47 %N W1 %& W605 %P W605 - W609 %I Oxford University Press %C Oxford %@ false
26. Schmidl C, Vladimer GI, Rendeiro AF, Schnabl S, Krausgruber T, Taubert C, Krall N, Pemovska T, Araghi M, Snijder B, Hubmann R, Ringler A, Runggatscher K, Demirtas D, Lopez de la Fuente O, Hilgarth M, Skrabs C, Porpaczy E, Gruber M, Hoermann G, Kubicek S, Staber PB, Shehata M, Superti-Furga G, Jaeger U, Bock C: Combined Chemosensitivity and Chromatin Profiling Prioritizes Drug Combinations in CLL. Nature Chemical Biology 2019, 15.
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@article{Schmidl2019, TITLE = {Combined Chemosensitivity and Chromatin Profiling Prioritizes Drug Combinations in {CLL}}, AUTHOR = {Schmidl, Christian and Vladimer, Gregory I. and Rendeiro, Andre F. and Schnabl, Susanne and Krausgruber, Thomas and Taubert, Christina and Krall, Nikolaus and Pemovska, Tea and Araghi, Mohammad and Snijder, Berend and Hubmann, Rainer and Ringler, Anna and Runggatscher, Kathrin and Demirtas, Dita and Lopez de la Fuente, Oscar and Hilgarth, Martin and Skrabs, Cathrin and Porpaczy, Edit and Gruber, Michaela and Hoermann, Gregor and Kubicek, Stefan and Staber, Philipp B. and Shehata, Medhat and Superti-Furga, Giulio and Jaeger, Ulrich and Bock, Christoph}, LANGUAGE = {eng}, ISSN = {1552-4450}, DOI = {10.1038/s41589-018-0205-2}, PUBLISHER = {Nature Pub. Group}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Nature Chemical Biology}, VOLUME = {15}, NUMBER = {3}, PAGES = {232--240}, }
Endnote
%0 Journal Article %A Schmidl, Christian %A Vladimer, Gregory I. %A Rendeiro, Andre F. %A Schnabl, Susanne %A Krausgruber, Thomas %A Taubert, Christina %A Krall, Nikolaus %A Pemovska, Tea %A Araghi, Mohammad %A Snijder, Berend %A Hubmann, Rainer %A Ringler, Anna %A Runggatscher, Kathrin %A Demirtas, Dita %A Lopez de la Fuente, Oscar %A Hilgarth, Martin %A Skrabs, Cathrin %A Porpaczy, Edit %A Gruber, Michaela %A Hoermann, Gregor %A Kubicek, Stefan %A Staber, Philipp B. %A Shehata, Medhat %A Superti-Furga, Giulio %A Jaeger, Ulrich %A Bock, Christoph %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T Combined Chemosensitivity and Chromatin Profiling Prioritizes Drug Combinations in CLL : %G eng %U http://hdl.handle.net/21.11116/0000-0003-1434-C %R 10.1038/s41589-018-0205-2 %7 2019 %D 2019 %J Nature Chemical Biology %O Nat. Chem. Biol. %V 15 %N 3 %& 232 %P 232 - 240 %I Nature Pub. Group %C New York, NY %@ false
27. Schmidt F, Schulz MH: On the Problem of Confounders in Modeling Gene Expression. Bioinformatics 2019, 35.
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@article{Schmidt2019, TITLE = {On the Problem of Confounders in Modeling Gene Expression}, AUTHOR = {Schmidt, Florian and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/bty674}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Bioinformatics}, VOLUME = {35}, NUMBER = {4}, PAGES = {711--719}, }
Endnote
%0 Journal Article %A Schmidt, Florian %A Schulz, Marcel Holger %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T On the Problem of Confounders in Modeling Gene Expression : %G eng %U http://hdl.handle.net/21.11116/0000-0003-2094-1 %R 10.1093/bioinformatics/bty674 %2 PMC6530814 %7 2018 %D 2019 %J Bioinformatics %V 35 %N 4 %& 711 %P 711 - 719 %I Oxford University Press %C Oxford %@ false
28. Schmidt F, Kern F, Ebert P, Baumgarten N, Schulz MH: TEPIC 2 - an Extended Framework for Transcription Factor Binding Prediction and Integrative Epigenomic Analysis. Bioinformatics 2019, 35.
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@article{Schmidt2019, TITLE = {{TEPIC} 2 -- an Extended Framework for Transcription Factor Binding Prediction and Integrative Epigenomic Analysis}, AUTHOR = {Schmidt, Florian and Kern, Fabian and Ebert, Peter and Baumgarten, Nina and Schulz, Marcel Holger}, LANGUAGE = {eng}, ISSN = {1367-4803}, DOI = {10.1093/bioinformatics/bty856}, PUBLISHER = {Oxford University Press}, ADDRESS = {Oxford, UK}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Bioinformatics}, VOLUME = {35}, NUMBER = {9}, PAGES = {1608--1609}, }
Endnote
%0 Journal Article %A Schmidt, Florian %A Kern, Fabian %A Ebert, Peter %A Baumgarten, Nina %A Schulz, Marcel Holger %+ Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society %T TEPIC 2 - an Extended Framework for Transcription Factor Binding Prediction and Integrative Epigenomic Analysis : %G eng %U http://hdl.handle.net/21.11116/0000-0003-C333-7 %R 10.1093/bioinformatics/bty856 %2 PMC6499243 %7 2019 %D 2019 %J Bioinformatics %V 35 %N 9 %& 1608 %P 1608 - 1609 %I Oxford University Press %C Oxford, UK %@ false
29. Sdelci S, Rendeiro AF, Rathert P, You W, Lin J-MG, Ringler A, Hofstaetter G, Moll HP, Guertl B, Farlik M, Schick S, Klepsch F, Oldach M, Buphamalai P, Schischlik F, Majek P, Parapatics K, Schmidl C, Schuster M, Penz T, Buckley DL, Hudecz O, Imre R, Wang S-Y, Maric HM, Kralovics R, Bennett KL, Mueller AC, Mechtler K, Menche J, et al.: MTHFD1 Interaction with BRD4 Links Folate Metabolism to Transcriptional Regulation. Nature Genetics 2019, 51.
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@article{Sdelci2019, TITLE = {{MTHFD1} interaction with {BRD4} links folate metabolism to transcriptional regulation}, AUTHOR = {Sdelci, Sara and Rendeiro, Andre F. and Rathert, Philipp and You, Wanhui and Lin, Jung-Ming G. and Ringler, Anna and Hofstaetter, Gerald and Moll, Herwig P. and Guertl, Bettina and Farlik, Matthias and Schick, Sandra and Klepsch, Freya and Oldach, Matthew and Buphamalai, Pisanu and Schischlik, Fiorella and Majek, Peter and Parapatics, Katja and Schmidl, Christian and Schuster, Michael and Penz, Thomas and Buckley, Dennis L. and Hudecz, Otto and Imre, Richard and Wang, Shuang-Yan and Maric, Hans Michael and Kralovics, Robert and Bennett, Keiryn L. and Mueller, Andre C. and Mechtler, Karl and Menche, Joerg and Bradner, James E. and Winter, Georg E. and Klavins, Kristaps and Casanova, Emilio and Bock, Christoph and Zuber, Johannes and Kubicek, Stefan}, LANGUAGE = {eng}, ISSN = {1061-4036}, DOI = {10.1038/s41588-019-0413-z}, PUBLISHER = {Nature America, Inc.}, ADDRESS = {New York, NY}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Nature Genetics}, VOLUME = {51}, NUMBER = {6}, PAGES = {990--998}, }
Endnote
%0 Journal Article %A Sdelci, Sara %A Rendeiro, Andre F. %A Rathert, Philipp %A You, Wanhui %A Lin, Jung-Ming G. %A Ringler, Anna %A Hofstaetter, Gerald %A Moll, Herwig P. %A Guertl, Bettina %A Farlik, Matthias %A Schick, Sandra %A Klepsch, Freya %A Oldach, Matthew %A Buphamalai, Pisanu %A Schischlik, Fiorella %A Majek, Peter %A Parapatics, Katja %A Schmidl, Christian %A Schuster, Michael %A Penz, Thomas %A Buckley, Dennis L. %A Hudecz, Otto %A Imre, Richard %A Wang, Shuang-Yan %A Maric, Hans Michael %A Kralovics, Robert %A Bennett, Keiryn L. %A Mueller, Andre C. %A Mechtler, Karl %A Menche, Joerg %A Bradner, James E. %A Winter, Georg E. %A Klavins, Kristaps %A Casanova, Emilio %A Bock, Christoph %A Zuber, Johannes %A Kubicek, Stefan %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations %T MTHFD1 Interaction with BRD4 Links Folate Metabolism to Transcriptional Regulation : %G eng %U http://hdl.handle.net/21.11116/0000-0003-D413-8 %R 10.1038/s41588-019-0413-z %7 2019 %D 2019 %J Nature Genetics %O Nature Genet. %V 51 %N 6 %& 990 %P 990 - 998 %I Nature America, Inc. %C New York, NY %@ false
30. Soldatov R, Kaucka M, Kastriti ME, Petersen J, Chontorotzea T, Englmaier L, Akkuratova N, Yang Y, Haring M, Dyachuk V, Bock C, Farlik M, Piacentino ML, Boismoreau F, Hilscher MM, Yokota C, Qian X, Nilsson M, Bronner ME, Croci L, Hsiao W-Y, Guertin DA, Brunet J-F, Consalez GG, Ernfors P, Fried K, Kharchenko PV, Adameyko I: Spatiotemporal Structure of Cell Fate Decisions in Murine Neural Crest. Science 2019, 364.
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@article{Soldatov2019, TITLE = {Spatiotemporal Structure of Cell Fate Decisions in Murine Neural Crest}, AUTHOR = {Soldatov, Ruslan and Kaucka, Marketa and Kastriti, Maria Eleni and Petersen, Julian and Chontorotzea, Tatiana and Englmaier, Lukas and Akkuratova, Natalia and Yang, Yunshi and Haring, Martin and Dyachuk, Viacheslav and Bock, Christoph and Farlik, Matthias and Piacentino, Michael L. and Boismoreau, Franck and Hilscher, Markus M. and Yokota, Chika and Qian, Xiaoyan and Nilsson, Mats and Bronner, Marianne E. and Croci, Laura and Hsiao, Wen-Yu and Guertin, David A. and Brunet, Jean-Francois and Consalez, Gian Giacomo and Ernfors, Patrik and Fried, Kaj and Kharchenko, Peter V. and Adameyko, Igor}, LANGUAGE = {eng}, ISSN = {0036-8075}, DOI = {10.1126/science.aas9536}, PUBLISHER = {AAAS}, ADDRESS = {Washington, D.C.}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, JOURNAL = {Science}, VOLUME = {364}, NUMBER = {64444}, EID = {eaas9536}, }
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%0 Journal Article %A Soldatov, Ruslan %A Kaucka, Marketa %A Kastriti, Maria Eleni %A Petersen, Julian %A Chontorotzea, Tatiana %A Englmaier, Lukas %A Akkuratova, Natalia %A Yang, Yunshi %A Haring, Martin %A Dyachuk, Viacheslav %A Bock, Christoph %A Farlik, Matthias %A Piacentino, Michael L. %A Boismoreau, Franck %A Hilscher, Markus M. %A Yokota, Chika %A Qian, Xiaoyan %A Nilsson, Mats %A Bronner, Marianne E. %A Croci, Laura %A Hsiao, Wen-Yu %A Guertin, David A. %A Brunet, Jean-Francois %A Consalez, Gian Giacomo %A Ernfors, Patrik %A Fried, Kaj %A Kharchenko, Peter V. %A Adameyko, Igor %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T Spatiotemporal Structure of Cell Fate Decisions in Murine Neural Crest : %G eng %U http://hdl.handle.net/21.11116/0000-0003-E117-5 %R 10.1126/science.aas9536 %7 2019 %D 2019 %J Science %O Science %V 364 %N 64444 %Z sequence number: eaas9536 %I AAAS %C Washington, D.C. %@ false
31. Yi G, Wierenga ATJ, Petraglia F, Narang P, Janssen-Megens EM, Mandoli A, Merkel A, Berentsen K, Kim B, Matarese F, Singh AA, Habibi E, Prange KHM, Mulder AB, Jansen JH, Clarke L, Heath S, van der Reijden BA, Flicek P, Yaspo M-L, Gut I, Bock C, Schuringa JJ, Altucci L, Vellenga E, Stunnenberg HG, Martens J, H. A: Chromatin-Based Classification of Genetically Heterogeneous AMLs into Two Distinct Subtypes with Diverse Stemness Phenotypes. Cell Reports 2019, 26.
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@article{Yi2019, TITLE = {Chromatin-Based Classification of Genetically Heterogeneous {AMLs} into Two Distinct Subtypes with Diverse Stemness Phenotypes}, AUTHOR = {Yi, Guoqiang and Wierenga, Albertus T. J. and Petraglia, Francesca and Narang, Pankaj and Janssen-Megens, Eva M. and Mandoli, Amit and Merkel, Angelika and Berentsen, Kim and Kim, Bowon and Matarese, Filomena and Singh, Abhishek A. and Habibi, Ehsan and Prange, Koen H. M. and Mulder, Andre B. and Jansen, Joop H. and Clarke, Laura and Heath, Simon and van der Reijden, Bert A. and Flicek, Paul and Yaspo, Marie-Laure and Gut, Ivo and Bock, Christoph and Schuringa, Jan Jacob and Altucci, Lucia and Vellenga, Edo and Stunnenberg, Hendrik G. and Martens, Joost and H., A.}, LANGUAGE = {eng}, ISSN = {2211-1247}, DOI = {10.1016/j.celrep.2018.12.098}, PUBLISHER = {Cell Press}, ADDRESS = {Maryland Heights, MO}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, JOURNAL = {Cell Reports}, VOLUME = {26}, NUMBER = {4}, PAGES = {1059--1069}, EID = {e6}, }
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%0 Journal Article %A Yi, Guoqiang %A Wierenga, Albertus T. J. %A Petraglia, Francesca %A Narang, Pankaj %A Janssen-Megens, Eva M. %A Mandoli, Amit %A Merkel, Angelika %A Berentsen, Kim %A Kim, Bowon %A Matarese, Filomena %A Singh, Abhishek A. %A Habibi, Ehsan %A Prange, Koen H. M. %A Mulder, Andre B. %A Jansen, Joop H. %A Clarke, Laura %A Heath, Simon %A van der Reijden, Bert A. %A Flicek, Paul %A Yaspo, Marie-Laure %A Gut, Ivo %A Bock, Christoph %A Schuringa, Jan Jacob %A Altucci, Lucia %A Vellenga, Edo %A Stunnenberg, Hendrik G. %A Martens, Joost %A H., A. %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations %T Chromatin-Based Classification of Genetically Heterogeneous AMLs into Two Distinct Subtypes with Diverse Stemness Phenotypes : %G eng %U http://hdl.handle.net/21.11116/0000-0002-F6CB-4 %R 10.1016/j.celrep.2018.12.098 %7 2019 %D 2019 %J Cell Reports %V 26 %N 4 %& 1059 %P 1059 - 1069 %Z sequence number: e6 %I Cell Press %C Maryland Heights, MO %@ false