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371 results on '"Kohlbacher, O."'

1. The German Network for Personalized Medicine to enhance patient care and translational research

Author

Illert, A. L., Stenzinger, A., Bitzer, M., Horak, P., Gaidzik, V. I., Möller, Y., Beha, J., Öner, Ö., Schmitt, F., Laßmann, S., Ossowski, S., Schaaf, C. P., Hallek, M., Brümmendorf, T. H., Albers, P., Fehm, T., Brossart, P., Glimm, H., Schadendorf, D., Bleckmann, A., Brandts, C. H., Esposito, I., Mack, E., Peters, C., Bokemeyer, C., Fröhling, S., Kindler, T., Algül, H., Heinemann, V., Döhner, H., Bargou, R., Ellenrieder, V., Hillemanns, P., Lordick, F., Hochhaus, A., Beckmann, M. W., Pukrop, T., Trepel, M., Sundmacher, L., Wesselmann, S., Nettekoven, G., Kohlhuber, F., Heinze, O., Budczies, J., Werner, M., Nikolaou, K., Beer, A. J., Tabatabai, G., Weichert, W., Keilholz, U., Boerries, M., Kohlbacher, O., Duyster, J., Thimme, R., Seufferlein, T., Schirmacher, P., and Malek, N. P.

Published
2023
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2. Pseudonymisierung in REDCap mit E-PIX und gPAS

Author

Erhardt, C, Achauer, LC, Bialke, M, Stahl, D, Hardt, M, Kohlbacher, O, Verbücheln, R, Biergans, S, Erhardt, C, Achauer, LC, Bialke, M, Stahl, D, Hardt, M, Kohlbacher, O, Verbücheln, R, and Biergans, S

Published
2024

3. OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data

Author

Pfeuffer, J., Bielow, C., Wein, S., Jeong, K., Netz, E., Walter, A., Alka, O., Nilse, L., Colaianni, P.D., McCloskey, D., Kim, J., Rosenberger, G., Bichmann, L., Walzer, M., Veit, J., Boudaud, B., Bernt, Matthias, Patikas, N., Pilz, M., Startek, M.P., Kutuzova, S., Heumos, L., Charkow, J., Sing, J.C., Feroz, A., Siraj, A., Weisser, H., Dijkstra, T.M.H., Perez-Riverol, Y., Röst, H., Kohlbacher, O., Sachsenberg, T., Pfeuffer, J., Bielow, C., Wein, S., Jeong, K., Netz, E., Walter, A., Alka, O., Nilse, L., Colaianni, P.D., McCloskey, D., Kim, J., Rosenberger, G., Bichmann, L., Walzer, M., Veit, J., Boudaud, B., Bernt, Matthias, Patikas, N., Pilz, M., Startek, M.P., Kutuzova, S., Heumos, L., Charkow, J., Sing, J.C., Feroz, A., Siraj, A., Weisser, H., Dijkstra, T.M.H., Perez-Riverol, Y., Röst, H., Kohlbacher, O., and Sachsenberg, T.

Abstract

Mass spectrometry (MS) has become an indispensable analytical technique in the life sciences. For more than two decades, the OpenMS1 open-source project has been aiding mass spectrometrists with data processing. In version 3, OpenMS extends its capabilities beyond bottom-up proteomics to include high-throughput workflows in top-down proteomics, metabolomics, structural biology and oligonucleotide mass spectrometry. OpenMS makes analyses from emerging fields available to experimentalists, enhances computational workflows, and provides a reworked Python interface to make the computational methods more accessible to bioinformaticians and data scientists (Fig. 1). To help new users explore and quickly become productive with OpenMS, the website and documentation were modernized for this release. For a detailed overview of new tools and changes — based on more than 20,000 Git commits contributed by 150+ developers since the last major release in 2015 — we refer the reader to the Supplementary Note and Supplementary Table 1.

Published
2024

4. A novel formulation of nonlocal electrostatics

Author

Hildebrandt, A., Blossey, R., Rjasanow, S., Kohlbacher, O., and Lenhof, H. -P.

Subjects
Physics - Classical Physics, Physics - General Physics
Abstract

The accurate modeling of the dielectric properties of water is crucial for many applications in physics, computational chemistry and molecular biology. This becomes possible in the framework of nonlocal electrostatics, for which we propose a novel formulation allowing for numerical solutions for the nontrivial molecular geometries arising in the applications mentioned before. Our approach is based on the introduction of a secondary field, $\psi$, which acts as the potential for the rotation free part of the dielectric displacement field ${\bf D}$. For many relevant models, the dielectric function of the medium can be expressed as the Green's function of a local differential operator. In this case, the resulting coupled Poisson (-Boltzmann) equations for $\psi$ and the electrostatic potential $\phi$ reduce to a system of coupled PDEs. The approach is illustrated by its application to simple geometries., Comment: Some minor changes in the text; extended the explanations More precise formulation of the derivation our results. 5 pages, 3 figures, submitted to Phys. Rev. Lett

Published
2004
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5. Using nonlocal electrostatics for solvation free energy computations: ions and small molecules

Author

Hildebrandt, A., Kohlbacher, O., Blossey, R., and Lenhof, H. -P.

Subjects
Physics - Biological Physics, Physics - Chemical Physics
Abstract

Solvation free energy is an important quantity in Computational Chemistry with a variety of applications, especially in drug discovery and design. The accurate prediction of solvation free energies of small molecules in water is still a largely unsolved problem, which is mainly due to the complex nature of the water-solute interactions. In this letter we develop a scheme for the determination of the electrostatic contribution to the solvation free energy of charged molecules based on nonlocal electrostatics involving a minimal parameter set which in particular allows to introduce atomic radii in a consistent way. We test our approach on simple ions and small molecules for which both experimental results and other theoretical descriptions are available for quantitative comparison. We conclude that our approach is both physically transparent and quantitatively reliable.

Published
2002

6. The German Network for Personalized Medicine to enhance patient care and translational research

Author

Illert, A. L., Stenzinger, A., Bitzer, M., Horak, P., Gaidzik, V. I., Möller, Y., Beha, J., Öner, Ö., Schmitt, F., Laßmann, S., Ossowski, S., Schaaf, C. P., Hallek, M., Brümmendorf, T. H., Albers, P., Fehm, T., Brossart, P., Glimm, H., Schadendorf, D., Bleckmann, A., Brandts, C. H., Esposito, I., Mack, E., Peters, C., Bokemeyer, C., Fröhling, S., Kindler, T., Algül, H., Heinemann, V., Döhner, H., Bargou, R., Ellenrieder, V., Hillemanns, P., Lordick, F., Hochhaus, A., Beckmann, M. W., Pukrop, T., Trepel, M., Sundmacher, L., Wesselmann, S., Nettekoven, G., Kohlhuber, F., Heinze, O., Budczies, J., Werner, M., Nikolaou, K., Beer, A. J., Tabatabai, G., Weichert, W., Keilholz, U., Boerries, M., Kohlbacher, O., Duyster, J., Thimme, R., Seufferlein, T., Schirmacher, P., and Malek, N. P.

Published
2024
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7. Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha) - a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke

Author

Blum, C., Baur, D., Achauer, L., Berens, P., Biergans, S., Erb, M., Hömberg, V., Huang, Z., Kohlbacher, O., Liepert, J., Lindig, T., Lohmann, G., Macke, J., Römhild, J., Rösinger-Hein, C., Zrenner, B., and Ziemann, U.

Abstract

Background: Stroke is one of the most frequent diseases, and half of the stroke survivors are left with permanent impairment. Prediction of individual outcome is still difficult. Many but not all patients with stroke improve by approximately 1.7 times the initial impairment, that has been termed proportional recovery rule. The present study aims at identifying factors predicting motor outcome after stroke more accurately than before, and observe associations of rehabilitation treatment with outcome. Methods: The study is designed as a multi-centre prospective clinical observational trial. An extensive primary data set of clinical, neuroimaging, electrophysiological, and laboratory data will be collected within 96 h of stroke onset from patients with relevant upper extremity deficit, as indexed by a Fugl-Meyer-Upper Extremity (FM-UE) score ≤ 50. At least 200 patients will be recruited. Clinical scores will include the FM-UE score (range 0-66, unimpaired function is indicated by a score of 66), Action Research Arm Test, modified Rankin Scale, Barthel Index and Stroke-Specific Quality of Life Scale. Follow-up clinical scores and applied types and amount of rehabilitation treatment will be documented in the rehabilitation hospitals. Final follow-up clinical scoring will be performed 90 days after the stroke event. The primary endpoint is the change in FM-UE defined as 90 days FM-UE minus initial FM-UE, divided by initial FM-UE impairment. Changes in the other clinical scores serve as secondary endpoints. Machine learning methods will be employed to analyze the data and predict primary and secondary endpoints based on the primary data set and the different rehabilitation treatments. Discussion: If successful, outcome and relation to rehabilitation treatment in patients with acute motor stroke will be predictable more reliably than currently possible, leading to personalized neurorehabilitation. An important regulatory aspect of this trial is the first-time implementation of systematic patient data transfer between emergency and rehabilitation hospitals, which are divided institutions in Germany.

Published
2022

8. GECCO on FHIR – Towards Interoperable Data on COVID-19

Author

Erbelding, C, Sailer, B, Stenzhorn, H, Biergans, S, Kohlmayer, F, Kobak, EM, Pape, AA, Verbücheln, R, and Kohlbacher, O

Subjects
Electronic Data Processing, Health Information Exchange, ddc: 610, SARS-CoV-2, Health Information Interoperability, Medicine and health, COVID-19, Electronic Health Records
Abstract

As part of the NUM CODEX project, we have developed a workflow for data collection and transformation of patients suffering from COVID-19. Based on the GECCO dataset, electronic Case Report Forms (eCRFs) were designed for the electronic data capture (EDC)-Systems REDCap and DIS. Their standard CDISC [for full text, please go to the a.m. URL]

Published
2021
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9. Assessment of network module identification across complex diseases

Author

Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Marbach, D, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, W, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Choobdar S., Ahsen M. E., Crawford J., Tomasoni M., Fang T., Lamparter D., Lin J., Hescott B., Hu X., Mercer J., Natoli T., Narayan R., Aicheler F., Amoroso N., Arenas A., Azhagesan K., Baker A., Banf M., Batzoglou S., Baudot A., Bellotti R., Bergmann S., Boroevich K. A., Brun C., Cai S., Caldera M., Calderone A., Cesareni G., Chen W., Chichester C., Cowen L., Cui H., Dao P., De Domenico M., Dhroso A., Didier G., Divine M., del Sol A., Feng X., Flores-Canales J. C., Fortunato S., Gitter A., Gorska A., Guan Y., Guenoche A., Gomez S., Hamza H., Hartmann A., He S., Heijs A., Heinrich J., Hu Y., Huang X., Hughitt V. K., Jeon M., Jeub L., Johnson N. T., Joo K., Joung I. S., Jung S., Kalko S. G., Kamola P. J., Kang J., Kaveelerdpotjana B., Kim M., Kim Y. -A., Kohlbacher O., Korkin D., Krzysztof K., Kunji K., Kutalik Z., Lage K., Lang-Brown S., Le T. D., Lee J., Lee S., Li D., Li J., Liu L., Loizou A., Luo Z., Lysenko A., Ma T., Mall R., Marbach D., Mattia T., Medvedovic M., Menche J., Micarelli E., Monaco A., Muller F., Narykov O., Norman T., Park S., Perfetto L., Perrin D., Pirro S., Przytycka T. M., Qian X., Raman K., Ramazzotti D., Ramsahai E., Ravindran B., Rennert P., Saez-Rodriguez J., Scharfe C., Sharan R., Shi N., Shin W., Shu H., Sinha H., Slonim D. K., Spinelli L., Srinivasan S., Subramanian A., Suver C., Szklarczyk D., Tangaro S., Thiagarajan S., Tichit L., Tiede T., Tripathi B., Tsherniak A., Tsunoda T., Turei D., Ullah E., Vahedi G., Valdeolivas A., Vivek J., von Mering C., Waagmeester A., Wang B., Wang Y., Weir B. A., White S., Winkler S., Xu K., Xu T., Yan C., Yang L., Yu K., Yu X., Zaffaroni G., Zaslavskiy M., Zeng T., Zhang J. D., Zhang L., Zhang W., Zhang X., Zhang J., Zhou X., Zhou J., Zhu H., Zhu J., Zuccon G., Stolovitzky G., Cowen L. J., Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Marbach, D, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, W, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Choobdar S., Ahsen M. E., Crawford J., Tomasoni M., Fang T., Lamparter D., Lin J., Hescott B., Hu X., Mercer J., Natoli T., Narayan R., Aicheler F., Amoroso N., Arenas A., Azhagesan K., Baker A., Banf M., Batzoglou S., Baudot A., Bellotti R., Bergmann S., Boroevich K. A., Brun C., Cai S., Caldera M., Calderone A., Cesareni G., Chen W., Chichester C., Cowen L., Cui H., Dao P., De Domenico M., Dhroso A., Didier G., Divine M., del Sol A., Feng X., Flores-Canales J. C., Fortunato S., Gitter A., Gorska A., Guan Y., Guenoche A., Gomez S., Hamza H., Hartmann A., He S., Heijs A., Heinrich J., Hu Y., Huang X., Hughitt V. K., Jeon M., Jeub L., Johnson N. T., Joo K., Joung I. S., Jung S., Kalko S. G., Kamola P. J., Kang J., Kaveelerdpotjana B., Kim M., Kim Y. -A., Kohlbacher O., Korkin D., Krzysztof K., Kunji K., Kutalik Z., Lage K., Lang-Brown S., Le T. D., Lee J., Lee S., Li D., Li J., Liu L., Loizou A., Luo Z., Lysenko A., Ma T., Mall R., Marbach D., Mattia T., Medvedovic M., Menche J., Micarelli E., Monaco A., Muller F., Narykov O., Norman T., Park S., Perfetto L., Perrin D., Pirro S., Przytycka T. M., Qian X., Raman K., Ramazzotti D., Ramsahai E., Ravindran B., Rennert P., Saez-Rodriguez J., Scharfe C., Sharan R., Shi N., Shin W., Shu H., Sinha H., Slonim D. K., Spinelli L., Srinivasan S., Subramanian A., Suver C., Szklarczyk D., Tangaro S., Thiagarajan S., Tichit L., Tiede T., Tripathi B., Tsherniak A., Tsunoda T., Turei D., Ullah E., Vahedi G., Valdeolivas A., Vivek J., von Mering C., Waagmeester A., Wang B., Wang Y., Weir B. A., White S., Winkler S., Xu K., Xu T., Yan C., Yang L., Yu K., Yu X., Zaffaroni G., Zaslavskiy M., Zeng T., Zhang J. D., Zhang L., Zhang W., Zhang X., Zhang J., Zhou X., Zhou J., Zhu H., Zhu J., Zuccon G., Stolovitzky G., and Cowen L. J.

Abstract

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Published
2019

10. Swarm Learning for decentralized and confidential clinical machine learning

Author

Warnat-Herresthal, S. Schultze, H. Shastry, K.L. Manamohan, S. Mukherjee, S. Garg, V. Sarveswara, R. Händler, K. Pickkers, P. Aziz, N.A. Ktena, S. Tran, F. Bitzer, M. Ossowski, S. Casadei, N. Herr, C. Petersheim, D. Behrends, U. Kern, F. Fehlmann, T. Schommers, P. Lehmann, C. Augustin, M. Rybniker, J. Altmüller, J. Mishra, N. Bernardes, J.P. Krämer, B. Bonaguro, L. Schulte-Schrepping, J. De Domenico, E. Siever, C. Kraut, M. Desai, M. Monnet, B. Saridaki, M. Siegel, C.M. Drews, A. Nuesch-Germano, M. Theis, H. Heyckendorf, J. Schreiber, S. Kim-Hellmuth, S. Balfanz, P. Eggermann, T. Boor, P. Hausmann, R. Kuhn, H. Isfort, S. Stingl, J.C. Schmalzing, G. Kuhl, C.K. Röhrig, R. Marx, G. Uhlig, S. Dahl, E. Müller-Wieland, D. Dreher, M. Marx, N. Nattermann, J. Skowasch, D. Kurth, I. Keller, A. Bals, R. Nürnberg, P. Rieß, O. Rosenstiel, P. Netea, M.G. Theis, F. Mukherjee, S. Backes, M. Aschenbrenner, A.C. Ulas, T. Angelov, A. Bartholomäus, A. Becker, A. Bezdan, D. Blumert, C. Bonifacio, E. Bork, P. Boyke, B. Blum, H. Clavel, T. Colome-Tatche, M. Cornberg, M. De La Rosa Velázquez, I.A. Diefenbach, A. Dilthey, A. Fischer, N. Förstner, K. Franzenburg, S. Frick, J.-S. Gabernet, G. Gagneur, J. Ganzenmueller, T. Gauder, M. Geißert, J. Goesmann, A. Göpel, S. Grundhoff, A. Grundmann, H. Hain, T. Hanses, F. Hehr, U. Heimbach, A. Hoeper, M. Horn, F. Hübschmann, D. Hummel, M. Iftner, T. Iftner, A. Illig, T. Janssen, S. Kalinowski, J. Kallies, R. Kehr, B. Keppler, O.T. Klein, C. Knop, M. Kohlbacher, O. Köhrer, K. Korbel, J. Kremsner, P.G. Kühnert, D. Landthaler, M. Li, Y. Ludwig, K.U. Makarewicz, O. Marz, M. McHardy, A.C. Mertes, C. Münchhoff, M. Nahnsen, S. Nöthen, M. Ntoumi, F. Overmann, J. Peter, S. Pfeffer, K. Pink, I. Poetsch, A.R. Protzer, U. Pühler, A. Rajewsky, N. Ralser, M. Reiche, K. Ripke, S. da Rocha, U.N. Saliba, A.-E. Sander, L.E. Sawitzki, B. Scheithauer, S. Schiffer, P. Schmid-Burgk, J. Schneider, W. Schulte, E.-C. Sczyrba, A. Sharaf, M.L. Singh, Y. Sonnabend, M. Stegle, O. Stoye, J. Vehreschild, J. Velavan, T.P. Vogel, J. Volland, S. von Kleist, M. Walker, A. Walter, J. Wieczorek, D. Winkler, S. Ziebuhr, J. Breteler, M.M.B. Giamarellos-Bourboulis, E.J. Kox, M. Becker, M. Cheran, S. Woodacre, M.S. Goh, E.L. Schultze, J.L. COVID-19 Aachen Study (COVAS) Deutsche COVID-19 Omics Initiative (DeCOI)

Abstract

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. © 2021, The Author(s).

Published
2021

12. Tracing incorporation of heavy water into proteins for species-specific metabolic activity in complex communities

Author

Starke, Robert, Oliphant, K., Jehmlich, Nico, Schäpe, Stephanie, Sachsenberg, T., Kohlbacher, O., Allen-Vercoe, E., von Bergen, Martin, Starke, Robert, Oliphant, K., Jehmlich, Nico, Schäpe, Stephanie, Sachsenberg, T., Kohlbacher, O., Allen-Vercoe, E., and von Bergen, Martin

Abstract

Stable isotope probing (SIP) approaches are a suitable tool to identify active organisms in bacterial communities, but adding isotopically labeled substrate can alter both the structure and the functionality of the community. Here, we validated and demonstrated a substrate-independent protein-SIP protocol using isotopically labeled water that captures the entire microbial activity of a community. We found that 18O yielded a higher incorporation rate into peptides and thus comprised a higher sensitivity. We then applied the method to an in vitro model of a human distal gut microbial ecosystem grown in two medium formulations, to evaluate changes in microbial activity between a high-fiber and high-protein diet. We showed that only little changes are seen in the community structure but the functionality varied between the diets. In conclusion, our approach can detect species-specific metabolic activity in complex bacterial communities and more specifically to quantify the amount of amino acid synthesis. Heavy water makes possible to analyze the activity of bacterial communities for which adding an isotopically labeled energy and nutrient sources is not easily feasible.

Published
2020

15. Yes-Associated Protein 1 (YAP1) Promotes Melanoma Metastasis: P-071

Author

Meckbach, D., Menzel, M., Weide, B., Toussaint, C. N., Schilbach, K., Eigentler, T., Ikenberg, K., Busch, C., Quintanilla-Martinez, L., Kohlhofer, U., Göke, A., Göke, F., Handgretinger, R., Ottmann, C., Bastian, B., Garbe, C., Röcken, M., Perner, S., Kohlbacher, O., and Bauer, J.

Published
2013

18. The endometrial transcription landscape of MRKH syndrome

Author

Hentrich, T, primary, Koch, A, additional, Weber, N, additional, Kilzheimer, A, additional, Burkhardt, S, additional, Rall, K, additional, Casadei, N, additional, Kohlbacher, O, additional, Riess, O, additional, Schulze-Hentrich, JM, additional, and Brucker, SY, additional

Published
2020
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20. Letalität von Patienten mit COVID-19: Untersuchungen zu Ursachen und Dynamik an deutschen Universitätsklinika.

Author

Schüttler, J., Mang, J. M., Kapsner, L. A., Seuchter, S. A., Binder, H., Zöller, D., Kohlbacher, O., Boeker, M., Zacharowski, K., Rohde, G., Balig, J., Kampf, M. O., Röhrig, R., and Prokosch, H.-U.

Abstract

Copyright of Anaesthesiologie & Intensivmedizin is the property of DGAI e.V. - Deutsche Gesellschaft fur Anasthesiologie und Intensivmedizin e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2021
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21. User-driven development of an innovative software tool to support molecular tumor boards

Author

Stenzhorn, H, Halfmann, M, Oestermeier, U, Gerjets, P, and Kohlbacher, O

Subjects
ddc: 610, Medizinische Informatik, Medizinische Bioinformatik und Systembiologie, 610 Medical sciences, Medicine
Abstract

Background: Personalized Medicine is a rapidly growing area in healthcare which fundamentally changes the treatment of patients. This is especially true for cancer therapy where more and more hospitals conduct molecular tumor boards (MTB) bringing together experts from various clinical fields to jointly[for full text, please go to the a.m. URL], 63. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Published
2018
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22. An unusually high substitution rate in transplant-associated BK polyomavirus in vivo is further concentrated in HLA-C-bound viral peptides

Author

Domingo-Calap P, Schubert B, Joly M, Solis M, Untrau M, Carapito R, Georgel P, Caillard S, Fafi-Kremer S, Paul N, Kohlbacher O, González-Candelas F, and Bahram S

Subjects
viruses
Abstract

Infection with human BK polyomavirus, a small double-stranded DNA virus, potentially results in severe complications in immunocompromised patients. Here, we describe the in vivo variability and evolution of the BK polyomavirus by deep sequencing. Our data reveal the highest genomic evolutionary rate described in double-stranded DNA viruses, i.e., 10(-3)-10(-5) substitutions per nucleotide site per year. High mutation rates in viruses allow their escape from immune surveillance and adaptation to new hosts. By combining mutational landscapes across viral genomes with in silico prediction of viral peptides, we demonstrate the presence of significantly more coding substitutions within predicted cognate HLA-C-bound viral peptides than outside. This finding suggests a role for HLA-C in antiviral immunity, perhaps through the action of killer cell immunoglobulin-like receptors. The present study provides a comprehensive view of viral evolution and immune escape in a DNA virus.

Published
2018

23. The future of metabolomics in ELIXIR

Author

van Rijswijk, M, Beirnaert, C, Caron, C, Cascante, M, Dominguez, V, Dunn, WB, Ebbels, TMD, Giacomoni, F, Gonzalez-Beltran, A, Hankemeier, T, Haug, K, Izquierdo-Garcia, JL, Jimenez, RC, Jourdan, F, Kale, N, Klapa, MI, Kohlbacher, O, Koort, K, Kultima, K, Le Corguillé, G, Moreno, P, Moschonas, NK, Neumann, S, O'Donovan, C, Reczko, M, Rocca-Serra, P, Rosato, A, Salek, RM, Sansone, S-A, Satagopam, V, Schober, D, Shimmo, R, Spicer, RA, Spjuth, O, Thévenot, EA, Viant, MR, Weber, RJM, Willighagen, EL, Zanetti, G, Steinbeck, C, Dutch Techcentre for Life Sciences [Utrecht], Netherlands Metabolomics Centre, Department of Mathematics and Computer Science, ADReM, University of Antwerp (UA), French Institute of Bioinformatics (ELIXIR-FR), Department of Biochemistry and Molecular Biomedicine, Faculty of Science and Technology, Biochemistry and Molecular Biology, University of Barcelona, Birmingham Metabolomics Training Centre, University of Birmingham, Department of Surgery and Cancer, Imperial College London, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), MetaboHUB, Oxford e-Research Centre, University of Oxford, Leiden Academic Centre for Drug Research (LACDR), Universiteit Leiden, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Centro Nacional de Investigaciones Cardiovasculares, Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), ELIXIR Hub [Cambridge], Métabolisme et Xénobiotiques (ToxAlim-MeX), ToxAlim (ToxAlim), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT), Forth ICE-HT, Institute of Chemical Engineering Sciences, Foundation for Research and Technology - Hellas (FORTH), Max Planck Institute for Developmental Biology, Max-Planck-Gesellschaft, Department of Computer Science, Duke University [Durham], Center for Bioinformatics, University of Tübingen, The Centre of Excellence in Neural and Behavioural Sciences, Tallinn University, School of Natural Sciences and Health, Department of Medical Sciences (University of Miyasaki), University of Miyasaki, ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC), Centre National de la Recherche Scientifique (CNRS), Department of General Biology, Universidade Federal de Minas Gerais [Belo Horizonte] (UFMG), University of Patras, Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry (IPB), BSRC 'Alexander Fleming', Magnetic Resonance Center/Interuniversity Consortium of Magnetic Resonance Metalloproteins, Università degli Studi di Firenze = University of Florence (UniFI), Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg [Luxembourg], Université du Luxembourg (Uni.lu), Department of Pharmaceutical Biosciences, Uppsala University, Laboratoire Sciences des Données et de la Décision (LS2D), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Department of Bioinformatics, BiGCaT, Maastricht University [Maastricht], NUTRIM, Data-Intensive Computing, CRS4 Bioinformat, Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), European Project: 654241,H2020,H2020-EINFRA-2014-2,PhenoMeNal(2015), European Commission, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Leiden University, Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (FR2424), Université Pierre et Marie Curie (Paris 6), Federal University of Minas Gerais Belo Horizonte, University of Patras [Patras], University of Florence (UNIFI), Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Maastricht University, Friedrich-Schiller-Universität Jena, ANR-11-INBS-0010/11-INBS-0010,METABOHUB,Développement d’une infrastructure française distribuée pour la métabolomique dédiée à l’innovation(2011), Commission of the European Communities, European Molecular Biology Laboratory, Apollo-University Of Cambridge Repository, van Rijswijk, Merlijn [0000-0002-1067-7766], Beirnaert, Charlie [0000-0003-3007-2569], Gonzalez-Beltran, Alejandra [0000-0003-3499-8262], Haug, Kenneth [0000-0003-3168-4145], Jimenez, Rafael C [0000-0001-5404-7670], Kale, Namrata [0000-0002-4255-8104], Klapa, Maria I [0000-0002-2047-3185], Moschonas, Nicholas K [0000-0002-2556-537X], Rosato, Antonio [0000-0001-6172-0368], Salek, Reza M [0000-0001-8604-1732], Sansone, Susanna-Assunta [0000-0001-5306-5690], Schober, Daniel [0000-0001-8014-6648], Spicer, Rachel A [0000-0002-2807-8796], Spjuth, Ola [0000-0002-8083-2864], Thévenot, Etienne A [0000-0003-1019-4577], Willighagen, Egon L [0000-0001-7542-0286], Steinbeck, Christoph [0000-0001-6966-0814], Apollo - University of Cambridge Repository, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, Bioinformatica, RS: NUTRIM - R4 - Gene-environment interaction, University of Oxford [Oxford], Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Università degli Studi di Firenze = University of Florence [Firenze], Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées

Subjects
0301 basic medicine, databases, Data management, computational workflows, infrastructure en ligne, Cloud computing, Computational biology, Biology, General Biochemistry, Genetics and Molecular Biology, Chemical Biology of the Cell, analyse métabolomique, 03 medical and health sciences, statistical analysis, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL], Use case, General Pharmacology, Toxicology and Pharmaceutics, signal processing, computer.programming_language, bioinformatics infrastructure, training, 030102 biochemistry & molecular biology, General Immunology and Microbiology, metabolomics, bioinformatics, distributed computing, cloud, business.industry, cloud computing, pipeline, Articles, General Medicine, Opinion Article, Data science, metabolomics, Open data, Identification (information), 030104 developmental biology, Workflow, multi-omics approaches, Elixir (programming language), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, data standards, computer, Omics technologies
Abstract

We are grateful to the proteomics community for sharing their experiences from their meeting “The future of proteomics in ELIXIR” on March 1–2 2017 in Tübingen, allowing us to build on this and organise our workshop as a one-day event.The meeting was funded by PhenoMeNal, European Commission's Horizon2020 programme, grant agreement number 654241; Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases

Published
2017

25. The mzIdentML data standard version 1.2, supporting advances in proteome informatics

Author

Vizcaíno, JA, Mayer, G, Perkins, SR, Barsnes, H, Vaudel, M, Perez-Riverol, Y, Ternent, T, Uszkoreit, J, Eisenacher, M, Fischer, L, Rappsilber, J, Netz, E, Walzer, M, Kohlbacher, O, Leitner, A, Chalkley, RJ, Ghali, F, Martínez-Bartolomé, S, Deutsch, EW, Jones, AR, Vizcaíno, JA, Mayer, G, Perkins, SR, Barsnes, H, Vaudel, M, Perez-Riverol, Y, Ternent, T, Uszkoreit, J, Eisenacher, M, Fischer, L, Rappsilber, J, Netz, E, Walzer, M, Kohlbacher, O, Leitner, A, Chalkley, RJ, Ghali, F, Martínez-Bartolomé, S, Deutsch, EW, and Jones, AR

Abstract

The first stable version of the Proteomics Standards Initiative mzIdentML open data standard (version 1.1) was published in 2012 - capturing the outputs of peptide and protein identification software. In the intervening years, the standard has become well supported in both commercial and open software, as well as a submission and download format for public repositories. Here we report a new release of mzIdentML (version 1.2) that is required to keep pace with emerging practice in proteome informatics. New features have been added to support: (i) scores associated with localization of modifications on peptides; (ii) statistics performed at the level of peptides; (iii) identification of cross-linked peptides; and (iv) support for proteogenomics approaches. In addition, there is now improved support for the encoding of de novo sequencing of peptides, spectral library searches and protein inference. As a key point, the underlying XML schema has only undergone very minor modifications to simplify as much as possible the transition from version 1.1 to version 1.2 for implementers, but there have been several notable updates to the format specification, implementation guidelines, controlled vocabularies and validation software. mzIdentML 1.2 can be described as backwards compatible, in that reading software designed for mzIdentML 1.1 should function in most cases without adaptation. We anticipate that these developments will provide a continued stable base for software teams working to implement the standard. All the related documentation is accessible at http://www.psidev.info/mzidentml.

Published
2017

27. Machine learning competition in immunology – Prediction of HLA class I binding peptides

Author

Zhang, G. L., Ansari, H. R., Bradley, P., Cawley, G. C., Hertz, T., Hu, X., Jojic, N., Kim, Y., Kohlbacher, O., Lund, O., Lundegaard, C., Magaret, C. A., Nielsen, M., Papadopoulos, H., Raghava, G. P. S., Tal, V. -S, Xue, L. C., Yanover, C., Zhu, S., Rock, M. T., Crowe, J. E., Panayiotou, Christos G., Polycarpou, Marios M., Duch, W., Brusic, V., Panayiotou, Christos G. [0000-0002-6476-9025], and Polycarpou, Marios M. [0000-0001-6495-9171]

Subjects
Class (computer programming), Computer science, Histocompatibility Antigens Class I, Immunology, Peptide binding, Human leukocyte antigen, Competition (economics), Artificial Intelligence, Allergy and Immunology, Humans, Immunology and Allergy, Peptides, Algorithms, Protein Binding
Abstract

374 1-2 1 4

Published
2011

29. Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics

Author

University of Cambridge, Breckels, L.M., Holden, S.B., Wojnar, D., Mulvey, C.M., Christoforou, A., Groen, A., Trotter, M.W.B., Kohlbacher, O., Lilley, K.S., Gatto, Laurent, University of Cambridge, Breckels, L.M., Holden, S.B., Wojnar, D., Mulvey, C.M., Christoforou, A., Groen, A., Trotter, M.W.B., Kohlbacher, O., Lilley, K.S., and Gatto, Laurent

Abstract

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Published
2016

30. Testing and Validation of Computational Methods for Mass Spectrometry

Author

University of Cambridge, Gatto, Laurent, Hansen, K.D., Hoopmann, M.R., Hermjakob, H., Kohlbacher, O., Beyer, A., University of Cambridge, Gatto, Laurent, Hansen, K.D., Hoopmann, M.R., Hermjakob, H., Kohlbacher, O., and Beyer, A.

Abstract

High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets (http://compms.org/RefData) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.

Published
2016

31. Optimized neoantigen selection based on tumor exome data

Author

Kyzirakos, C., primary, Mohr, C., additional, Armeanu-Ebinger, S., additional, Feldhahn, M., additional, Hadaschik, D., additional, Walzer, M., additional, Döcker, D., additional, Menzel, M., additional, Nahnsen, S., additional, Kohlbacher, O., additional, and Biskup, S., additional

Published
2016
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34. The mzTab data exchange format: Communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience

Author

University of Cambridge, Griss, J., Jones, A.R., Sachsenberg, T., Walzer, M., Gatto, Laurent, Hartler, J., Thallinger, G.G., Salek, R.M., Steinbeck, C., Neuhauser, N., Cox, J., Neumann, S., Fan, J., Reisinger, F., Xu, Q.-W., Del Toro, N., Pérez-Riverol, Y., Ghali, F., Bandeira, N., Xenarios, I., Kohlbacher, O., Vizcaíno, J.A., Hermjakob, H., University of Cambridge, Griss, J., Jones, A.R., Sachsenberg, T., Walzer, M., Gatto, Laurent, Hartler, J., Thallinger, G.G., Salek, R.M., Steinbeck, C., Neuhauser, N., Cox, J., Neumann, S., Fan, J., Reisinger, F., Xu, Q.-W., Del Toro, N., Pérez-Riverol, Y., Ghali, F., Bandeira, N., Xenarios, I., Kohlbacher, O., Vizcaíno, J.A., and Hermjakob, H.

Published
2014

35. qcML: An exchange format for quality control metrics from mass spectrometry experiments

Author

University of Cambridge, Walzer, M., Pernas, L.E., Nasso, S., Bittremieux, W., Nahnsen, S., Kelchtermans, P., Pichler, P., Van Den Toorn, H.W.P., Staes, A., Vandenbussche, J., Mazanek, M., Taus, T., Scheltema, R.A., Kelstrup, C.D., Gatto, Laurent, Van Breukelen, B., Aiche, S., Valkenborg, D., Laukens, K., Lilley, K.S., Olsen, J.V., Heck, A.J.R., Mechtler, K., Aebersold, R., Gevaert, K., Vizcaíno, J.A., Hermjakob, H., Kohlbacher, O., Martens, L., University of Cambridge, Walzer, M., Pernas, L.E., Nasso, S., Bittremieux, W., Nahnsen, S., Kelchtermans, P., Pichler, P., Van Den Toorn, H.W.P., Staes, A., Vandenbussche, J., Mazanek, M., Taus, T., Scheltema, R.A., Kelstrup, C.D., Gatto, Laurent, Van Breukelen, B., Aiche, S., Valkenborg, D., Laukens, K., Lilley, K.S., Olsen, J.V., Heck, A.J.R., Mechtler, K., Aebersold, R., Gevaert, K., Vizcaíno, J.A., Hermjakob, H., Kohlbacher, O., and Martens, L.

Abstract

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities.

Published
2014

36. MetaProSIP: Automated inference of stable isotope incorporation rates in proteins for functional metaproteomics

Author

Sachsenberg, T., Herbst, Florian-Alexander, Taubert, Martin, Kermer, René, Jehmlich, Nico, von Bergen, Martin, Seifert, Jana, Kohlbacher, O., Sachsenberg, T., Herbst, Florian-Alexander, Taubert, Martin, Kermer, René, Jehmlich, Nico, von Bergen, Martin, Seifert, Jana, and Kohlbacher, O.

Abstract

We propose a joint experimental and theoretical approach to the automated reconstruction of elemental fluxes in microbial communities. While stable isotope probing of proteins (protein-SIP) has been successfully applied to study interactions and elemental carbon and nitrogen fluxes, the volume and complexity of mass spectrometric data in protein-SIP experiments pose new challenges for data analysis. Together with a flexible experimental setup, the novel bioinformatics tool MetaProSIP offers an automated high-throughput solution for a wide range of 13C or 15N protein-SIP experiments with special emphasis on the analysis of metaproteomic experiments where differential labeling of organisms can occur. The information calculated in MetaProSIP includes the determination of multiple relative isotopic abundances, the labeling ratio between old and new synthesized proteins, and the shape of the isotopic distribution. These parameters define the metabolic capacities and dynamics within the investigated microbial culture. MetaProSIP features a high degree of reproducibility, reliability, and quality control reporting. The ability to embed into the OpenMS framework allows for flexible construction of custom-tailored workflows. Software and documentation are available under an open-source license at www.openms.de/MetaProSIP.

Published
2014

38. A branch and cut algorithm for the optimal solution of the side-chain placement problem

Author

Althaus, E., Kohlbacher, O., Lenhof, H., and Müller, P.

Subjects
Quantitative Biology::Biomolecules
Abstract

Rigid--body docking approaches are not sufficient to predict the structure of a protein complex from the unbound (native) structures of the two proteins. Accounting for side chain flexibility is an important step towards fully flexible protein docking. This work describes an approach that allows conformational flexibility for the side chains while keeping the protein backbone rigid. Starting from candidates created by a rigid--docking algorithm, we demangle the side chains of the docking site, thus creating reasonable approximations of the true complex structure. These structures are ranked with respect to the binding free energy. We present two new techniques for side chain demangling. Both approaches are based on a discrete representation of the side chain conformational space by the use of a rotamer library. This leads to a combinatorial optimization problem. For the solution of this problem we propose a fast heuristic approach and an exact, albeit slower, method that uses branch--\&--cut techniques. As a test set we use the unbound structures of three proteases and the corresponding protein inhibitors. For each of the examples, the highest--ranking conformation produced was a good approximation of the true complex structure.

Published
2000

40. BALL: Biochemical Algorithms Library

Author

Boghossian, N., Kohlbacher, O., and Lenhof, H.

Abstract

In the next century, virtual laboratories will play a key role in biotechnology. Computer experiments will not only replace time-consuming and expensive real-world experiments, but they will also provide insights that cannot be obtained using ``wet'' experiments. The field that deals with the modeling of atoms, molecules, and their reactions is called Molecular Modeling. The advent of Life Sciences gave rise to numerous new developments in this area. However, the implementation of new simulation tools is extremely time-consuming. This is mainly due to the large amount of supporting code ({\eg} for data import/export, visualization, and so on) that is required in addition to the code necessary to implement the new idea. The only way to reduce the development time is to reuse reliable code, preferably using object-oriented approaches. We have designed and implemented {\Ball}, the first object-oriented application framework for rapid prototyping in Molecular Modeling. By the use of the composite design pattern and polymorphism we were able to model the multitude of complex biochemical concepts in a well-structured and comprehensible class hierarchy, the {\Ball} kernel classes. The isomorphism between the biochemical structures and the kernel classes leads to an intuitive interface. Since {\Ball} was designed for rapid software prototyping, ease of use and flexibility were our principal design goals. Besides the kernel classes, {\Ball} provides fundamental components for import/export of data in various file formats, Molecular Mechanics simulations, three-dimensional visualization, and more complex ones like a numerical solver for the Poisson-Boltzmann equation. The usefulness of {\Ball} was shown by the implementation of an algorithm that checks proteins for similarity. Instead of the five months that an earlier implementation took, we were able to implement it within a day using {\Ball}.

Published
1999

43. Entwicklung, Charakterisierung und Evaluierung neuartiger auf Naturstoffen basierender Histondeacetylase-Inhibitoren für die Therapie solider Tumore

Author

Venturelli, S, primary, Berger, A, additional, Weiland, T, additional, Smirnow, I, additional, Schenk, A, additional, Horn, K von, additional, Leischner, C, additional, Weiss, TS, additional, Kämper, A, additional, Kohlbacher, O, additional, Böcker, A, additional, Eickhoff, H, additional, Wiesmüller, KH, additional, Lauer, UM, additional, and Bitzer, M, additional

Published
2012
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45. Identifikation und präklinische Charakterisierung neuartiger epigenetischer Wirkstoffe zur Behandlung therapieresistenter Tumore am Beispiel des Hepatozellulären Karzinoms

Author

Venturelli, S, primary, Horn, K von, additional, Berger, A, additional, Weiland, T, additional, Smirnow, I, additional, Schenk, A, additional, Weiss, TS, additional, Kämper, A, additional, Kohlbacher, O, additional, Gregor, M, additional, Lauer, UM, additional, and Bitzer, M, additional

Published
2009
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47. Metabolomics – eine Strategie zur Identifizierung von prä-diabetischen Stoffwechselveränderungen und neuen Biomarkern?

Author

Lehmann, R, primary, Peter, A, additional, Fekete, A, additional, Lucio, M, additional, Forcisi, S, additional, Schmitt-Kopplin, P, additional, Zhao, X, additional, Xu, G, additional, Fritsche, J, additional, Franken, H, additional, Zell, A, additional, Kenar, E, additional, Kohlbacher, O, additional, Stefan, N, additional, Fritsche, A, additional, and Häring, HU, additional

Published
2009
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