Publikationen des QBiC:

2017

  1. Vizcaíno  et al., (2017). A community proposal to integrate proteomics activities in ELIXIR. F1000Res. 2017 Jun 13;6. pii: ELIXIR-875. doi: 10.12688/f1000research.11751.1.
  2. Backert et al., (2017) A meta-analysis of HLA peptidome composition in different hematological entities: entity-specific dividing lines and "pan-leukemia" antigens. Oncotarget. 2017 Jul 4;8(27):43915-43924. doi: 10.18632/oncotarget.14918.
  3. Vizcaíno  et al., (2017). The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics. Mol Cell Proteomics. 2017 Jul;16(7):1275-1285. doi: 10.1074/mcp.M117.068429.
  4. Schubert  et al., (2017) ImmunoNodes - graphical development of complex immunoinformatics workflows. BMC Bioinformatics. 2017 May 8;18(1):242. doi: 10.1186/s12859-017-1667
  5. Audain et al., (2017). In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics. J Proteomics. 2017 Jan 6;150:170-182. doi: 10.1016/j.jprot.2016.08.002. Epub 2016 Aug 4.
  6. Boyles et al., (2017). Copper oxide nanoparticle toxicity profiling using untargeted metabolomics. Part Fibre Toxicol. 2016 Sep 8;13(1):49. doi: 10.1186/s12989-016-0160-6.
  7. Czemmel et al., (2017) Transcriptome-Wide Identification of Novel UV-B- and Light Modulated Flavonol Pathway Genes Controlled by VviMYBF1. Front Plant Sci. 2017 Jun 22;8:1084. doi: 10.3389/fpls.2017.01084.

2016

  1. Schubert et al., (2016). FRED 2: an immunoinformatics framework for Python. Bioinformatics. 2016 Jul 1;32(13):2044-6. doi: 10.1093/bioinformatics/btw113.
  2. Mueller et al., (2016) BALL-SNPgp-from genetic variants toward computational diagnostics. Bioinformatics. 2016 Jun 15;32(12):1888-90. doi: 10.1093/bioinformatics/btw084.
  3. Kowalewski et al., (2016). Carfilzomib alters the HLA-presented peptidome of myeloma cells and impairs presentation of peptides with aromatic C-termini. Blood Cancer J. 2016 Apr 8;6:e411. doi: 10.1038/bcj.2016.14.
  4. Kohlbacher et al., (2016). Challenges in Large-Scale Computational Mass Spectrometry and Multiomics. J Proteome Res. 2016 Mar 4;15(3):681-2. doi: 10.1021/acs.jproteome.6b00067.
  5. Schubert B, Kohlbacher O. (2016). Designing string-of-beads vaccines with optimal spacers. Genome Med. 2016 Jan 26;8(1):9. doi: 10.1186/s13073-016-0263-6.
  6. Gatto et al., (2016). Testing and Validation of Computational Methods for Mass Spectrometry. J Proteome Res. 2016 Mar 4;15(3):809-14. doi: 10.1021/acs.jproteome.5b00852.
  7. Röst  et al., (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods. 2016  Aug 30;13(9):741-8. doi: 10.1038/nmeth.3959.
  8. Griss et al., (2016). Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets. Nat Methods. 2016 Aug;13(8):651-656. Epub 2016 Jun 27.
  9. Veit et al., (2016). LFQProfiler and RNP(xl): Open-Source Tools for Label-Free Quantification and Protein-RNA Cross-Linking Integrated into Proteome Discoverer. J Proteome Res. 2016 Sep 2;15(9):3441-8. doi: 10.1021/acs.jproteome.6b00407.
  10. Breckels et al., (2016). Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Comput Biol. 2016 May 13;12(5):e1004920. doi: 10.1371/journal.pcbi.1004920.
  11. Ranninger et al., (2016). Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry. Anal Chim Acta. 2016 Aug 3;930:13-22. doi: 10.1016/j.aca.2016.05.017.
  12. Codrea MC, Nahnsen S (2016). Platforms and Pipelines for Proteomics Data Analysis and Management. Adv Exp Med Biol. 2016;919:203-215. Review.
  13. Löffler et al., (2016). Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient. J Hepatol. 2017 Jan;66(1):252-253. doi: 10.1016/j.jhep.2016.10.021
  14. Hesselager et al., (2016). The Pig PeptideAtlas: A resource for systems biology in animal production and biomedicine. Proteomics. 2016 Feb;16(4):634-44. doi: 10.1002/pmic.201500195.
  15. Loyola R et al., (2016). The photomorphogenic factors UV-B RECEPTOR 1, ELONGATED HYPOCOTYL 5, and HY5 HOMOLOGUE are part of the UV-B signalling pathway in grapevine and mediate flavonol accumulation in response to the environment. J Exp Bot. 2016 Oct;67(18):5429-5445.
  16. Sinnberg T et al., (2016) A Nexus Consisting of Beta-Catenin and Stat3 Attenuates BRAF Inhibitor Efficacy and Mediates Acquired Resistance to Vemurafenib. EBioMedicine. 2016 Jun;8:132-149. doi: 10.1016/j.ebiom.2016.04.037
  17. Dammeier et al., (2016). Mass-Spectrometry-Based Proteomics Reveals Organ-Specific Expression Patterns To Be Used as Forensic Evidence. J Proteome Res. 2016 Jan 4;15(1):182-92. doi: 10.1021/acs.jproteome.5b00704. Epub 2015 Dec 8.

 

2015

  1. Aicheler, F, Li, J, Lehmann, R, Xu, G, and Kohlbacher, O, (2015), Retention Time Prediction Improves Identification in Non-Targeted Lipidomics Approaches. Anal Chem. 87(15):7698-704. PMID 26145158
  2. Friedrich, A, Kenar, E, Kohlbacher, O, and Nahnsen, S, (2015), Intuitive Web-based Experimental Design for High-throughput Biomedical Data. BioMed Res Int, 2015:958302. PMID 25954760
  3. Hildebrandt, AK, Stöckel,D, Fischer, N, de la Garza Trevino, L, Krüger, J, Nickels, S, Röttig, M, Schärfe, C,  Schumann, M, Thiel, P, Lenhof, HP, Kohlbacher, O, and Hildebrandt, A, (2015), ballaxy: web services for structural bioinformatics. Bioinformatics, 31(1):121-2. PMID 25183489
  4. Martens, L, Kohlbacher, O, and Weintraub, ST, (2015), Managing Expectations when Publishing Tools and Methods for Computational Proteomics. J. Proteome Res., 14(5):2002-4. PMID 25764342
  5. Proikas-Cezanne, T, Takacs, Z, Dönnes, P, and Kohlbacher, O (2015). WIPI proteins: essential PtdIns3P effectors at the nascent autophagosome, J. Cell Sci., 128 (2):207-217. PMID 25568150
  6. Ranninger, C, Rurik, M, Limonciel, A, Ruzek, S, Reischl, R, Wilmes, A, Jennings, P, Hewitt, P, Dekant, W, Kohlbacher, O, and Huber, CG (2015). Nephron Toxicity Profiling via Untargeted Metabolome Analysis Emplying a High-Performance Liquid Chromatography-Mass Spectrometry-Based Experimental and Computational Pipeline, J. Biol. Chem., 290(31):19121-32. PMID 26055719
  7. Sachsenberg, T, Herbst, F, Taubert, M, Kermer, R, Jehmlich, N, von Bergen, M, Seifert, J, and Kohlbacher, O (2015). MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics, J. Proteome Res., 14(2):619-27. PMID 25412983
  8. Schubert, B, Brachvogel, H, Jürges, C, and Kohlbacher, O (2015). EpiToolKit - A Web-based Workbench for Vaccine Design. Bioinformatics, 31(13):2211-3. PMID 25712691
  9. Thost, A, Dönnes, P, Kohlbacher, O, and Proikas-Cezanne, T (2015). Fluorescence-based imaging of autophagy progression by human WIPI beta-propeller protein detection in single cells. Methods, 75:69-78. PMID 25462558
  10. Ziller, MJ, Edri, R, Yaffe, Y, Donaghey, J, Pop, R, Mallard, W, Issner, R, Gifford, CA, Goren, A, Xing, J, Gu, H, Cacchiarelli, D, Tsankov, AM, Epstein, C, Rinn, JL, Mikkelsen, TS, Kohlbacher, O, Gnirke, A, Bernstein, BE, Elkabetz, Y, and Meissner, A (2015). Dissecting neural differentiation regulatory networks through epigenetic footprinting. Nature, 518:355-359. PMID 25533951
  11. Aicheler, F et al., (2015). Retention Time Prediction Improves Identification in Non-Targeted Lipidomics Approaches. Anal Chem. 87(15):7698-704.
  12. Friedrich, A et al., (2015). Intuitive Web-based Experimental Design for High-throughput Biomedical Data. BioMed Res Int. 2015:958302.
  13. Hildebrandt, AK et al., (2015). ballaxy: web services for structural bioinformatics. Bioinformatics, 31(1):121-2.
  14. Backert L, Kohlbacher O. (2015). Immunoinformatics and epitope prediction in the age of genomic medicine. Genome Med. 2015 Nov 20;7:119. doi: 10.1186/s13073-015-0245-0.
  15. Martens, L, Kohlbacher, O, and Weintraub, ST, (2015), Managing Expectations when Publishing Tools and Methods for Computational Proteomics. J. Proteome Res., 14(5):2002-4.
  16. Proikas-Cezanne, T, Takacs, Z, Kohlbacher, O (2015). WIPI proteins: essential PtdIns3P effectors at the nascent autophagosome. J. Cell Sci., 128 (2):207-217.
  17. Ranninger, C et al., (2015). Nephron Toxicity Profiling via Untargeted Metabolome Analysis Emplying a High-Performance Liquid Chromatography-Mass Spectrometry-Based Experimental and Computational Pipeline. J. Biol. Chem., 290(31):19121-32.
  18. Sachsenberg, T, et al., (2015). MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics, J. Proteome Res., 14(2):619-27.
  19. Schubert, B, Brachvogel, H, Kohlbacher, O (2015). EpiToolKit - A Web-based Workbench for Vaccine Design. Bioinformatics. 31(13):2211-3.
  20. Thost, A, Dönnes, P, Kohlbacher, O, (2015). Fluorescence-based imaging of autophagy progression by human WIPI beta-propeller protein detection in single cells. Methods, 75:69-78.
  21. Ziller, MJ, et al., (2015). Dissecting neural differentiation regulatory networks through epigenetic footprinting. Nature, 518:355-359. 

 2014

  1. Gerasch, A, Faber, D, Küntzer, J, Niermann, P, Kohlbacher, O, Lenhof, H, and Kaufmann, M (2014), BiNA: a visual analytics tool for biological network data. PLoS ONE, 9(2):e87397. PMID: 24551056
  2. Griss, J, et al., (2014), The mzTab Data Exchange Format: communicating MS-based proteomics and metabolomics experimental results to a wider audience. Mol. Cell. Prot.:mcp.O113.036681. PMID: 2498048 
  3. Hopf, TA, et al., (2014).  Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife:10.7554/eLife.03430. PMID: 25255213  
  4. Jordan, E, et al., (2014). Competing Salt Effects on Phase Behavior of Protein Solutions: Tailoring of Protein Interaction by the Binding of Multivalent Ions and Charge Screening. J. Phys. Chem. B, 118(38):11365-74. PMID: 2518081
  5. Kenar, E, Franken, H, Forcisi, S, Wörmann, K, Häring, H, Lehmann, R, Schmitt-Kopplin, P, Zell, A, and Kohlbacher, O (2014). Automated Label-Free Quantification of Metabolites from LC-MS Data. Mol. Cell. Prot., 13(1):348-59. PMID: 24176773
  6. Kramer, K, et al., (2014), Photo-cross linking and high-resolution mass spectrometry for assignment of RNA-binding sites in RNA-binding proteins. Nat. Methods, 11(10):1064-70. PMID: 25173706
  7. Krüger, J, Grunzke, R, Herres-Pawlies, S, de la Garza, L, Kohlbacher, O, Nagel, WE, and Gesing, S (2014), Performance Studies on Distributed Virtual Screening. 2014:624024. PMID: 25032219
  8. Thiel, P, Sach-Peltason, L, Ottmann, C, and Kohlbacher, O (2014), Blocked Inverted Indices for Exact Clustering of Large Chemical Spaces. J. Chem. Inf. Model., 54(9):2395-401. PMID: 25136755
  9. Wagner, R, et al., (2014), Clinical and non-targeted metabolomic profiling of homozygous carries of Transcription Factor 7-like 2 variant rs7903146. Sci. Rep., 4:5296. PMID: 24925104
  10. Walzer, M, et al., (2014), qcML: an exchange format for quality control metrics from mass spectometry experiments. Mol. Cell. Prot., 13(8):1905-13. PMID: 24760958
  11. Szolek A, Schubert B, Mohr C, Sturm M, Feldhahn M, Kohlbacher O, (2014), Opti Type: precision HLA typing from next-generation sequencing data. Bioinformatics. 30 (23):3310-6. PMID: 25143287
  12. Hildebrandt AK, Stöckel D, Fischer NM, de la Garza L, Krüger J, Nickels S, Röttig M, Schärfe C et al., (2014), ballaxy: web services for structural bioinformatics. 31(1):121-2. PMID: 25183489 

2013

  1. Gifford, C.A., et al., Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell, 2013. 153(5): p. 1149-63. PMID:10.1016/j.cell.2013.04.037
  2. Nahnsen, S., T. Sachsenberg, and O. Kohlbacher, PTMeta: Increasing identification rates of modified peptides using modification prescanning and meta-analysis. Proteomics, 2013. 13(6): p. 1042-51. PMID:10.1002/pmic.201200315
  3. Perez-Riverol, Y., et al., Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 workshop report. J Proteomics, 2013. 87: p. 134-8. PMID: 10.1016/j.jprot.2013.01.019
  4. Roosen-Runge, F., et al., Interplay of pH and binding of multivalent metal ions: charge inversion and reentrant condensation in protein solutions. J Phys Chem B, 2013. 117(18): p. 5777-87. PMID: 10.1021/jp401874t
  5. Thiel, P., et al., Virtual screening and experimental validation reveal novel small-molecule inhibitors of 14-3-3 protein-protein interactions. Chem Commun (Camb), 2013. 49(76): p. 8468-70. PMID: 10.1039/c3cc44612c
  6. Walzer, M., et al., The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics. Mol Cell Proteomics, 2013. 12(8): p. 2332-40. PMID: 10.1074/mcp.O113.028506
  7. Ziller, M.J., et al., Charting a dynamic DNA methylation landscape of the human genome. Nature, 2013. 500(7463): p. 477-81. PMID: 10.1038/nature12433
  8. Ahrends, R., et al., Comparison of displacement versus gradient mode for separation of a complex protein mixture by anion-exchange chromatography. J Chromatogr B Analyt Technol Biomed Life Sci, 2012. 901: p. 34-40. PMID: 10.1016/j.jchromb.2012.05.037
  9. Feldhahn, M., et al., miHA-Match: computational detection of tissue-specific minor histocompatibility antigens. J Immunol Methods, 2012. 386(1-2): p. 94-100. PMID: 10.1016/j.jim.2012.09.004
  10. Junker, J., et al., TOPPAS: a graphical workflow editor for the analysis of high-throughput proteomics data. J Proteome Res, 2012. 11(7): p. 3914-20. PMID: 10.1021/pr300187f
  11. Nahnsen, S. and O. Kohlbacher, In silico design of targeted SRM-based experiments. BMC Bioinformatics, 2012. 13 Suppl 16: p. S8. PMID: 10.1186/1471-2105-13-S16-S8

2012

  1. Roglin, L., et al., Covalent attachment of pyridoxal-phosphate derivatives to 14-3-3 proteins. Proc Natl Acad Sci U S A, 2012. 109(18): p. E1051-3; author reply E1054. PMID: 10.1073/pnas.1116592109