Es gibt drei Dinge, die sich nicht vereinen lassen: Intelligenz, Anständigkeit und Nationalsozialismus.
Man kann intelligent und Nazi sein. Dann ist man nicht anständig.
Man kann anständig und Nazi sein. Dann ist man nicht intelligent.
Und man kann anständig und intelligent sein. Dann ist man kein Nazi.
~ Gerhard Bronner ~

Franziska Hufsky

E-Mail: franziska.hufsky*
Room: 08S01
Phone: +49-3641-9-46482

*@uni-jena.de

CV
  • since 2017 Scientific coordinator, European Virus Bioinformatics Center
  • since 2014 Research assistant, RNA Bioinformatics and High Throughput Analysis, Friedrich Schiller University Jena
  • 2014 Doctorate degree Dr. rer. nat, Friedrich Schiller University Jena, Faculty of Mathematics and Computer Science
  • 2010 — 2016 Research assistant, Chair of Bioinformatics, Friedrich Schiller University Jena
  • 2010 — 2013 PhD student fellowship, International Max Planck Research School, Max Planck Institute for Chemical Ecology, Jena
  • 2010 Diploma in Bioinformatics, Friedrich Schiller University Jena
  • 2008 — 2009 Student assistant, Chair of Bioinformatics, Friedrich Schiller University Jena
  • 2007 — 2008 Student assistant, Medical Physics Group, University Hospital Jena
Theses
  • Doctoral Thesis: Novel Methods for the Analysis of Small Molecule Fragmentation Mass Spectra (Supervisor S. Böcker)
  • Diploma Thesis: Algorithm Engineering for the Center String Problem (Supervisor S. Böcker)
Awards
  • 2015 Doctoral Thesis Award of the Dean of the Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena
  • 2014 Excellent computer science dissertation, German Informatics Society (GI)
Research interests
  • Metabolomics and mass spectrometry
    • Significance estimation of spectral database search results
    • Prediction of uncommon elements in unkown biomolecules
    • Local tree alignment for automated comparison of fragmentation trees
    • Fragmentation trees for electron impact mass spectra and multiple MS data
    • Estimation of enrichment of heavy isotopes in labeled proteins using isotope pattern analysis
  • Transcriptomics
    • Viral RNA bioinformatics
    • Effect of long non-coding RNAs during fungal infection
  • Integrated analysis
    • Combined metabolome and transcriptome analysis of the circadian cycle of cyanobacteria
  • Algorithms/Theoretical informatics/NP-hard problems
    • Computing center strings
    • Local tree alignment for automated comparison of fragmentation trees
Grants
  • 2014 — 2016 Combined metabolome and transcriptome analysis of the circadian cycle of cyanobacteria, Postdoctoral fellowship of the Carl-Zeiss-Stiftung, own position and one PhD student
Public relations work
  • regular workshops as part of the Campus Thüringen Tour (since 2013)
  • regular workshops for the Girls’Day (since 2016)
  • creating a station for the Tech Parcour of the Thüringer Universities
  • posters and presentations during the Long Night of Sciences
  • own bioinformatics blog BioinfoWelten
  • own podcast Die 3 Formeltiere
Conference contributions
as organizer
  • 2019 Third Meeting of the European Virus Bioinformatics Centre (Glasgow, UK), Organizer
  • 2017 First Meeting of the European Virus Bioinformatics Centre (Jena, Germany), Organizer
  • 2016 International Study Group for Systems Biology meeting (Jena, Germany), YSGSB Organizer
  • 2012 German Conference on Bioinformatics (Jena, Germany), Organizing committee, Editorial board member
as participant
  • 2016 Metabolomics (Dublin, Ireland), Talk, Significance of metabolite identifications from searching mass spectral libraries
  • 2015 Dagstuhl Seminar on Computational Metabolomics (Dagstuhl, Germany), Invited participant
  • 2014 Herbstseminar der Bioinformatik (Doubice, CZ), Talk, Novel Methods for the Analysis of Small Molecule Fragmentation Mass Spectra
  • 2014 Workshop on Algorithms in Bioinformatics (Wrocław, Poland), Poster, Multiple mass spectrometry fragmentation trees revisited: Boosting performance and quality
  • 2013 Intelligent Systems in Molecular Biology (Berlin, Germany), Poster, De novo analysis of electron ionization mass spectra using fragmentation trees
  • 2012 German Conference on Bioinformatics (Jena, Germany), Talk, Comparing fragmentation trees from electron impact mass spectra with annotated fragmentation pathways
  • 2012 Intelligent Systems in Molecular Biology (Long Beach, CA, USA), Talk, Fast alignment of fragmentation trees
  • 2012 IMPRS Symposium (Jena, Germany), Talk, Automated interpretation of electron impact mass spectra
  • 2011 Conference on Mass Spectrometry and Allied Topics (Denver, Colorado, USA), Poster, Computation of fragmentation trees from metabolite GC/MS data
  • 2011 Conference on Research in Computational Molecular Biology (Vancouver, Canada), Poster, Fragmentation trees from MSn data
  • 2010 Workshop on Algorithms in Bioinformatics (Liverpool, GB), Talk, Swiftly computing center strings
Commissions of trust
  • I am editor of a special issue “Virus Bioinformatics” in Virus Research and a special issue “Virus Bioinformatics” in Viruses.
  • I am editor of conference proceedings of German Conference on Bioinformatics 2012.
  • I have been ad hoc reviewer for several journals and conferences: Journal of Proteome Research, Journal of Mass Spectrometry, Analytical Chemistry, Metabolites, Rapid Communications in Mass Spectrometry, Annual International Conference on Research in Computational Molecular Biology (RECOMB), Annual International Conference on Intelligent Systems for Molecular Biology/ European Conference on Computational Biology (ISMB/ECCB), Workshop on Algorithms in Bioinformatics (WABI), IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB), German Conference on Bioinformatics (GCB), International Workshop on Combinatorial Algorithms (IWOCA).
  • I am founding member of the European Virus Bioinformatics Center.
Publications

Google Scholar

  • [DOI] F. Hufsky, K. Lamkiewicz, A. Almeida, A. Aouacheria, C. Arighi, A. Bateman, J. Baumbach, N. Beerenwinkel, C. Brandt, M. Cacciabue, S. Chuguransky, O. Drechsel, R. D. Finn, A. Fritz, S. Fuchs, G. Hattab, A. Hauschild, D. Heider, M. Hoffmann, M. Hölzer, S. Hoops, L. Kaderali, I. Kalvari, M. von Kleist, R. Kmiecinski, D. Kühnert, G. Lasso, P. Libin, M. List, H. F. Löchel, M. J. Martin, R. Martin, J. Matschinske, A. C. McHardy, P. Mendes, J. Mistry, V. Navratil, E. Nawrocki, Á. N. O’Toole, N. Palacios-Ontiveros, A. I. Petrov, G. Rangel-Piñeros, N. Redaschi, S. Reimering, K. Reinert, A. Reyes, L. Richardson, D. L. Robertson, S. Sadegh, J. B. Singer, K. Theys, C. Upton, M. Welzel, L. Williams, and M. Marz, “Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research,” Perprints, 2020.
    [Bibtex] 
    @Article{Hufsky:20,
author = {Franziska Hufsky and Kevin Lamkiewicz and Alexandre Almeida and Abdel Aouacheria and Cecilia Arighi and Alex Bateman and Jan Baumbach and Niko Beerenwinkel and Christian Brandt and Marco Cacciabue and Sara Chuguransky and Oliver Drechsel and Robert D. Finn and Adrian Fritz and Stephan Fuchs and Georges Hattab and Anne-Christin Hauschild and Dominik Heider and Marie Hoffmann and Martin Hölzer and Stefan Hoops and Lars Kaderali and Ioanna Kalvari and Max von Kleist and Rene Kmiecinski and Denise Kühnert and Gorka Lasso and Pieter Libin and Markus List and Hannah F. Löchel and Maria J. Martin and Roman Martin and Julian Matschinske and Alice C. McHardy and Pedro Mendes and Jaina Mistry and Vincent Navratil and Eric Nawrocki and {\'{A}}ine Niamh O'Toole and Nancy Palacios-Ontiveros and Anton I. Petrov and Guillermo Rangel-Pi{\~{n}}eros and Nicole Redaschi and Susanne Reimering and Knut Reinert and Alejandro Reyes and Lorna Richardson and David L. Robertson and Sepideh Sadegh and Joshua B. Singer and Kristof Theys and Chris Upton and Marius Welzel and Lowri Williams and Manja Marz},
title = {Computational Strategies to Combat {COVID}-19: Useful Tools to Accelerate {SARS}-{CoV}-2 and Coronavirus Research},
journal = {Perprints},
year = {2020},
doi = {10.20944/preprints202005.0376.v1},
publisher = {{MDPI} {AG}},
}
  • [DOI] B. Ibrahim, D. P. McMahon, F. Hufsky, M. Beer, L. Deng, P. L. Mercier, M. Palmarini, V. Thiel, and M. Marz, “A new era of virus bioinformatics,” Virus Res, vol. 251, p. 86–90, 2018.
    [Bibtex] 
    @Article{Ibrahim:18a,
author = {Ibrahim, Bashar and McMahon, Dino P and Hufsky, Franziska and Beer, Martin and Deng, Li and Mercier, Philippe Le and Palmarini, Massimo and Thiel, Volker and Marz, Manja},
title = {A new era of virus bioinformatics},
journal = {{Virus Res}},
year = {2018},
volume = {251},
pages = {86--90},
abstract = {Despite the recognized excellence of virology and bioinformatics, these two communities have interacted surprisingly sporadically, aside from some pioneering work on HIV-1 and influenza. Bringing together the expertise of bioinformaticians and virologists is crucial, since very specific but fundamental computational approaches are required for virus research, particularly in an era of big data. Collaboration between virologists and bioinformaticians is necessary to improve existing analytical tools, cloud-based systems, computational resources, data sharing approaches, new diagnostic tools, and bioinformatic training. Here, we highlight current progress and discuss potential avenues for future developments in this promising era of virus bioinformatics. We end by presenting an overview of current technologies, and by outlining some of the major challenges and advantages that bioinformatics will bring to the field of virology.},
doi = {10.1016/j.virusres.2018.05.009},
keywords = {Computational Biology, methods, trends; Virology, methods, trends; Viruses, genetics, growth & development; Bioinformatics; Software; Virology; Viruses},
pmid = {29751021},
}
  • [DOI] F. Hufsky, B. Ibrahim, M. Beer, L. Deng, P. L. Mercier, D. P. McMahon, M. Palmarini, V. Thiel, and M. Marz, “Virologists-heroes need weapons,” PLoS Pathog, vol. 14, p. e1006771, 2018.
    [Bibtex] 
    @Article{Hufsky:18,
author = {Hufsky, Franziska and Ibrahim, Bashar and Beer, Martin and Deng, Li and Mercier, Philippe Le and McMahon, Dino P and Palmarini, Massimo and Thiel, Volker and Marz, Manja},
title = {Virologists-Heroes need weapons},
journal = {{PLoS Pathog}},
year = {2018},
volume = {14},
pages = {e1006771},
doi = {10.1371/journal.ppat.1006771},
issue = {2},
keywords = {Animals; Biomedical Research, manpower, methods, trends; Computational Biology, manpower, methods, trends; Europe; Humans; Professional Role; Virology, manpower, methods, trends},
pmid = {29420617},
}
  • [DOI] K. Riege, M. Hölzer, T. E. Klassert, E. Barth, J. Bräuer, M. Collatz, F. Hufsky, N. Mostajo, M. Stock, B. Vogel, H. Slevogt, and M. Marz, “Massive effect on lncRNAs in human monocytes during fungal and bacterial infections and in response to vitamins A and D,” Sci Rep, vol. 7, p. 40598, 2017.
    [Bibtex] 
    @Article{Riege:17,
author = {Riege, Konstantin and H\"{o}lzer, Martin and Klassert, Tilman E and Barth, Emanuel and Br\"{a}uer, Julia and Collatz, Maximilian and Hufsky, Franziska and Mostajo, Nelly and Stock, Magdalena and Vogel, Bertram and Slevogt, Hortense and Marz, Manja},
title = {Massive Effect on Lnc{RNA}s in Human Monocytes During Fungal and Bacterial Infections and in Response to Vitamins {A} and {D}},
journal = {{Sci Rep}},
year = {2017},
volume = {7},
pages = {40598},
abstract = {Mycoses induced by C.albicans or A.fumigatus can cause important host damage either by deficient or exaggerated immune response. Regulation of chemokine and cytokine signaling plays a crucial role for an adequate inflammation, which can be modulated by vitamins A and D. Non-coding RNAs (ncRNAs) as transcription factors or cis-acting antisense RNAs are known to be involved in gene regulation. However, the processes during fungal infections and treatment with vitamins in terms of therapeutic impact are unknown. We show that in monocytes both vitamins regulate ncRNAs involved in amino acid metabolism and immune system processes using comprehensive RNA-Seq analyses. Compared to protein-coding genes, fungi and bacteria induced an expression change in relatively few ncRNAs, but with massive fold changes of up to 4000. We defined the landscape of long-ncRNAs (lncRNAs) in response to pathogens and observed variation in the isoforms composition for several lncRNA following infection and vitamin treatment. Most of the involved antisense RNAs are regulated and positively correlated with their sense protein-coding genes. We investigated lncRNAs with stimulus specific immunomodulatory activity as potential marker genes: LINC00595, SBF2-AS1 (A.fumigatus) and RP11-588G21.2, RP11-394l13.1 (C.albicans) might be detectable in the early phase of infection and serve as therapeutic targets in the future.},
doi = {10.1038/srep40598},
keywords = {Bacterial Infections, genetics, microbiology; Gene Expression Regulation, drug effects; Humans; Monocytes, metabolism; Mycoses, genetics, microbiology; RNA, Antisense, genetics; RNA, Long Noncoding, chemistry, genetics; RNA, Messenger, genetics; RNA, Untranslated, genetics; Vitamin A, metabolism, pharmacology; Vitamin D, metabolism, pharmacology},
pmid = {28094339},
}
  • [DOI] M. Hölzer, V. Krähling, F. Amman, E. Barth, S. H. Bernhart, V. A. O. Carmelo, M. Collatz, G. Doose, F. Eggenhofer, J. Ewald, J. Fallmann, L. M. Feldhahn, M. Fricke, J. Gebauer, A. J. Gruber, F. Hufsky, H. Indrischek, S. Kanton, J. Linde, N. Mostajo, R. Ochsenreiter, K. Riege, L. Rivarola-Duarte, A. H. Sahyoun, S. J. Saunders, S. E. Seemann, A. Tanzer, B. Vogel, S. Wehner, M. T. Wolfinger, R. Backofen, J. Gorodkin, I. Grosse, I. Hofacker, S. Hoffmann, C. Kaleta, P. F. Stadler, S. Becker, and M. Marz, “Differential transcriptional responses to Ebola and Marburg virus infection in bat and human cells,” Sci Rep, vol. 6, p. 34589, 2016.
    [Bibtex] 
    @Article{Hoelzer:16,
author = {H\"{o}lzer, Martin and Kr\"{a}hling, Verena and Amman, Fabian and Barth, Emanuel and Bernhart, Stephan H and Carmelo, Victor A O and Collatz, Maximilian and Doose, Gero and Eggenhofer, Florian and Ewald, Jan and Fallmann, J\"{o}rg and Feldhahn, Lasse M and Fricke, Markus and Gebauer, Juliane and Gruber, Andreas J and Hufsky, Franziska and Indrischek, Henrike and Kanton, Sabina and Linde, J\"{o}rg and Mostajo, Nelly and Ochsenreiter, Roman and Riege, Konstantin and Rivarola-Duarte, Lorena and Sahyoun, Abdullah H and Saunders, Sita J and Seemann, Stefan E and Tanzer, Andrea and Vogel, Bertram and Wehner, Stefanie and Wolfinger, Michael T and Backofen, Rolf and Gorodkin, Jan and Grosse, Ivo and Hofacker, Ivo and Hoffmann, Steve and Kaleta, Christoph and Stadler, Peter F and Becker, Stephan and Marz, Manja},
title = {Differential transcriptional responses to {E}bola and {M}arburg virus infection in bat and human cells},
journal = {{Sci Rep}},
year = {2016},
volume = {6},
pages = {34589},
abstract = {The unprecedented outbreak of Ebola in West Africa resulted in over 28,000 cases and 11,000 deaths, underlining the need for a better understanding of the biology of this highly pathogenic virus to develop specific counter strategies. Two filoviruses, the Ebola and Marburg viruses, result in a severe and often fatal infection in humans. However, bats are natural hosts and survive filovirus infections without obvious symptoms. The molecular basis of this striking difference in the response to filovirus infections is not well understood. We report a systematic overview of differentially expressed genes, activity motifs and pathways in human and bat cells infected with the Ebola and Marburg viruses, and we demonstrate that the replication of filoviruses is more rapid in human cells than in bat cells. We also found that the most strongly regulated genes upon filovirus infection are chemokine ligands and transcription factors. We observed a strong induction of the JAK/STAT pathway, of several genes encoding inhibitors of MAP kinases (DUSP genes) and of PPP1R15A, which is involved in ER stress-induced cell death. We used comparative transcriptomics to provide a data resource that can be used to identify cellular responses that might allow bats to survive filovirus infections.},
doi = {10.1038/srep34589},
keywords = {Animals; Cell Line, Tumor; Chiroptera; Ebolavirus, metabolism; Gene Expression Regulation; Hemorrhagic Fever, Ebola, metabolism; Humans; Marburg Virus Disease, metabolism; Marburgvirus, metabolism; Signal Transduction; Transcription, Genetic},
pmid = {27713552},
}
  • [DOI] F. Hufsky, B. Ibrahim, S. Modha, M. R. J. Clokie, S. Deinhardt-Emmer, B. E. Dutilh, S. Lycett, P. Simmonds, V. Thiel, A. Abroi, E. M. Adriaenssens, M. Escalera-Zamudio, J. N. Kelly, K. Lamkiewicz, L. Lu, J. Susat, T. Sicheritz, D. L. Robertson, and M. Marz, “The third annual meeting of the European Virus Bioinformatics Center,” Viruses, vol. 11, iss. 5, p. 420, 2019.
    [Bibtex] 
    @Article{Hufsky:19,
author = {Franziska Hufsky and Bashar Ibrahim and Sejal Modha and Martha R. J. Clokie and Stefanie Deinhardt-Emmer and Bas E. Dutilh and Samantha Lycett and Peter Simmonds and Volker Thiel and Aare Abroi and Evelien M. Adriaenssens and Marina Escalera-Zamudio and Jenna Nicole Kelly and Kevin Lamkiewicz and Lu Lu and Julian Susat and Thomas Sicheritz and David L. Robertson and Manja Marz},
title = {The Third Annual Meeting of the {E}uropean {V}irus {B}ioinformatics {C}enter},
journal = {Viruses},
year = {2019},
volume = {11},
number = {5},
pages = {420},
doi = {10.3390/v11050420},
publisher = {{MDPI} {AG}},
}
Publications in Previous Groups
  • [DOI] K. Scheubert, F. Hufsky, D. Petras, M. Wang, L. Nothias, K. Dührkop, N. Bandeira, P. C. Dorrestein, and S. Böcker, “Significance estimation for large scale metabolomics annotations by spectral matching,” Nat Commun, vol. 8, p. 1494, 2017.
    [Bibtex] 
    @Article{Scheubert:17,
author = {Scheubert, Kerstin and Hufsky, Franziska and Petras, Daniel and Wang, Mingxun and Nothias, Louis-F\'{e}lix and D\"{u}hrkop, Kai and Bandeira, Nuno and Dorrestein, Pieter C and B\"{o}cker, Sebastian},
title = {Significance estimation for large scale metabolomics annotations by spectral matching},
journal = {{Nat Commun}},
year = {2017},
volume = {8},
pages = {1494},
abstract = {The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.},
doi = {10.1038/s41467-017-01318-5},
issue = {1},
keywords = {Algorithms; Chromatography, Liquid; Computational Biology, methods; Databases, Protein; Metabolomics; Tandem Mass Spectrometry, methods},
pmid = {29133785},
}
  • [DOI] M. Meusel, F. Hufsky, F. Panter, D. Krug, R. Müller, and S. Böcker, “Predicting the presence of uncommon elements in unknown biomolecules from isotope patterns,” Anal Chem, vol. 88, p. 7556–7566, 2016.
    [Bibtex] 
    @Article{Meusel:16,
author = {Meusel, Marvin and Hufsky, Franziska and Panter, Fabian and Krug, Daniel and M\"{u}ller, Rolf and B\"{o}cker, Sebastian},
title = {Predicting the Presence of Uncommon Elements in Unknown Biomolecules from Isotope Patterns},
journal = {{Anal Chem}},
year = {2016},
volume = {88},
pages = {7556--7566},
abstract = {The determination of the molecular formula is one of the earliest and most important steps when investigating the chemical nature of an unknown compound. Common approaches use the isotopic pattern of a compound measured using mass spectrometry. Computational methods to determine the molecular formula from this isotopic pattern require a fixed set of elements. Considering all possible elements severely increases running times and more importantly the chance for false positive identifications as the number of candidate formulas for a given target mass rises significantly if the constituting elements are not prefiltered. This negative effect grows stronger for compounds of higher molecular mass as the effect of a single atom on the overall isotopic pattern grows smaller. On the other hand, hand-selected restrictions on this set of elements may prevent the identification of the correct molecular formula. Thus, it is a crucial step to determine the set of elements most likely comprising the compound prior to the assignment of an elemental formula to an exact mass. In this paper, we present a method to determine the presence of certain elements (sulfur, chlorine, bromine, boron, and selenium) in the compound from its (high mass accuracy) isotopic pattern. We limit ourselves to biomolecules, in the sense of products from nature or synthetic products with potential bioactivity. The classifiers developed here predict the presence of an element with a very high sensitivity and high specificity. We evaluate classifiers on three real-world data sets with 663 isotope patterns in total: 184 isotope patterns containing sulfur, 187 containing chlorine, 14 containing bromine, one containing boron, one containing selenium. In no case do we make a false negative prediction; for chlorine, bromine, boron, and selenium, we make ten false positive predictions in total. We also demonstrate the impact of our method on the identification of molecular formulas, in particular on the number of considered candidates and running time. The element prediction will be part of the next SIRIUS release, available from https://bio.informatik.uni-jena.de/software/sirius/ .},
doi = {10.1021/acs.analchem.6b01015},
issue = {15},
keywords = {Algorithms; Chemical Phenomena; Datasets as Topic; Elements; Isotopes, chemistry; Machine Learning; Mass Spectrometry; Molecular Weight},
pmid = {27398867},
}
  • [DOI] F. Hufsky and S. Böcker, “Mining molecular structure databases: identification of small molecules based on fragmentation mass spectrometry data.,” Mass Spectrom Rev, vol. 36, p. 624–633, 2017.
    [Bibtex] 
    @Article{Hufsky:17,
author = {Hufsky, Franziska and B\"{o}cker, Sebastian},
title = {Mining molecular structure databases: Identification of small molecules based on fragmentation mass spectrometry data.},
journal = {{Mass Spectrom Rev}},
year = {2017},
volume = {36},
pages = {624--633},
abstract = {Mass spectrometry (MS) is a key technology for the analysis of small molecules. For the identification and structural elucidation of novel molecules, new approaches beyond straightforward spectral comparison are required. In this review, we will cover computational methods that help with the identification of small molecules by analyzing fragmentation MS data. We focus on the four main approaches to mine a database of metabolite structures, that is rule-based fragmentation spectrum prediction, combinatorial fragmentation, competitive fragmentation modeling, and molecular fingerprint prediction. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:624-633, 2017.},
doi = {10.1002/mas.21489},
issue = {5},
keywords = {computational methods; mass spectrometry; mining metabolite structures; structural elucidation},
pmid = {26763615},
}
  • [DOI] F. Hufsky, K. Scheubert, and S. Böcker, “New kids on the block: novel informatics methods for natural product discovery.,” Nat Prod Rep, vol. 31, p. 807–817, 2014.
    [Bibtex] 
    @Article{Hufsky:14,
author = {Hufsky, Franziska and Scheubert, Kerstin and B\"{o}cker, Sebastian},
title = {New kids on the block: novel informatics methods for natural product discovery.},
journal = {{Nat Prod Rep}},
year = {2014},
volume = {31},
pages = {807--817},
abstract = {Covering: 2008 to 2014 Mass spectrometry is a key technology for the identification and structural elucidation of natural products. Manual interpretation of the resulting data is tedious and time-consuming, so methods for automated analysis are highly sought after. In this review, we focus on four recently developed methods for the detection and investigation of small molecules, namely MetFrag/MetFusion, ISIS, FingerID, and FT-BLAST. These methods have the potential to significantly advance the field of computational mass spectrometry for the research of natural products. For example, they may help with the dereplication of compounds at an early stage of the drug discovery process; that is, the detection of molecules that are identical or highly similar to known drugs or drug leads. Furthermore, when a potential drug lead has been determined, these tools may help to identify it and elucidate its structure.},
doi = {10.1039/c3np70101h},
issue = {6},
keywords = {Biological Products, analysis, chemistry; Drug Discovery; Humans; Mass Spectrometry, methods; Molecular Structure; Small Molecule Libraries},
pmid = {24752343},
}
  • [DOI] K. Scheubert, F. Hufsky, and S. Böker, “Computational mass spectrometry for small molecules,” J Cheminf, vol. 5, p. 12, 2013.
    [Bibtex] 
    @Article{Scheubert:13,
author = {Scheubert, Kerstin and Hufsky, Franziska and B\"{o}ker, Sebastian},
title = {Computational mass spectrometry for small molecules},
journal = {{J Cheminf}},
year = {2013},
volume = {5},
pages = {12},
abstract = {: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.},
doi = {10.1186/1758-2946-5-12},
issue = {1},
pmid = {23453222},
}
  • [DOI] K. Scheubert, F. Hufsky, F. Rasche, and S. Böcker, “Computing fragmentation trees from metabolite multiple mass spectrometry data,” J Comput Biol, vol. 18, p. 1383–1397, 2011.
    [Bibtex] 
    @Article{Scheubert:11,
author = {Scheubert, Kerstin and Hufsky, Franziska and Rasche, Florian and B\"{o}cker, Sebastian},
title = {Computing fragmentation trees from metabolite multiple mass spectrometry data},
journal = {{J Comput Biol}},
year = {2011},
volume = {18},
pages = {1383--1397},
abstract = {Since metabolites cannot be predicted from the genome sequence, high-throughput de novo identification of small molecules is highly sought. Mass spectrometry (MS) in combination with a fragmentation technique is commonly used for this task. Unfortunately, automated analysis of such data is in its infancy. Recently, fragmentation trees have been proposed as an analysis tool for such data. Additional fragmentation steps (MS(n)) reveal more information about the molecule. We propose to use MS(n) data for the computation of fragmentation trees, and present the Colorful Subtree Closure problem to formalize this task: There, we search for a colorful subtree inside a vertex-colored graph, such that the weight of the transitive closure of the subtree is maximal. We give several negative results regarding the tractability and approximability of this and related problems. We then present an exact dynamic programming algorithm, which is parameterized by the number of colors in the graph and is swift in practice. Evaluation of our method on a dataset of 45 reference compounds showed that the quality of constructed fragmentation trees is improved by using MS(n) instead of MS² measurements.},
doi = {10.1089/cmb.2011.0168},
issue = {11},
keywords = {Algorithms; Data Interpretation, Statistical; Mass Spectrometry, methods, standards; Metabolome; Models, Chemical; Molecular Weight; Reference Standards},
pmid = {22035289},
}
  • [DOI] F. Rasche, K. Scheubert, F. Hufsky, T. Zichner, M. Kai, A. Svatoš, and S. Böcker, “Identifying the unknowns by aligning fragmentation trees,” Anal Chem, vol. 84, p. 3417–3426, 2012.
    [Bibtex] 
    @Article{Rasche:12,
author = {Rasche, Florian and Scheubert, Kerstin and Hufsky, Franziska and Zichner, Thomas and Kai, Marco and Svato\v{s}, Ale\v{s} and B\"{o}cker, Sebastian},
title = {Identifying the unknowns by aligning fragmentation trees},
journal = {{Anal Chem}},
year = {2012},
volume = {84},
pages = {3417--3426},
abstract = {Mass spectrometry allows sensitive, automated, and high-throughput analysis of small molecules. In principle, tandem mass spectrometry allows us to identify "unknown" small molecules not in any database, but the automated interpretation of such data is in its infancy. Fragmentation trees have recently been introduced for the automated analysis of the fragmentation patterns of small molecules. We present a method for the automated comparison of such fragmentation patterns, based on aligning the compounds' fragmentation trees. We cluster compounds based solely on their fragmentation patterns and show a good agreement with known compound classes. Fragmentation pattern similarities are strongly correlated with the chemical similarity of molecules. We present a tool for searching a database for compounds with fragmentation pattern similar to an unknown sample compound. We apply this tool to metabolites from Icelandic poppy. Our method allows fully automated computational identification of small molecules that cannot be found in any database.},
doi = {10.1021/ac300304u},
issue = {7},
keywords = {Cluster Analysis; Databases, Factual; Mass Spectrometry, methods; Papaver, chemistry; Statistics as Topic, methods},
pmid = {22390817},
}
  • [DOI] F. Hufsky, M. Rempt, F. Rasche, G. Pohnert, and S. Böcker, “De novo analysis of electron impact mass spectra using fragmentation trees,” Anal Chim Acta, vol. 739, p. 67–76, 2012.
    [Bibtex] 
    @Article{Hufsky:12,
author = {Hufsky, Franziska and Rempt, Martin and Rasche, Florian and Pohnert, Georg and B\"{o}cker, Sebastian},
title = {De novo analysis of electron impact mass spectra using fragmentation trees},
journal = {{Anal Chim Acta}},
year = {2012},
volume = {739},
pages = {67--76},
abstract = {The automated fragmentation analysis of high resolution EI mass spectra based on a fragmentation tree algorithm is introduced. Fragmentation trees are constructed from EI spectra by automated signal extraction and evaluation. These trees explain relevant fragmentation reactions and assign molecular formulas to fragments. The method enables the identification of the molecular ion and the molecular formula of a metabolite if the molecular ion is present in the spectrum. These identifications are independent of existing library knowledge and, thus, support assignment and structural elucidation of unknown compounds. The method works even if the molecular ion is of very low abundance or hidden under contaminants with higher masses. We apply the algorithm to a selection of 50 derivatized and underivatized metabolites and demonstrate that in 78% of cases the molecular ion can be correctly assigned. The automatically constructed fragmentation trees correspond very well to published mechanisms and allow the assignment of specific relevant fragments and fragmentation pathways even in the most complex EI-spectra in our dataset. This method will be very helpful in the automated analysis of metabolites that are not included in common libraries and it thus has the potential to support the explorative character of metabolomics studies.},
doi = {10.1016/j.aca.2012.06.021},
keywords = {Algorithms; Cluster Analysis; Databases, Factual; Gas Chromatography-Mass Spectrometry, methods; Metabolomics, methods; Models, Chemical},
pmid = {22819051},
}
  • [DOI] F. Hufsky, L. Kuchenbecker, K. Jahn, J. Stoye, and S. Böcker, “Swiftly computing center strings,” BMC Bioinf, vol. 12, p. 106, 2011.
    [Bibtex] 
    @Article{Hufsky:11,
author = {Hufsky, Franziska and Kuchenbecker, Léon and Jahn, Katharina and Stoye, Jens and B\"{o}cker, Sebastian},
title = {Swiftly computing center strings},
journal = {{BMC Bioinf}},
year = {2011},
volume = {12},
pages = {106},
abstract = {The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete. In this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is efficient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application. We find that the running time for computing the optimal center string is dominated by the subroutine calls for d = dopt -1 and d = dopt. Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably.},
doi = {10.1186/1471-2105-12-106},
keywords = {Algorithms; Bacteria, genetics; Cluster Analysis; Computational Biology, methods; Genome, Bacterial; Genomics, methods; Models, Genetic},
pmid = {21504573},
}
  • [DOI] F. Hufsky, K. Dührkop, F. Rasche, M. Chimani, and S. Böcker, “Fast alignment of fragmentation trees,” Bioinformatics, vol. 28, p. i265–i273, 2012.
    [Bibtex] 
    @Article{Hufsky:12a,
author = {Hufsky, Franziska and Dührkop, Kai and Rasche, Florian and Chimani, Markus and B\"{o}cker, Sebastian},
title = {Fast alignment of fragmentation trees},
journal = {Bioinformatics},
year = {2012},
volume = {28},
pages = {i265--i273},
abstract = {Mass spectrometry allows sensitive, automated and high-throughput analysis of small molecules such as metabolites. One major bottleneck in metabolomics is the identification of 'unknown' small molecules not in any database. Recently, fragmentation tree alignments have been introduced for the automated comparison of the fragmentation patterns of small molecules. Fragmentation pattern similarities are strongly correlated with the chemical similarity of the molecules, and allow us to cluster compounds based solely on their fragmentation patterns. Aligning fragmentation trees is computationally hard. Nevertheless, we present three exact algorithms for the problem: a dynamic programming (DP) algorithm, a sparse variant of the DP, and an Integer Linear Program (ILP). Evaluation of our methods on three different datasets showed that thousands of alignments can be computed in a matter of minutes using DP, even for 'challenging' instances. Running times of the sparse DP were an order of magnitude better than for the classical DP. The ILP was clearly outperformed by both DP approaches. We also found that for both DP algorithms, computing the 1% slowest alignments required as much time as computing the 99% fastest.},
doi = {10.1093/bioinformatics/bts207},
issue = {12},
keywords = {Algorithms; Computational Biology, methods; Databases, Factual; Mass Spectrometry; Metabolomics, methods},
pmid = {22689771},
}
  • [DOI] K. Dührkop, F. Hufsky, and S. Böcker, “Molecular formula identification using isotope pattern analysis and calculation of fragmentation trees.,” Mass Spectrom, vol. 3, p. S0037, 2014.
    [Bibtex] 
    @Article{Duehrkop:14,
author = {D\"{u}hrkop, Kai and Hufsky, Franziska and B\"{o}cker, Sebastian},
title = {Molecular Formula Identification Using Isotope Pattern Analysis and Calculation of Fragmentation Trees.},
journal = {{Mass Spectrom}},
year = {2014},
volume = {3},
pages = {S0037},
abstract = {We present the results of a fully automated de novo approach for identification of molecular formulas in the CASMI 2013 contest. Only results for Category 1 (molecular formula identification) were submitted. Our approach combines isotope pattern analysis and fragmentation pattern analysis and is completely independent from any (spectral and structural) database. We correctly identified the molecular formula for ten out of twelve challenges, being the best automated method competing in this category. },
doi = {10.5702/massspectrometry.S0037},
issue = {Spec Iss 2},
keywords = {CASMI 2013; fragmentation patterns; fragmentation trees; isotope patterns; mass spectrometry; molecular formula identification; small molecules},
pmid = {26819880},
}
  • [DOI] L. Ullmann-Zeunert, A. Muck, N. Wielsch, F. Hufsky, M. A. Stanton, S. Bartram, S. Böcker, I. T. Baldwin, K. Groten, and A. Svatoš, “Determination of ¹⁵N-incorporation into plant proteins and their absolute quantitation: a new tool to study nitrogen flux dynamics and protein pool sizes elicited by plant-herbivore interactions,” J Proteome Res, vol. 11, p. 4947–4960, 2012.
    [Bibtex] 
    @Article{Ullmann-Zeunert:12,
author = {Ullmann-Zeunert, Lynn and Muck, Alexander and Wielsch, Natalie and Hufsky, Franziska and Stanton, Mariana A and Bartram, Stefan and B\"{o}cker, Sebastian and Baldwin, Ian T and Groten, Karin and Svato\v{s}, Ale\v{s}},
title = {Determination of ¹⁵{N}-incorporation into plant proteins and their absolute quantitation: a new tool to study nitrogen flux dynamics and protein pool sizes elicited by plant-herbivore interactions},
journal = {{J Proteome Res}},
year = {2012},
volume = {11},
pages = {4947--4960},
abstract = {Herbivory leads to changes in the allocation of nitrogen among different pools and tissues; however, a detailed quantitative analysis of these changes has been lacking. Here, we demonstrate that a mass spectrometric data-independent acquisition approach known as LC-MS(E), combined with a novel algorithm to quantify heavy atom enrichment in peptides, is able to quantify elicited changes in protein amounts and (15)N flux in a high throughput manner. The reliable identification/quantitation of rabbit phosphorylase b protein spiked into leaf protein extract was achieved. The linear dynamic range, reproducibility of technical and biological replicates, and differences between measured and expected (15)N-incorporation into the small (SSU) and large (LSU) subunits of ribulose-1,5-bisphosphate-carboxylase/oxygenase (RuBisCO) and RuBisCO activase 2 (RCA2) of Nicotiana attenuata plants grown in hydroponic culture at different known concentrations of (15)N-labeled nitrate were used to further evaluate the procedure. The utility of the method for whole-plant studies in ecologically realistic contexts was demonstrated by using (15)N-pulse protocols on plants growing in soil under unknown (15)N-incorporation levels. Additionally, we quantified the amounts of lipoxygenase 2 (LOX2) protein, an enzyme important in antiherbivore defense responses, demonstrating that the approach allows for in-depth quantitative proteomics and (15)N flux analyses of the metabolic dynamics elicited during plant-herbivore interactions.},
doi = {10.1021/pr300465n},
issue = {10},
keywords = {Algorithms; Amino Acid Sequence; Animals; Bayes Theorem; Chromatography, Liquid, standards; Herbivory; Likelihood Functions; Lipoxygenase, chemistry, isolation & purification, metabolism; Molecular Sequence Data; Nitrogen, metabolism; Nitrogen Isotopes, metabolism; Peptide Fragments, chemistry; Peptide Mapping, standards; Phosphorylase b, chemistry; Plant Extracts, chemistry, isolation & purification; Plant Leaves, chemistry, metabolism; Plant Proteins, chemistry, isolation & purification, metabolism; Rabbits; Reference Standards; Ribulose-Bisphosphate Carboxylase, chemistry, isolation & purification, metabolism; Spectrometry, Mass, Electrospray Ionization, standards; Tandem Mass Spectrometry, standards; Tobacco, chemistry, metabolism},
pmid = {22905865},
}
  • [DOI] F. Hufsky, K. Scheubert, and S. Böcker, “Computational mass spectrometry for small-molecule fragmentation,” Trends anal chem, vol. 53, p. 41–48, 2014.
    [Bibtex] 
    @Article{Hufsky:14a,
author = {Franziska Hufsky and Kerstin Scheubert and Sebastian B\"{o}cker},
title = {Computational mass spectrometry for small-molecule fragmentation},
journal = {Trends Anal Chem},
year = {2014},
volume = {53},
pages = {41--48},
doi = {10.1016/j.trac.2013.09.008},
publisher = {Elsevier {BV}},
}
  • [DOI] K. Scheubert, F. Hufsky, and S. Böcker, “Multiple mass spectrometry fragmentation trees revisited: boosting performance and quality,” in Proc. of workshop on algorithms in bioinformatics (wabi 2014), Springer berlin heidelberg, 2014, vol. 8701, p. 217–231.
    [Bibtex] 
    @InCollection{Scheubert:14,
author = {Kerstin Scheubert and Franziska Hufsky and Sebastian B\"{o}cker},
title = {Multiple Mass Spectrometry Fragmentation Trees Revisited: Boosting Performance and Quality},
booktitle = {Proc. of Workshop on Algorithms in Bioinformatics (WABI 2014)},
publisher = {Springer Berlin Heidelberg},
year = {2014},
volume = {8701},
series = {Lect Notes Comput Sci},
pages = {217--231},
doi = {10.1007/978-3-662-44753-6_17},
}
  • [DOI] F. Hufsky and S. Böcker, “Comparing fragmentation trees from electron impact mass spectra with annotated fragmentation pathways,” in German conference on bioinformatics 2012, GCB 2012, jena, germany, september 20-22, 2012., 2012, p. 12–22.
    [Bibtex] 
    @InProceedings{Hufsky:12b,
author = {Hufsky, Franziska and B\"{o}cker, Sebastian},
title = {Comparing Fragmentation Trees from Electron Impact Mass Spectra with Annotated Fragmentation Pathways},
booktitle = {German Conference on Bioinformatics 2012, {GCB} 2012, Jena, Germany, September 20-22, 2012.},
year = {2012},
pages = {12--22},
doi = {10.4230/oasics.gcb.2012.12},
keywords = {Computer Science, 000 Computer science, knowledge, general works},
}
  • [DOI] M. Ludwig, F. Hufsky, S. Elshamy, and S. Böcker, “Finding characteristic substructures for metabolite classes,” in German conference on bioinformatics 2012, GCB 2012, jena, germany, september 20-22, 2012., 2012, p. 23–38.
    [Bibtex] 
    @InProceedings{Ludwig:12,
author = {Ludwig, Marcus and Hufsky, Franziska and Elshamy, Samy and B\"{o}cker, Sebastian},
title = {Finding Characteristic Substructures for Metabolite Classes},
booktitle = {German Conference on Bioinformatics 2012, {GCB} 2012, Jena, Germany, September 20-22, 2012.},
year = {2012},
pages = {23--38},
doi = {10.4230/oasics.gcb.2012.23},
keywords = {Computer Science, 000 Computer science, knowledge, general works},
}
  • [DOI] K. Scheubert, F. Hufsky, F. Rasche, and S. Böcker, “Computing fragmentation trees from metabolite multiple mass spectrometry data,” in Proc. of research in computational molecular biology (recomb 2011), Springer berlin heidelberg, 2011, vol. 6577, p. 377–391.
    [Bibtex] 
    @InCollection{Scheubert:11a,
author = {Kerstin Scheubert and Franziska Hufsky and Florian Rasche and Sebastian B\"{o}cker},
title = {Computing Fragmentation Trees from Metabolite Multiple Mass Spectrometry Data},
booktitle = {Proc. of Research in Computational Molecular Biology (RECOMB 2011)},
publisher = {Springer Berlin Heidelberg},
year = {2011},
volume = {6577},
series = {Lect Notes Comput Sci},
pages = {377--391},
doi = {10.1007/978-3-642-20036-6_36},
}
  • [DOI] F. Hufsky, L. Kuchenbecker, K. Jahn, J. Stoye, and S. Böcker, “Swiftly computing center strings,” in Proc. of workshop on algorithms in bioinformatics (wabi 2010), Springer berlin heidelberg, 2010, vol. 6293, p. 325–336.
    [Bibtex] 
    @InCollection{Hufsky:10,
author = {Franziska Hufsky and L{\'{e}}on Kuchenbecker and Katharina Jahn and Jens Stoye and Sebastian Böcker},
title = {Swiftly Computing Center Strings},
booktitle = {Proc. of Workshop on Algorithms in Bioinformatics (WABI 2010)},
publisher = {Springer Berlin Heidelberg},
year = {2010},
volume = {6293},
series = {Lect Notes Comput Sci},
pages = {325--336},
doi = {10.1007/978-3-642-15294-8_27},
}