, Volume 40, Issue 2, pp 153–160 | Cite as

Bioinformatics advances biology and medicine by turning big data troves into knowledge

  • Julien Gagneur
  • Caroline Friedel
  • Volker Heun
  • Ralf Zimmer
  • Burkhard Rost


Informatics and life sciences (molecular biology and medicine) are undoubtedly the most rapidly growing and most dynamic endeavors of modern society. Computational biology or bioinformatics describes the rising field that integrates those endeavors. Over the last 50 years, the field has shifted focus from the study of individual genes and proteins (1967–1994), to that of entire organisms (19952015), and more recently to studying the diversity of populations. The increasing amount of big data created by the life sciences is challenging already by its volume alone. Even more challenging is the high intrinsic complexity of the data. In addition, the data are changing at a breathtaking speed; most data generated in 2016 probes conditions that had not been anticipated 15 years ago. Precision medicine and personalized health are just two descriptors of how modern biology will become relevant for improving our health. All new drugs have at some point have bioinformatics tools in their development. Similarly, there would not be any digital medicine without the bioinformatics expertise or any advances without mastering machine learning tools turning raw data into valuable insights and decisions.


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  1. 1.
    Arnold R, Goldenberg F, Mewes HW, Rattei T (2014) SIMAP – the database of all-against-all protein sequence similarities and annotations with new interfaces and increased coverage. Nucl Acids Res 42:D279–D284CrossRefGoogle Scholar
  2. 2.
    Barker WC, George DG, Mewes HW, Pfeiffer F, Tsugita A (1993) The PIR-International databases. Nucl Acids Res 21:3089–3092CrossRefGoogle Scholar
  3. 3.
    Birzele F, Csaba G, Erhard F, Friedel CC, Küffner R, Petri T, Windhager L, Zimmer R (2009) Algorithmische Systembiologie mit Petrinetzen – Von qualitativen zu quantitativen Systemmodellen. Informatik-Spektrum 32:310–319CrossRefGoogle Scholar
  4. 4.
    Blasi T, Hennig H, Summers HD, Theis FJ, Cerveira J, Patterson JO, Davies D, Filby A, Carpenter AE, Rees P (2016) Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nature Commun 7:10256CrossRefGoogle Scholar
  5. 5.
    Dolken L, Malterer G, Erhard F, Kothe S, Friedel CC, Suffert G, Marcinowski L, Motsch N, Barth S, Beitzinger M, Lieber D, Bailer SM, Hoffmann R, Ruzsics Z, Kremmer E, Pfeffer S, Zimmer R, Koszinowski UH, Grasser F, Meister G, Haas J (2010) Systematic analysis of viral and cellular microRNA targets in cells latently infected with human gamma-herpesviruses by RISC immunoprecipitation assay. Cell Host Microbe 7:324–334CrossRefGoogle Scholar
  6. 6.
    Ellwanger DC, Leonhardt JF, Mewes HW (2014) Large-scale modeling of condition-specific gene regulatory networks by information integration and inference. Nucl Acids Res 42:e166, doi: 10.1093/nar/gku916Google Scholar
  7. 7.
    Eser P, Wachutka L, Maier KC, Demel C, Boroni M, Iyer S, Cramer P, Gagneur J (2016) Determinants of RNA metabolism in the Schizosaccharomyces pombe genome. Mol Syst Biol 12:857CrossRefGoogle Scholar
  8. 8.
    Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb J-F, Dougherty BA, Merrick JM, McKenney K, Sutton G, FitzHugh W, Fields C, Gocayne JD, Scott J, Shirley R, Liu L-I, Glodek A, Kelley JM, Weidman JF, Phillips CA, Spriggs T, Hedblom E, Cotton MD, Utterback TR, Hanna MC, Nguyen DT, Saudek DM, Brandon RC, Fine LD, Fritchman JL, Fuhrmann JL, Geoghagen NSM, Gnehm CL, McDonald LA, Small KV, Fraser CM, Smith HO, Venter JC (1995) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269:496–512CrossRefGoogle Scholar
  9. 9.
    Friedel CC, Dolken L, Ruzsics Z, Koszinowski UH, Zimmer R (2009) Conserved principles of mammalian transcriptional regulation revealed by RNA half-life. Nucl Acids Res 37:e115CrossRefGoogle Scholar
  10. 10.
    Friedel CC, Zimmer R (2007) Influence of degree correlations on network structure and stability in protein-protein interaction networks. BMC Bioinformatics 8:297CrossRefGoogle Scholar
  11. 11.
    Ginzinger SW, Skocibusic M, Heun V (2009) CheckShift improved: fast chemical shift reference correction with high accuracy. J Biomol NMR 44:207–211CrossRefGoogle Scholar
  12. 12.
    Goldberg T, Rost B, Bromberg Y (2016) Computational prediction shines light on type III secretion origins. Scientific reports 6:34516CrossRefGoogle Scholar
  13. 13.
    Hecht M, Bromberg Y, Rost B (2013) News from the protein mutability landscape. J Mol Biol 425:3937–3948CrossRefGoogle Scholar
  14. 14.
    Honigschmid P, Frishman D (2016) Accurate prediction of helix interactions and residue contacts in membrane proteins. J Struct Biol 194:112–123CrossRefGoogle Scholar
  15. 15.
    Jaravine V, Raffegerst S, Schendel DJ, Frishman D (2016) Assessment of cancer and virus antigens for cross-reactivity in human tissues. Bioinformatics 33:107–111Google Scholar
  16. 16.
    Karabulut NP, Frishman D (2016) Sequence- and Structure-Based Analysis of Tissue-Specific Phosphorylation Sites. PLoS One 11:e0157896CrossRefGoogle Scholar
  17. 17.
    Kremer L, Bader D, Mertes C, Kopajtich R, Pichler G, Iuso A, Haack T, Graf E, Schwarzmayr T, Terrile C, Konafikova E, Repp B, Kastenmüller G, Adamski J, Lichtner P, Leonhardt C, Funalot B, Donati A, Tiranti V, Lombes A, Jardel C, Gläser D, Taylor R, Ghezzi D, Mayr J, Rötig A, Freisinger P, Distelmaier F, Strom T, Meitinger T, Gagneur J, Prokisch H (2017) Genetic diagnosis of Mendelian disorders via RNA sequencing. bioRxivGoogle Scholar
  18. 18.
    Krumsiek J, Friedel CC, Zimmer R (2008) ProCope – protein complex prediction and evaluation. Bioinformatics 24:2115–2116CrossRefGoogle Scholar
  19. 19.
    Mahlich Y, Hecht M, De Beer TAP, Bromberg Y, Rost B (2016) Common sequence variants affect molecular function more than rare variants? PNAS (submitted)Google Scholar
  20. 20.
    Mewes HW, Albermann K, Heumann K, Liebl S, Pfeiffer F (1997) MIPS: a database for protein sequences, homology data and yeast genome information. Nucl Acids Res 25:28–30CrossRefGoogle Scholar
  21. 21.
    Montgomery SB, Dermitzakis ET (2011) From expression QTLs to personalized transcriptomics. Nat Rev Genet 12:277–282CrossRefGoogle Scholar
  22. 22.
    Rost B, Radivojac P, Bromberg Y (2016) Protein function in precision medicine: deep understanding with machine learning. FEBS Letters 590:2327–2341CrossRefGoogle Scholar
  23. 23.
    Rost B, Sander C (1992) Jury returns on structure prediction. Nature 360:540CrossRefGoogle Scholar
  24. 24.
    Rost B, Sander C (1993) Improved prediction of protein secondary structure by use of sequence profiles and neural networks. PNAS 90:7558–7562CrossRefGoogle Scholar
  25. 25.
    Schneider M, Rosam M, Glaser M, Patronov A, Shah H, Back KC, Daake MA, Buchner J, Antes I (2016) BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP. Proteins 84:1390–1407CrossRefGoogle Scholar
  26. 26.
    Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, Hamp T, Honigschmid P, Schafferhans A, Roos M, Bernhofer M, Richter L, Ashkenazy H, Punta M, Schlessinger A, Bromberg Y, Schneider R, Vriend G, Sander C, Ben-Tal N, Rost B (2014) PredictProtein – an open resource for online prediction of protein structural and functional features. Nucl Acids Res 42:W337–W343CrossRefGoogle Scholar
  27. 27.
    Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M, Savitski MM, Ziegler E, Butzmann L, Gessulat S, Marx H, Mathieson T, Lemeer S, Schnatbaum K, Reimer U, Wenschuh H, Mollenhauer M, Slotta-Huspenina J, Boese JH, Bantscheff M, Gerstmair A, Faerber F, Kuster B (2014) Mass-spectrometry-based draft of the human proteome. Nature 509:582–587CrossRefGoogle Scholar
  28. 28.
    Zhang Y, Xu H, Frishman D (2016) Genomic determinants of somatic copy number alterations across human cancers. Hum Mol Genet 25:1019–1030CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Julien Gagneur
    • 1
  • Caroline Friedel
    • 2
  • Volker Heun
    • 2
  • Ralf Zimmer
    • 2
  • Burkhard Rost
    • 1
    • 3
    • 4
    • 5
  1. 1.Department of Informatics, Bioinformatics and Computational Biology – i12TUM (Technical University of Munich)Garching/MunichGermany
  2. 2.Institut für InformatikLudwig-Maximilians-Universität MünchenMünchenGermany
  3. 3.Institute for Advanced Study (TUM-IAS)Garching/MunichGermany
  4. 4.New York Consortium on Membrane Protein Structure (NYCOMPS) and Department of Biochemistry and Molecular BiophysicsColumbia UniversityNew YorkUnited States
  5. 5.Institute for Food and Plant Sciences WZW – WeihenstephanFreisingGermany

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