Sebastian Winkler1,2, Ivana Winkler3,4,5, Mirjam Figaschewski6, Thorsten Tiede6, Alfred Nordheim4,7, Oliver Kohlbacher6,8,9. 1. Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany. sebwink@pm.me. 2. International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany. sebwink@pm.me. 3. International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany. 4. Interfaculty Institute for Cell Biology (IFIZ), University of Tuebingen, Tübingen, Germany. 5. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. 6. Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany. 7. Leibniz Institute on Aging (FLI), Jena, Germany. 8. Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany. 9. Translational Bioinformatics, University Hospital Tuebingen, Tübingen, Germany.
Abstract
BACKGROUND: With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. RESULTS: We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. CONCLUSION: The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.
BACKGROUND: With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. RESULTS: We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. CONCLUSION: The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.
Authors: Christina Backes; Alexander Rurainski; Gunnar W Klau; Oliver Müller; Daniel Stöckel; Andreas Gerasch; Jan Küntzer; Daniela Maisel; Nicole Ludwig; Matthias Hein; Andreas Keller; Helmut Burtscher; Michael Kaufmann; Eckart Meese; Hans-Peter Lenhof Journal: Nucleic Acids Res Date: 2011-12-30 Impact factor: 16.971
Authors: Shao-shan Carol Huang; David C Clarke; Sara J C Gosline; Adam Labadorf; Candace R Chouinard; William Gordon; Douglas A Lauffenburger; Ernest Fraenkel Journal: PLoS Comput Biol Date: 2013-02-07 Impact factor: 4.475
Authors: Nurcan Tuncbag; Sara J C Gosline; Amanda Kedaigle; Anthony R Soltis; Anthony Gitter; Ernest Fraenkel Journal: PLoS Comput Biol Date: 2016-04-20 Impact factor: 4.475