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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P66. PLADIPUS EMPOWERS UNIVERSAL DISTRIBUTED COMPUTING Kenneth Verheggen 1,2,3* , Harald Barsnes 4,5 , Lennart Martens 1,2,3 & Marc Vaudel 4 . Medical Biotechnology Center, VIB, Ghent, Belgium 1 ; Department of Biochemistry, Ghent University, Ghent 2 ; Belgium,Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium 3 ; Proteomics Unit, Department of Biomedicine, University of Bergen, Norway 4 ; KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway 5 . *kenneth.verheggen@vib-ugent.be The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity. However, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here, we propose a free and open source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. INTRODUCTION Various modern day bioinformatics-related fields have a growing focus on large scale data processing. This inevitably leads to an increased complexity, as is illustrated by the recent efforts to elaborate a comprehensive MS-based human proteome characterization (Kim et al., 2014; Wilhelm et al., 2014). Such high-throughput, complex studies are becoming increasingly popular, but require high performance computational setups in order to be analyzed swiftly. METHODS Here, we present a generic platform for distributed proteomics software, called Pladipus. It provides an end-user-oriented solution to distribute bioinformatics tasks over a network of computers, managed through an intuitive graphical user interface (GUI). Pladipus comes with several modules that work out of the box. They include SearchGUI (Vaudel et al., 2011), PeptideShaker (Vaudel et al., 2015), DeNovoGUI (Muth et al., 2014), MsConvert (part of Proteowizard (Kessner et al., 2008)) and three common forms of the BLAST (Altschul et al., 1990) algorithm (blastn, blastp and blastx). It is possible to link these together to set up tailored pipelines for specific needs, including custom, in-house algorithms and execute the whole on an inexpensive, scalable cluster infrastructure without additional cost or expert maintenance requirement. It can even be set up to allow existing (idle) hardware to hook into the network and participate in the processing. RESULTS & DISCUSSION To numerically assess the benefits of using a distributed computing framework, 52 CPTAC experiments (LTQ- Study6 : Orbitrap@86) (Paulovich et al., 2010) were searched three times against a protein sequence database (UniProtKB/SwissProt (release-2015_05)) on Pladipus networks of various. A selection of three search engines was applied: X!Tandem, Tide and MS-GF+. As expected for a distributed system, the wall time is very reproducible and decreased nearly exponentially with the number of workers. FIGURE 1. Benchmarking of a Pladipus network (16GB ram, 12cores, 250GB disk space, Ubuntu precise) Pladipus is freely available as open source under the permissive Apache2 license. Documentation, including example files, an installer and a video tutorial, can be found at https://compomics.github.io/projects/pladipus.html. REFERENCES Altschul,S.F. et al. (1990) Basic local alignment search tool. J. Mol. Biol., 215, 403–10. Kessner,D. et al. (2008) ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics, 24, 2534–6. Kim,M.-S. et al. (2014) A draft map of the human proteome. Nature, 509, 575–81. Muth,T. et al. (2014) DeNovoGUI: an open source graphical user interface for de novo sequencing of tandem mass spectra. J. Proteome Res., 13, 1143–6. Paulovich,A.G. et al. (2010) Interlaboratory study characterizing a yeast performance standard for benchmarking LC-MS platform performance. Mol. Cell. Proteomics, 9, 242–54. Vaudel,M. et al. (2015) PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat. Biotechnol., 33, 22–24. Vaudel,M. et al. (2011) SearchGUI: An open-source graphical user interface for simultaneous OMSSA and X!Tandem searches. Proteomics, 11, 996–9. Wilhelm,M. et al. (2014) Mass-spectrometry-based draft of the human proteome. Nature, 509, 582–7. 110
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P67. IDENTIFICATION OF ANTIBIOTIC RESISTANCE MECHANISMS USING A NETWORK-BASED APPROACH Bram Weytjens 1,2,3,4 , Dries De Maeyer 1,2,,3,4 & Kathleen Marchal 1,2,4 *. Dept. of Information Technology (INTEC, iMINDS), UGent, Ghent, 9052, Belgium 1 ; Dept. of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium 2 ; Dept. of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium 3 , Bioinformatics Institute Ghent, Ghent University, Ghent B-9000, Belgium 4 . * kathleen.marchal@intec.ugent.be Antibiotic resistance is a growing public health concern as the effectiveness of multiple types of antibiotics is decreasing. To prevent and combat the further spread of antibiotic resistance in bacteria there is the need to better understand the relationship between genetic alterations and the (molecular) phenotype of antibiotic resistant strains. As several (-omics) experiments regarding the attainment of antibiotic resistance by bacteria have already been performed and are publicly available, we re-analysed a laboratory evolution experiment by Suzuki et al. (Suzuki, 2014) in order to demonstrate the power of a network-based approach in identifying mutations and molecular pathways driving the resistance phenotype. INTRODUCTION While network-based approaches are no longer new in high-throughput (-omics) analysis, they are not yet widely used in standard analysis pipelines. We analysed a dataset consisting of multiple E. coli MDS42 strains, each independently evolved in the presence of a specific antibiotic (10 in total). By adapting PheNetic (De Maeyer. 2013), an algorithm which connects genetic alterations to their differentially expressed genes over a genome-wide interaction network, we were able to automatically identify mutations in genes which are known to induce antibiotic resistance. METHODS For every strain whole-genome sequencing data and microarray data (eQTL data) was available. By finding the most probable connections between the mutations of every strain and the strain’s respective expression data over a biological network, PheNetic was able to not only uncover potential driver genes and molecular pathways for the resistance phenotype but also to prioritize the identified mutations based on the likelihood that they are truly driving the resistance phenotype. Such network-based approach has following advantages: Integration of interactomics (network), genomics and interactomics data Multiple related datasets can be analyzed together FIGURE 1: Part of Amikacin resistance network. RESULTS & DISCUSSION In the case of Amikacin resistance (figure 1) we were able to uncover a gain-of-function mutation in cpxA, a gene of a two-component signal transduction mechanisms which is known to be involved in amikacin resistance for two strains out of four. For the other two strains, deleterious cyoB mutations were found, which is known to lead to intracellular oxidized copper and eventually multidrug resistance. These genes were furthermore ranked highest by PheNetic. REFERENCES Suzuki S et al. Nat Commun 5, 5792 (2014). De Maeyer D et al. Mol Biosyst 9: 1594-1603 (2013). 111
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