Accepted Posters

Category 'R'- Proteomics'
Poster R01
Evaluation of computational platforms for LC-MS based label-free quantitative proteomics
Runxuan Zhang- University of Dundee
Alun Barton (Univerisity of Dundee, Translational Medicine Research Collaboration); Julie Brittenden (University of Aberdeen, Department of Surgery); Jeffrey Huang (Pfizer Inc, Translational Medicine Research Collaboration); Daniel Crowther (Pfizer Inc, Translational Medicine Research Collaboration);
Short Abstract: A comprehensive procedure to provide a fast and global view for performances of LC-MS label-free computational software. Two high quality mass spectrometry datasets with carefully controlled QC samples and spiked-in proteins were used as benchmark datasets for such evaluations.
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Poster R02
A Machine Learning approach to Protein Secondary Structure Prediction, using Position Specific Residue Preferences
saraswathi sundararajan- Iowa State University
Pawel Gniewek (Warsaw University, Labolatory of Theory of Biopolymers, Faculty of Chemistry); Santhosh K. Vammi (Iowa State University, BCB program); Robert Jernigan (Iowa State University, Department of Biochemistry, Biophysics and Molecular Biology); Andrzej Kloczkowski (Iowa State University, Department of Biochemistry, Biophysics and Molecular Biology); Andrzej Kolinski (Warsaw University, Laboratory of Theory of Biopolymers, Faculty of Chemistry);
Short Abstract: A novel method is proposed for secondary structure prediction using knowledge based potentials and Neural Networks. Due to physico-chemical properties, some amino acids appear more often at the ends of secondary structures than others. Position Specific Residue Preferences (PSRP) of amino acids, can be used to improve secondary structure prediction.
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Poster R03
QuantProReloaded: Software for the statistical analysis of Microspot Immunoassays
Anika Joecker- German Cancer Research Center
Johanna Sonntag (German Cancer Research Center, Division of Molecular Genome Analysis); Frauke Henjes (German Cancer Research Center, Division of Molecular Genome Analysis); Frank Goetschel (German Cancer Research Center, Division of Molecular Genome Analysis); Tim Beissbarth (University Medicine Göttingen, Medical Statistics); Stefan Wiemann (German Cancer Research Center, Division of Molecular Genome Analysis); Ulrike Korf (German Cancer Research Center, Division of Molecular Genome Analysis);
Short Abstract: Microspot Immunoassays enable quantitative and parallel measurements of the abundance and activity levels of many proteins in one experiment. Because data analysis is still a bottleneck for these kind of data, we present QuantProReloaded an open source software tool designed for the analysis of time-resolved and non-time-resolved Microspot Immunoassay data.
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Poster R04
Improved Peak Detection and Quantification for Mass Spectrometry Analysis using CWT based Method
Kun-Pin Wu- National YangMing University
Tzu-Ching Wu (National YangMing University, Institute of BioMedical Informatics);
Short Abstract: We present a novel algorithm for MS data peak detection and peak quantification by utilize Continuous Wavelet Transform coefficients matrix, which derived from applying CWT over MS raw data. This algorithm improves the accuracy to detect peaks; peak quantification and resolution of overlapping peaks are also presented in this approach.
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Poster R05
Detection of Alternative Splice Variants at the Protein Level in the Human Genome
Iakes Ezkurdia- Cnio
Angela Del Pozo (CNIO, Structural Computational Biology Group); Alfonso Valencia (CNIO, Structural Computational Biology Group); Michael Tress (CNIO, Structural Computational Biology Group);
Short Abstract: We have carried out a comprehensive re-identification of the Human proteome using data from several data repositories. We have been able to confirm that many human genes do express multiple alternative protein isoforms that are stable enough and expressed in sufficient quantities to be detected in proteomics experiments.
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Poster R06
Subgraphs of the Protein-Protein Interaction Network with High Connectivity
Suzanne Gallagher- University of Colorado
No additional authors
Short Abstract: The edge (vertex) connectivity of a graph (or network) is the minimum number of edges (vertices) that must be removed in order to disconnect the graph. Our algorithm searches for the subgraph with the highest connectivity. When applied to protein-protein interaction networks, the algorithm produced several biologically significant subgraphs.
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Poster R07
A modular method to prediction of protein-protein interaction patches
Rafael Jordan- Iowa State University
Rafael Jordan (Iowa State University, Computer Science); Yasser EL-Manzalawy (Al-Azhar University, Systems and Computer Engineering); Vasant Honavar (Iowa State University, Computer Science); Drena Dobbs (Iowa State University, Genetics, Development and Cell Biology);
Short Abstract: A method for predicting protein-protein interface patches based on prediction of interface residues is presented. The method was validated on a blind data set of 220 monomers from ZDOCK benchmark 3.0 (success 74%) and produced a superior performance than SHARP2 and PPI-Pred on a subset of 24 proteins from CAPRI.
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Poster R08
The bait compatibility index: a computational approach towards bait selection for interaction proteomics experiments
Sudipto Saha- Case Western Reserve University
Rob Ewing (Case Western Reserve University, Center for Proteomics and Bioinformatics); Parminder Kaur (Case Western Reserve University, Center for Proteomics and Bioinformatics);
Short Abstract: Yeast two-hybrid (Y2H) and affinity-purification mass-spectrometry (AP-MS) are two commonly used techniques for large-scale detection of protein interactions. These techniques provide fundamentally different views of the protein interactome, and it is not clear what the specific biases in each technique really are. Here, we systematically study these biases and generate a novel score, the bait
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Poster R09
Global in silico identification of General and Kinase-Specific Phosphorylation Sites
Jianjiong Gao- University of Missouri
Jay Thelen (University of Missouri, Biochemistry); Keith Dunker (Indiana University, Schools of Medicine and Informatics); Dong Xu (University of Missouri, Computer Science);
Short Abstract: Here, we present a novel open-source software tool, Musite, specifically designed for large-scale prediction of both general and kinase-specific phosphorylation sites. Musite significantly outperforms existing tools for predicting general phosphorylation sites and is at least comparable to those for predicting kinase-specific phosphorylation sites. Musite is available at
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Poster R10
Replicate feature matching improves the identification of proteins via tandem mass spectrometry
Karin Noy- Siemens Corporate Research
Gayle Wittenberg (Siemens Corporate Research, Integrated Data Systems); Daniel Fasulo (Roche, 454 Life Sciences);
Short Abstract: The identification of peptides via tandem mass spectrometry is challenging. We present a novel approach in which only features of MS/MS spectra matched across replicates are used for peptide identification. Our approach is based on a novel shape-based feature matching algorithm and was shown to outperform the state-of-the-art software.
Long Abstract:Click Here

Poster R11
A statistical model for quantitative proteomics
Yuping Zhang- Stanford University
Wei-Jun Qian (Pacific Northwest National Laboratory, ); Ronald Davis (Stanford University, Biochemistry); Wing Hung Wong (Stanford University, Statistics);
Short Abstract: Quantitative proteomics can be used for the identification of biomarkers in diseases, and be further used for diagnosis and therapy. LC-MS is one of the most important proteomics technologies. We developed a comprehensive statistical model for quantifying and comparing the abundance of proteins in different biological conditions.
Long Abstract:Click Here

Poster R12
OpenFreezer LARISA: A web-based laboratory information and workflow management system
Marina Olhovsky- Mount Sinai Hospital
Adrian Pasculescu (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); John Paul Lee (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Jin Gyoon Park (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Clark Wells (Indiana University, School of Medicine); Kelly Williton (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Anna Dai (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Marilyn Goudreault (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Tony Pawson (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Rune Linding (Institute of Cancer Research, Cellular & Molecular Logic Team);
Short Abstract: OpenFreezer LARISA is a web-based laboratory reagent tracking application. OpenFreezer catalogs biochemical reagents and provides methods to search and update them. Interactuve workflow assistance tools include primer design and automated sequence construction modules. System may be customized to accommodate different reagent types, their properties and physical locations.
Long Abstract:Click Here

Poster R13
Global analysis and accurate prediction of human lysine acetylation proteins
Jianlin Shao- Department of Biology, The Chinese University of Hong Kong
Sai-Ming Ngai (Department of Biology, The Chinese University of Hong Kong);
Short Abstract: In the present study, we made the first attempt to systematically analyze human lysine acetylproteins and proposed Bi-RBS (Bi-Relative Binomial Score) Bayes combined with support vector machines (SVMs) to construct human-specific lysine acetylation predictor, which provides globally biological/pathological insights and a novel method for human lysine acetylation prediction.
Long Abstract:Click Here

Poster R14
Discriminative Motif Finding for Predicting Protein Subcellular Localization
Tien-Ho Lin- Carnegie Mellon University
Ziv Bar-Joseph (Carnegie Mellon University, Computer Science); Robert F. Murphy (Carnegie Mellon University, Lane Center for Computational Biology);
Short Abstract: Proper subcellular localization is critical for proteins to play their roles in cellular functions. Sequence motifs are central to localization. Many methods have been described to predict the subcellular location of proteins from sequence information, but most rely on global sequence properties or use known motifs. Profile hidden Markov models (HMM) are excellent for capturing motifs, since they permit gaps of variable length common in targeting domains. However, learning targeting motifs is complicated by the fact that proteins with different destinations may share a motif, and traditional HMM learning would not be able to find them. We therefore developed the first discriminative HMM learning method for protein motifs and also incorporated known targeting pathway information as a hierarchical structure. Both of these improve localization prediction on a yeast protein benchmark dataset. Using our system, we were able to correct location annotations in public databases, as subsequently supported by literature examination.
Long Abstract:Click Here

Poster R15
The Human Protein Atlas
Lisa Berglund- Royal Institute of Technology
Per Oksvold (School of Biotechnology, Royal Institute of Technology); Mathias Uhlén (School of Biotechnology, Royal Institute of Technology);
Short Abstract: In the Swedish Human Protein Atlas project, antibodies are used to map all proteins in the human body. The publicly available Human Protein Atlas web portal ( now contains localization data for >40% of the human proteins.
Long Abstract:Click Here

Poster R16
Spatial Segmentation of Mass Spectrometry Imaging Data
Theodore Alexandrov- Univerisity of Bremen
Peter Maass (University of Bremen, Center for Industrial Mathematics); Herbert Thiele (Bruker Daltonics, Bioinformatics);
Short Abstract: Mass spectrometry (MS) imaging is a prominent technique for spatial study of biological samples. We present a new procedure for spatial segmentation of MS-imaging data based on spectral similarity of spatial points, which represents the full dataset with just one image. Many applications (rat brain, cancer, bacteria colonies) are considered.
Long Abstract:Click Here

Poster R17
Prediction of Nuclear Proteins Based on Evolutionary Information and Probabilistic Latent Semantic Indexing
Emily Chia-Yu Su- Taipei Medical University
Jia-Ming Chang (Centre de Regulacio Genomica, Comparative Bioinformatics Group, Bioinformatics and Genomics Programme); Ting-Yi Sung (Academia Sinica, Institute of Information Science); Wen-Lian Hsu (Academia Sinica, Institute of Information Science);
Short Abstract: We present a nuclear localization prediction method based on evolutionary information and probabilistic latent semantic indexing. Using five-fold cross-validation on a benchmark data set, our method achieves 0.800 and 0.595 in overall accuracy and MCC, respectively, compared favorably to the state-of-the-art results of 0.749 and 0.497.
Long Abstract:Click Here

Poster R18
Mapping the subcellular location of the human proteome
Linn Fagerberg- Royal Institute of Technology
Charlotte Stadler (Royal Institute of Technology, Science for Life Laboratory); Mathias Uhlén (Royal Institute of Technology, Science for Life Laboratory); Emma Lundberg (Royal Institute of Technology, Science for Life Laboratory);
Short Abstract: Immunofluorescence-based confocal microscopy has been used to obtain a manual annotation of the subcellular localization for 4000 proteins in three different human cell lines using antibodies from the Human Protein Atlas program. We report on the distribution of localizations visualized by network analysis and hierarchical clustering.
Long Abstract:Click Here

Poster R19
The Complete Peptide Dictionary – A Meta-Proteomics Resource
Manor Askenazi- Hebrew University of Jerusalem
Jarrod Marto (Dana-Farber Cancer Institute, Cancer Biology); Michal Linial (Hebrew University of Jerusalem, Biological Chemistry);
Short Abstract: Pep2Pro ( is a fast web-service providing protein lookup by peptides, covering the entire (~10 million) UniRef100 sequence space. We demonstrate the usefulness of the service for mass-spectrometry proteomics by re-analyzing peptides from recent meta-proteomic datasets, identifying taxon-specific peptides implicating individual species as being present in these complex samples.
Long Abstract:Click Here

Poster R20
MSProcess – Mass Spectrometry Identification and Annotation Pipeline
Jill Wegrzyn- University of California, San Diego
Steven Bark (University of California at San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences); Vivian Hook (University of California at San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences);
Short Abstract: We have developed MSProcess, a platform for the processing and analysis of proteomics LC-MS/MS data. This analysis pipeline provides a full solution to the computational challenges resulting from large-scale proteomic experiments.
Long Abstract:Click Here

Poster R21
PIE - A protein inference engine: integrating proteomics data for comprehensive posttranslational modi?cation prediction
Stuart Jefferys- University of North Carolina - Chapel Hill
Morgan Giddings (University of North Carolina - Chapel Hill, Microbiology and Immunology);
Short Abstract: The Protein Inference Engine (PIE) predicts protein post-translational modifications (both adducts and cleavages) using Markov chain Monte Carlo. This allows integrating data such as top-down and bottom-up MS experimental data and database-derived prior distributions. PIE's modular construction makes incorporating additional data simple, including the predictions of other programs.
Long Abstract:Click Here

Poster R22
Bioinformatics Solutions to Enable the Generation of Complete Quantitative Proteomics Datasets Required for Modeling & Systems Biology
Christian Ahrens- University of Zurich
Erich Brunner (University of Zurich, Institute of Molecular Life Sciences); Ermir Qeli (University of Zurich, Institute of Molecular Life Sciences); Konrad Basler (University of Zurich, Institute of Molecular Life Sciences);
Short Abstract: We present solutions for comprehensive proteome analysis and support of targeted quantitative mass spectrometry. To generate quantitative proteomics datasets, proteotypic peptides (PTPs), i.e. peptides that unambiguously identify one protein are scored by multiple reaction monitoring (MRM). Reminiscent of microarray-based gene expression analysis, complete data series can be generated.
Long Abstract:Click Here

Accepted Posters

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