| Accepted Posters |
| Category 'N'- Proteomics' |
| Poster N01 |
| ProteoConnections: an analysis platform to accelerate proteomes and phosphoproteomes exploration |
| Mathieu Courcelles- Institute for research in immunology and cancer (IRIC) |
| Pierre Thibault (IRIC, Chemistry); Maria Marcantonio (IRIC, Biochemistry); Matthias Trost (IRIC, Biochemistry); Laure Voisin (IRIC, -); Sylvain Meloche (IRIC, Pharmacologie); |
| Short Abstract: Considering the wealth of information generated by large-scale phosphoproteomics experiments, novel and improved bioinformatics tools are now required to profile changes in phosphorylation and associate interacting partners involved in specific signaling cascade events. To this end, we developed a bioinformatics platform tailored to address the pressing needs of phosphoproteomics analyses. |
| Long Abstract: Click Here |
| Poster N02 |
| PeptideAtlas: A Resource for Capturing Proteomes Observed by Tandem Mass Spectrometry |
| Abhishek Pratap- Institute for Systems Biology/VIT University |
| Eric Deutsch (Institute for Systems Biology, Aebersold Lab); Henry Lam (Institute for Systems Biology, Aebersold Lab); Dave Campbell (Institute for Systems Biology, Aebersold Lab); Ning Zhang (Institute for Systems Biology, Aebersold Lab); Reudi Aebersold (Institute for Molecular Systems Biology, Aebersold Lab); |
| Short Abstract: PeptideAtlas is a growing, publicly accessible compendium of peptides identified in many tandem mass spectrometry proteomics studies. It comprises open source software tools that enable the building of the PeptideAtlas as well as tools that enable usage of the information by the community for designing new experiments. |
| Long Abstract: Click Here |
| Poster N03 |
| OpenFreezer: an information management system for reagent tracking and workflow automation |
| 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, Biochemistry and Molecular Biology); Kelly Elder (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Anna Dau (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 (The Institute of Cancer Research, Network & Systems Biology); Karen Colwill (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); |
| Short Abstract: OpenFreezer LIMS is a web application that tracks reagents and their properties (sequences, features, physical locations, mass spectrometry results, etc.). It contains tools for automated primer design, novel vector constitution, and more. OpenFreezer implements platform- and browser-independent client-server architecture using PHP/DHTML, MySQL, and Python CGI. |
| Long Abstract: Click Here |
| Poster N04 |
| A Bayesian approach for protein inference problem in shotgun proteomics |
| Yong Li- Indiana University - Bloomington |
| Haixu Tang (Indiana University - Bloomington, School of Informatics); Predrag Radivojac (Indiana University - Bloomington, School of Informatics); Randy Arnold (Indiana University - Bloomington, Department of Chemistry); |
| Short Abstract: We present a Bayesian approach which incorporates peptide detectability to solve the protein inference problem with high accuracy. The algorithm has better performance than other methods and it can effectively assign proteins with degenerate detected peptides. Besides, it also improves peptide identification. |
| Long Abstract: Click Here |
| Poster N05 |
| Spatial Querying of Imaging Mass Spectrometry Data |
| Raf Van de Plas- K.U.Leuven |
| Kristiaan Pelckmans (K.U.Leuven, Electrical Eng. ESAT-SCD); Bart De Moor (K.U.Leuven, Electrical Eng. ESAT-SCD); Etienne Waelkens (K.U.Leuven, Molecular Cell Biology); |
| Short Abstract: Imaging mass spectrometry or mass spectral imaging (MSI) is a technology that adds a spatial dimension to mass spectrometry-based biochemical analysis. This work develops an efficient computational method that enables the researcher to interrogate MSI data from a spatial standpoint, rather than with traditional mass-centric approaches such as ion images. |
| Long Abstract: Click Here |
| Poster N06 |
| Specificity and cross-reactions in PDZ interaction networks |
| David Gfeller- University of Toronto |
| Gary Bader (University of Toronto, Donnelly Centre for Cellular and Biomolecular Research); |
| Short Abstract: PDZ domains play a central role in protein interactions. We use phage display results to investigate how binding specificity is achieved. In particular, we focus on proteins that are predicted to interact with more than one domain to characterize the overlap between the binding regions of different PDZ domains. |
| Long Abstract: Click Here |
| Poster N07 |
| Assessment of ‘Golden Pair’ rule for peptide sequencing and protein identification using iontrap CAD/ETD MS/MS |
| Thomas Hansen- University of Southern Denmark |
| Ole Nørregaard Jensen (University of Southern Denmark, Department of Biochemistry and Molecular Biology); Frank Kjeldsen (University of Southern Denmark, Department of Biochemistry and Molecular Biology); |
| Short Abstract: From corresponding low-resolution MS/MS spectra fragmented using CAD and ETD respectively, we extract 'Golden Pairs' of the same fragment ions of different types. These pairs are used in three different methods to remove false positive peptide identifications and thereby increase the confidence in protein identifications. |
| Long Abstract: Click Here |
| Poster N08 |
| ProteoChart – a data analysis platform for systems biology |
| Adrian Pasculescu- Mount Sinai Hospital |
| Claus Jorgensen (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Marina Olhovsky (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Vivian Nguyen (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Andrew James (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Jonathan So (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Tony Pawson (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Karen Colwill (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Rune Linding (The Institute of Cancer Research, Network & Systems Biology); |
| Short Abstract: ProteoChart, a platform for biologists to integrate and analyze their data derived from multiple sources. It integrates mass spectrometry data (e.g. Mascot) and phenotypic data (e.g. RNAi screening), allows for data filtering, and provides further data mining and statistical analysis using R or through association with NetworKIN and STRING. |
| Long Abstract: Click Here |
| Poster N09 |
| Prediction of Cell Penetrating Peptides |
| William Sanders- Mississippi State University |
| Kenneth Willeford (Mississippi State University, Biochemistry & Molecular Biology); Susan Bridges (Mississippi State University, Computer Science & Engineering); |
| Short Abstract: Cell penetrating peptides are peptides capable of traversing cell membranes and entering cells. We have developed artificial neural network and support vector machine classifiers capable of predicting whether a given peptide will be a cell penetrating peptide. |
| Long Abstract: Click Here |
| Poster N10 |
| Differential proteomics of yeast chromatin-associated proteins |
| DongRyoung Kim- University of Waterloo |
| Rohan Gidvani (University of Waterloo, Biology); Bernard Duncker (University of Waterloo, Biology); Brian Ingalls (University of Waterloo, Applied Mathematics); Brendan McConkey (University of Waterloo, Biology); |
| Short Abstract: We applied differential proteomics methods to identify yeast chromatin associated proteins including cell cycle and chromatin remodeling proteins. This methodology, employing chromatin fractionation and DIGE screening, demonstrates the utility of proteomics mapping and quantification of low abundance chromatin associated proteins. |
| Long Abstract: Click Here |
| Poster N11 |
| Measuring phylogenetically deep conservation in protein sequences |
| Sergey Mokin- McGill University |
| Paul Harrison (McGill University, Biology); |
| Short Abstract: We have derived novel methods for assessing significant violation of phylogenetically deep conservation in protein sequences. We have applied these methods to the characterization of pseudogenes and evolutionarily young sequences. The methods can demonstrate a significant loss of long-standing purifying selection in these sequences, where other algorithms cannot. |
| Long Abstract: Click Here |
| Poster N12 |
| Comprehensive catalogues of yeast protein complexes from high throughput experiments and from literature |
| Shuye Pu- The Hospital for Sick Children |
| Jessica Wong (The Hospital for Sick Children, Molecular Structure and Function Program); Shoshana Wodak (The Hospital for Sick Children, Molecular Structure and Function Program); |
| Short Abstract: We report a new compendium of 407 protein complexes in yeast, that are supported by both the scientific literature and analysis of a recent high confidence yeast protein interaction network. This up-to-date standard should be instrumental for the successful application of machine learning techniques to predict new interactions. |
| Long Abstract: Click Here |
| Poster N13 |
| Machine learning classification of MS/MS database search results |
| Morten Källberg- University of Illinois at Chicago |
| Hui Lu (University of Illinois at Chicago, Bioengineering); |
| Short Abstract: We present a machine learning solution for classification of MS/MS database search results, improving on published performance by 6% (net prediction). Furthermore, we suggest a straight-forward way for extending this solution from the peptide prediction problem into the protein prediction problem and identified 80% of the proteins in the sample. |
| Long Abstract: Click Here |
| Poster N14 |
| Sequence clustering analysis to find protein kinase substrates in A. thaliana and O. sativa |
| Michael Gribskov- Purdue University |
| Greg Ziegler (Purdue University, Department of Biological Sciences); |
| Short Abstract: We have been analyzing serine, threonine, and tyrosine containing sequences in entire proteomes, and by a datamining process identifying subsequences that are likely to be protein kinase targets. Preliminary results have validated some of our early predictions for calcium dependent protein kinases in Arabidopsis. |
| Long Abstract: Click Here |
| Poster N15 |
| The Universal Similarity Metric applied to Protein Contact Map Comparison in a two dimensional Space |
| Sara Rahmati- Queen's University |
| Janice Glasgow (Queen's University, Computing); |
| Short Abstract: We present an approach to compare protein contact maps by means of the Universal Similarity Metric in their matrix format. Without loss of any information in converting the dimensionality of the maps, our method shows noise tolerance which makes it applicable in several comparison applications in bioinformatics and proteomics. |
| Long Abstract: Click Here |
| Poster N16 |
| EigenMS: LC-MS spectral peaks SVD-based normalization tool |
| Yuliya Karpievitch- Texas A&M |
| Alan Dabney (Texas A&M, Statistics); |
| Short Abstract: EigenMS is an open source graphical singular value decomposition based LC-MS data normalization tool. EigenMS estimates systematic biases by eigenpeptides computed on QC proteins, then subtracts the estimated eigenpeptides from the experimental samples. When QC proteins are unavailable, EigenMS can be applied directly to experimental samples in well-designed experiments. |
| Long Abstract: Click Here |
| Poster N17 |
| Method for comparing distant ion maps from LC-MS spectra |
| Claus Andersen- Siena Biotech SpA |
| Stefano Gotta (Siena Biotech S.p.A., Protein Sciences); Gianluca Sardone (Siena Biotech S.p.A., Protein Sciences); Roberto Raggiaschi (Siena Biotech S.p.A., Protein Sciences); Andreas Kremer (Siena Biotech S.p.A., Bioinformatics); Timothy Bonnert (Rosetta Biosciences, Bioinformatics); |
| Short Abstract: We describe an improved method to determine and link accurate mass tag windows, which uses various types of MS level information to improve an initial alignment. We show how improving the alignment of distant ion maps will facilitate the comparison of complex proteomes by decreasing the influence of chromatographic variability. |
| Long Abstract: Click Here |
| Poster N18 |
| Proteomics Data Management - Lab Versus Machine - How the small guy won |
| Simon Foote- National Research Council of Canada |
| Luc Tessier (National Research Council of Canada, Institute for Biological Sciences); John Kelly (National Research Council of Canada, Institute for Biological Sciences); |
| Short Abstract: The amount of data being generated by today's mass spectrometers soon leads to bottlenecks in a laboratory's ability to process and store the data. We present a data management plan that shows how a facility with minimal informatics resources handled the problem. |
| Long Abstract: Click Here |
| Poster N19 |
| Markov random field models for protein function prediction |
| Hisamitsu Akiba- Chuo University |
| Y-h. Taguchi (Chuo University, Physics Department); |
| Short Abstract: It has been proposed that protein-protein interactions could predict relationships using statistical model. We developed Markov random field(MRF) method to predict yeast protein functions. And, we could improve accuracy by applying new parameter estimation method and considering indirect relation protein pairs. |
| Long Abstract: Click Here |
| Accepted Posters |
| View Posters By Category |
- A) Arrays
- B) Bioinformatics of Health and Disease
- C) Chemical and Pharmaceutical Informatics
- D) Comparative Genomics
- E) Databases
- F) Evolution
- G) Functional Genomics
- H) Gene Prediction
- I) Genome Annotation
- J) Genomics
- K) Interactions
- L) Machine Learning
- M) Population Genetics and Variation
- N) Proteomics
- O) Regulation
- P) Sequence Analysis
- Q) Structure and Function Prediction
- R) Systems Biology and Networks
- S) text mining
- T) Other (includes posters with fewer than 10 submissions)
| Search Posters: |