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OrthoClusterDB: An Online Platform for Synteny Blocks

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2009
Abstract: 

Background: The recent availability of an expanding collection of genome sequences driven bytechnological advances has facilitated comparative genomics and in particular the identification ofsynteny among multiple genomes. However, the development of effective and easy-to-use methodsfor identifying such conserved gene clusters among multiple genomes–synteny blocks–as well asdatabases, which host synteny blocks from various groups of species (especially eukaryotes) andalso allow users to run synteny-identification programs, lags behind.Descriptions: OrthoClusterDB is a new online platform for the identification and visualization ofsynteny blocks. OrthoClusterDB consists of two key web pages: Run OrthoCluster and View Synteny.The Run OrthoCluster page serves as web front-end to OrthoCluster, a recently developed programfor synteny block detection. Run OrthoCluster offers full control over the functionalities ofOrthoCluster, such as specifying synteny block size, considering order and strandedness of geneswithin synteny blocks, including or excluding nested synteny blocks, handling one-to-manyorthologous relationships, and comparing multiple genomes. In contrast, the View Synteny page givesaccess to perfect and imperfect synteny blocks precomputed for a large number of genomes,without the need for users to retrieve and format input data. Additionally, genes are cross-linkedwith public databases for effective browsing. For both Run OrthoCluster and View Synteny, identifiedsynteny blocks can be browsed at the whole genome, chromosome, and individual gene level.OrthoClusterDB is freely accessible.Conclusion: We have developed an online system for the identification and visualization ofsynteny blocks among multiple genomes. The system is freely available at http://genome.sfu.ca/orthoclusterdb/.

Document type: 
Article

Sparsification of RNA Structure Prediction Including Pseudoknots

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2010
Abstract: 

Background: Although many RNA molecules contain pseudoknots, computational prediction of pseudoknottedRNA structure is still in its infancy due to high running time and space consumption implied by the dynamicprogramming formulations of the problem.Results: In this paper, we introduce sparsification to significantly speedup the dynamic programming approachesfor pseudoknotted RNA structure prediction, which also lower the space requirements. Although sparsification hasbeen applied to a number of RNA-related structure prediction problems in the past few years, we provide the firstapplication of sparsification to pseudoknotted RNA structure prediction specifically and to handling gappedfragments more generally - which has a much more complex recursive structure than other problems to whichsparsification has been applied. We analyse how to sparsify four pseudoknot structure prediction algorithms,among those the most general method available (the Rivas-Eddy algorithm) and the fastest one (Reeder-Giegerichalgorithm). In all algorithms the number of “candidate” substructures to be considered is reduced.Conclusions: Our experimental results on the sparsified Reeder-Giegerich algorithm suggest a linear speedup overthe unsparsified implementation.

Document type: 
Article

Interaction Profile-Based Protein Classification of Death Domain

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2004
Abstract: 

Background: The increasing number of protein sequences and 3D structure obtained fromgenomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimentaland computational efforts on the creation and analysis of information derived from 3D structure.In particular, the high-throughput generation of protein-protein interaction data from a feworganisms makes such an approach very important towards understanding the molecularrecognition that make-up the entire protein-protein interaction network. Since the generation ofsequences, and experimental protein-protein interactions increases faster than the 3D structuredetermination of protein complexes, there is tremendous interest in developing in silico methodsthat generate such structure for prediction and classification purposes. In this study we focused onclassifying protein family members based on their protein-protein interaction distinctiveness.Structure-based classification of protein-protein interfaces has been described initially by Ponstinglet al. [1] and more recently by Valdar et al. [2] and Mintseris et al. [3], from complex structures thathave been solved experimentally. However, little has been done on protein classification based onthe prediction of protein-protein complexes obtained from homology modeling and dockingsimulation.Results: We have developed an in silico classification system entitled HODOCO (Homologymodeling, Docking and Classification Oracle), in which protein Residue Potential InteractionProfiles (RPIPS) are used to summarize protein-protein interaction characteristics. This systemapplied to a dataset of 64 proteins of the death domain superfamily was used to classify eachmember into its proper subfamily. Two classification methods were attempted, heuristic andsupport vector machine learning. Both methods were tested with a 5-fold cross-validation. Theheuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an89% average accuracy.Conclusion: We have confirmed the reliability and potential value of classifying proteins via theirpredicted interactions. Our results are in the same range of accuracy as other studies that classifyprotein-protein interactions from 3D complex structure obtained experimentally. While ourclassification scheme does not take directly into account sequence information our results are inagreement with functional and sequence based classification of death domain family members.

Document type: 
Article

A High-Accuracy Nonintrusive Networking Testbed for Wireless Sensor Networks

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2010
Abstract: 

It becomes increasingly important to obtain the accurate and spontaneous runtime network behavior for further studies onwireless sensor networks. However, the existing testbeds cannot appropriately match such requirements. A High-accuracyNonintrusive Networking Testbed (HINT) is proposed. In HINT, the interconnected chip-level signals are passively captured withauxiliary test boards and the captured data are transferred in additional networks to test server. The test server of HINT collects allthe test data and depicts the full network behavior. HINT supports networking test, protocol verification, performance evaluationand so on. The experiments show that HINT transparently gathers accurate runtime data and does not disturb the spontaneousbehavior of sensor networks. HINT is also extendible to different hardware platforms of sensor nodes. Consequently, HINT isan upstanding testbed solution for the future fine-grained and experimental studies on the resource-constrained wireless sensornetworks.

Document type: 
Article

Relationship between Insertion/Deletion (Indel) Frequency of Proteins and Essentiality

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2007
Abstract: 

Background: In a previous study, we demonstrated that some essential proteins from pathogenicorganisms contained sizable insertions/deletions (indels) when aligned to human proteins of highsequence similarity. Such indels may provide sufficient spatial differences between the pathogenicprotein and human proteins to allow for selective targeting. In one example, an indel difference wastargeted via large scale in-silico screening. This resulted in selective antibodies and smallcompounds which were capable of binding to the deletion-bearing essential pathogen proteinwithout any cross-reactivity to the highly similar human protein. The objective of the current studywas to investigate whether indels were found more frequently in essential than non-essentialproteins.Results: We have investigated three species, Bacillus subtilis, Escherichia coli, and Saccharomycescerevisiae, for which high-quality protein essentiality data is available. Using these data, wedemonstrated with t-test calculations that the mean indel frequencies in essential proteins weregreater than that of non-essential proteins in the three proteomes. The abundance of indels in bothtypes of proteins was also shown to be accurately modeled by the Weibull distribution. However,Receiver Operator Characteristic (ROC) curves showed that indel frequencies alone could not beused as a marker to accurately discriminate between essential and non-essential proteins in thethree proteomes. Finally, we analyzed the protein interaction data available for S. cerevisiae andobserved that indel-bearing proteins were involved in more interactions and had greaterbetweenness values within Protein Interaction Networks (PINs).Conclusion: Overall, our findings demonstrated that indels were not randomly distributed acrossthe studied proteomes and were likely to occur more often in essential proteins and those thatwere highly connected, indicating a possible role of sequence insertions and deletions in theregulation and modification of protein-protein interactions. Such observations will provide newinsights into indel-based drug design using bioinformatics and cheminformatics tools.

Document type: 
Article

The Optimizability-Fidelty Trade-Off in Image Analysis

Author: 
Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2011-09-01
Abstract: 

Image analysis problems (e.g. segmentation and registration) are typically formulated in an optimization framework (energy-minimization). In this work, I present two important recurring questions: What to optimize and how to optimize and expose the resulting tradeoffs between the fidelity (of the domain and range) of the objective function and its optimizability.

Document type: 
Video