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Computing H/D-Exchange rates of single residues from data of proteolytic fragments

BMC bioinformatics 2010年第1期11卷 1-12页

作者： Althaus, Ernst Canzar, Stefan Ehrler, Carsten Emmett, Mark R. Karrenbauer, Andreas Marshall, Alan G. Meyer-Bäse, Anke Tipton, Jeremiah D. Zhang, Hui-Min Institut für Informatik Fachbereich 08 Staudingerweg 9 55099 Mainz Germany Algorithmic Computational Biology Centrum Wiskunde and Informatica Amsterdam Netherlands Center for Bioinformatics Computer Science Department Saarland University 66041 Saarbrücken Germany Ion Cyclotron Resonance Program National High Magnetic Field Laboratory Florida State University 1800 East Paul Dirac Drive Tallahassee FL 32310-4005 United States Department of Chemistry and Biochemistry Florida State University 95 Chieftain Way Tallahassee FL 32306 United States Institute of Mathematics EPFL Station 8 1015 Lausanne Switzerland Department of Scientific Computing Florida State University Tallahassee FL 32306-4120 United States

Background: Protein conformation and protein/protein interaction can be elucidated by solution-phase Hydrogen/Deuterium exchange (sHDX) coupled to high-resolution mass analysis of the digested protein or protein complex. In sHDX experiments mutant proteins are compared to wild-type proteins or a ligand is added to the protein and compared to the wild-type protein (or mutant). The number of deuteriums incorporated into the polypeptides generated from the protease digest of the protein is related to the solvent accessibility of amide protons within the original protein ***: In this work, sHDX data was collected on a 14.5 T FT-ICR MS. An algorithm was developed based on combinatorial optimization that predicts deuterium exchange with high spatial resolution based on the sHDX data of overlapping proteolytic fragments. Often the algorithm assigns deuterium exchange with single residue ***: With our new method it is possible to automatically determine deuterium exchange with higher spatial resolution than the level of digested fragments. © 2010 Althaus et al;licensee BioMed Central Ltd.

关键词： Proteins

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Selecting SNPs for pharmacogenomic association study

Selecting SNPs for pharmacogenomic association study

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： TaeJin Ahna Kyunghee Park Dae-Soon Son Nam Huh Kyusang Lee Taejung Bae Jung-Sun Park So-Hee Oh Ji-Hyun Lee Kyoohyuong Rho Sung Kim Tae Sung Park Samsung Advanced Institute of Technology Suwon Gyeonggi-do KR Genetic Analysis Group Samsung Advanced Institute of Technology Suwon Korea College of Pharmacy Seoul National University Seoul Korea Bioinformatics Program Seoul National University Seoul Korea Department of Statistics Seoul National University Seoul Korea

SNP genotyping device is an essential tool in the upcoming era of personal genome, personalized medicine. Human genome has more than 10 million SNPs while conventional SNP genotyping device can only hold 1 million SNPs. Thus intelligent SNP contents selection is required to maximize the value of SNP genotyping device. Here, we developed a new selection algorithm and applied this method to design genotyping contents for cancer and pharmacogenomic association study. This approach significantly increased the product value when compared to contents of competitive SNP genotyping product.

关键词： bioinformatics Drugs Genomics Cancer Databases Humans Tagging

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The keyword-based and semantic-driven data matching approach for assisting structuralizing the textual clinical documents

The keyword-based and semantic-driven data matching approach...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Chi-Huang Chen Xiao-Ou Ping Zi-Jun Wang Sheau-Ling Hsieh Li-Chin Chen Yi-Ju Tseng Ching-Wei Hsu Feipei Lai Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronic and Bioinformatics National Taiwan University Taipei Taiwan EECS International Graduate Program Institute IT Center National Chiao Tung University Hsinchu Taiwan

The clinical data stored in the health information system can be categorized as two types including structuralized data and non-structuralized ones. In the paper, a data extraction system is developed to assist data retrieval from the non-structuralized textual clinical documents such as radiology reports, pathology reports, etc. The system provides keyword-based and semantic-driven data matching methodology to extract the specific information from the textual clinical documents. The matching methodology provides the capabilities to recognize the selected keywords and the related semantics in the documents. Through the extraction verification interface, clinicians can extract and verify the matched information semi-automatically. The extracted data can be filled into predefined case-oriented templates. The structuralized data can be stored back into the clinical data warehouse for further analyzing. Moreover, the case-oriented templates can support collecting corresponding extracted data for various researches.

关键词： Data mining Semantics Pattern matching Tumors Radiology Pathology Cancer

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A pilot study for genome wide association study (GWAS) in patients with juvenile idiopathic arthritis (JIA) and their parents

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Pediatric Rheumatology 2011年第1期9卷 1-1页

作者： Seza Ozen Yelda Bilginer Nesrin Besbas Yesim Aydin Son Yener Tuncel Erkan Demirkaya Cengizhan Acikel Bilge Volkan Salancı Mehmet Alikasifoglu Ozgur Kasapcopur Erbil Unsal Hacettepe University Faculty of Medicine Department of Pediatrics Pediatric Nephrology and Rheumatology Unit Ankara Turkey Department of Health Informatics METU Informatics Institute Ankara Turkey METU Informatics Institute Bioinformatics Graduate Program Ankara Turkey Gulhane Military Medical Academy Pediatric Nephrology & Rheumatology Unit Ankara Turley Department of Public Health Division of Epidemiology Gulhane Military Medical Faculty Ankara Turkey Department of Pediatrics Clinical Genetics Unit Hacettepe University Faculty of Medicine Ankara Turkey Istanbul University Cerrahpasa Medical School Department of Pediatric Rheumatology Istanbul Turley Department of Pediatrics Division of Immunology- Rheumatology Dokuz Eylul University Faculty of Medicine Izmir Turkey

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Characterizing Canine Lymphoma As a Potential Large Animal Model of Human Diffuse Large B-Cell Lymphoma

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Blood 2011年第21期118卷 5193-5193页

作者： Kristy L. Richards Alison Motsinger-Reif Hsiao-Wei Chen Yuri D. Fedoriw Cheng Fan Dahlia M. Nielsen Rachael Thomas Chris Smith Sandeep S. Dave Charles M. Perou Matthew Breen Steven E. Suter Division of Hematology/Oncology University of North Carolina Chapel Hill NC USA Department of Statistics North Carolina State University Genomic Sciences Graduate Program North Carolina State University Department of Pathology University of North Carolina Lineberger Comprehensive Cancer Center University of North Carolina Chapel Hill NC USA Department of Genetics North Carolina State University Center for Comparative Medicine & Translational Research North Carolina State University Raleigh NC USA Bioinformatics Research Center North Carolina State University Duke University Durham NC USA Department of Molecular Biomedical Sciences North Carolina State University Department of Clincal Sciences North Carolina State University

Abstract 5193 Diffuse large B-cell lymphoma (DLBCL) affects ∼25,000 people in the U.S. each year, and fewer than half of them are cured with standard therapy. DLBCL can be divided into two subtypes by gene expression profiling, germinal center B-cell (GCB) type and activated B-cell (ABC) type. ABC-type DLBCL patients have significantly poorer outcomes. To improve therapeutic options, better animal models that accurately mimic human DLBCL (hDLBCL) are needed. Canine DLBCL is one of the most common cancers in veterinary oncology. Similar to human DLBCL patients, dogs with lymphoma are treated with both CHOP-like regimens and autologous stem cell transplants. Morphologically, canine lymphomas are similar to hDLBCL, with shared histologic markers, such as CD20 and PAX5. With recent technologies based on knowledge of the canine genome sequence, it is now possible to evaluate dogs as a potential large-animal model for hDLBCL. We evaluated 58 canine B-cell lymphomas by generating comprehensive gene expression profiles and comparing them to previously published hDLBCL expression profiles. Canine B-cell lymphoma expression profiles were similar in some ways to hDLBCLs. For instance, increased expression of NF-kB pathway genes was noted in a subset of lymphomas, mirroring NF-kB pathway activation in human ABC-type DLBCL. Furthermore, immunoglobulin heavy chain (IGH) mutation status, which is correlated with ABC/GCB cell of origin in hDLBCL, separated canine DLBCL into two groups with statistically different progression-free and overall survival times. However, canine DLBCL differed from hDLBCL in other aspects, including rare immunohistochemical positivity for BCL6 and MUM1/IRF4. Collectively, these results define aspects of canine B-cell lymphomas that resemble hDLBCL, identifying molecular similarities that could allow dogs to be used as a representative model of hDLBCL. Further comparative studies, including therapeutic trials, could potentially improve outcomes in both sp

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Protein subcellular localization prediction of eukaryotes using a knowledge-based approach

Protein subcellular localization prediction of eukaryotes us...

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8th International Conference on bioinformatics: bioinformatics, InCoB 2009

作者： Lin, Hsin-Nan Chen, Ching-Tai Sung, Ting-Yi Ho, Shinn-Ying Hsu, Wen-Lian Bioinformatics Program Academia Sinica Taipei Taiwan Bioinformatics Lab. Institute of Information Science Academia Sinica Taipei Taiwan Institute of Bioinformatics National Chiao Tung University Hsinchu Taiwan

Background: The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles. Results: In this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPred site's performance. The experiment results show that KnowPredsite achieves higher prediction accuracy than ngLOC and Blast-hit method. For singlelocalized proteins, the overall accuracy of KnowPredsite is 91.7%. For multi-localized proteins, the overall accuracy of KnowPred site is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPredsite. Conclusion: KnowPredsite demonstrates the power of identifying re

关键词： Proteins

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Chi-Huang Chen Sheau-Ling Hsieh Yung-Ching Weng Wen-Yung Chang Feipei Lai Department of Electrical Engineering National Taiwan University Taipei Taiwan EECS International Graduate Program Institute IT Center National Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering National Taiwan University Taiwan Institute of Computer Science and Engineering College of Computer Science National Chiao Tung University Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.

关键词： Semantics Biomedical measurements Correlation Kernel Training Medical services Support vector machine classification

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Web-based healthcare Hand Drawing Management System

Web-based healthcare Hand Drawing Management System

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Sheau-Ling Hsieh Yung-Ching Weng Chi-Huang Chen Kai-Ping Hsu Jeng-Wei Lin Feipei Lai EECS International Graduate Program Institute IT Center National Chiao Tung University Hsinchu Taiwan Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Computer and Information Networking Center National Taiwan University Taipei Taiwan Department of Information Management Tunghai University Taichung Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan

The paper addresses Medical Hand Drawing Management System architecture and implementation. In the system, we developed four modules: hand drawing management module; patient medical records query module; hand drawing editing and upload module; hand drawing query module. The system adapts windows-based applications and encompasses web pages by *** hosting mechanism under web services platforms. The hand drawings implemented as files are stored in a FTP server. The file names with associated data, e.g. patient identification, drawing physician, access rights, etc. are reposited in a database. The modules can be conveniently embedded, integrated into any system. Therefore, the system possesses the hand drawing features to support daily medical operations, effectively improve healthcare qualities as well. Moreover, the system includes the printing capability to achieve a complete, computerized medical document process. In summary, the system allows web-based applications to facilitate the graphic processes for healthcare operations.

关键词： Medical services Biomedical imaging Printing Conferences Web services Graphics Databases

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Predicting helix-helix interactions from residue contacts in membrane proteins

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bioinformatics 2009年第8期25卷 996-1003页

作者： Lo, Allan Chiu, Yi-Yuan Rødland, Einar Andreas Lyu, Ping-Chiang Sung, Ting-Yi Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structural Biology Department of Life Sciences National Tsing Hua University Hsinchu Taiwan Bioinformatics Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Centre for Cancer Biomedicine University of Oslo NO-0027 Oslo Norway SAMBA Norwegian Computing Center Blindern NO-0314 Oslo P.O. Box 114 Norway

Motivation: Helix-helix interactions play a critical role in the structure assembly, stability and function of membrane proteins. On the molecular level, the interactions are mediated by one or more residue contacts. Although previous studies focused on helix-packing patterns and sequence motifs, few of them developed methods specifically for contact prediction. Results: We present a new hierarchical framework for contact prediction, with an application in membrane proteins. The hierarchical scheme consists of two levels: in the first level, contact residues are predicted from the sequence and their pairing relationships are further predicted in the second level. Statistical analyses on contact propensities are combined with other sequence and structural information for training the support vector machine classifiers. Evaluated on 52 protein chains using leave-one-out cross validation (LOOCV) and an independent test set of 14 protein chains, the two-level approach consistently improves the conventional direct approach in prediction accuracy, with 80% reduction of input for prediction. Furthermore, the predicted contacts are then used to infer interactions between pairs of helices. When at least three predicted contacts are required for an inferred interaction, the accuracy, sensitivity and specificity are 56%, 40% and 89%, respectively. Our results demonstrate that a hierarchical framework can be applied to eliminate false positives (FP) while reducing computational complexity in predicting contacts. Together with the estimated contact propensities, this method can be used to gain insights into helix-packing in membrane proteins. © 2009 The Author(s).

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Integrative biological systems modeling:challenges and opportunities

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中国高等学校学术文摘·计算机科学 2009年第1期3卷 92-100页

作者： Jialiang WU Eberhard VOIT Department of Mathematics Bioinformatics Program Georgia Institute of Technology Atlanta GA 30332 USA Integrative BioSystems Institute Georgia Institute of Technology Atlanta GA 30332 USA The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332 USA

Most biological systems are by nature hybrids consist of interacting discrete and continuous components,which may even operate on different time scales. Therefore,' it is desirable to establish modeling frameworks that are capable of combining deterministic and stochastic, discrete and continuous, as well as multi-timescale features. In the context of molecular systems biology, an example for the need of such a combination is the investigation of integrated biological pathways that contain gene regulatory, metabolic and signaling components, which may operate on different time scales and involve on-off switches as well as stochastic effects. The implementation of integrated hybrid systems is not trivial because most software is limited to one or the other of the dichotomies above. In this study, we first review the motivation for hybrid modeling. Secondly, by using the example of a toggle switch model, we illustrate a recently developed modeling framework that is based on the combination of biochemical systems theory (BST) and hybrid functional Petri nets (HFPN). Finally, we discuss remaining challenges and future opportunities.

关键词： biochemical systems theory Petri net hybrid modeling hybrid functional Petri net toggle switch canonical modeling stochastic delay

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