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Biocomputing 2012 - Proceedings Of The Pacific Symposium.

Yazar:Klein, Teri E
Katkıda bulunan(lar):Jung, Tiffany A | Hunter, Lawrence | Dunker, A Keith | Altman, Russ B
Materyal türü: KonuKonuYayıncı: Singapore : World Scientific Publishing Company, 2011Telif hakkı tarihi: �2012Tanım: 1 online resource (455 pages)İçerik türü:text Ortam türü:computer Taşıyıcı türü: online resourceISBN: 9789814366496Tür/Form:Electronic books.Ek fiziksel biçimler:Print version:: Biocomputing 2012 - Proceedings Of The Pacific SymposiumÇevrimiçi kaynaklar: Click to View
İçindekiler:
Intro -- Contents -- Preface -- IDENTIFICATION OF ABERRANT PATHWAY AND NETWORK ACTIVITY FROM HIGH-THROUGHPUT DATA -- Session Introduction Rachel Karchin, Michael F. Ochs, Joshua M. Stuart, and Joel S. Bader -- Introduction -- Genetic interaction networks in model organisms -- Human data and local subnetworks -- Converging problems and challenges -- References -- SSLPred : Predicting Synthetic Sickness Lethality Nirmalya Bandyopadhyayy, Sanjay Ranka, and Tamer Kahveci -- 1. Introduction -- 2. Background -- 3. Methods -- 3.1. Problem Formulation and Notation -- 3.2. Between Pathway Conjectures -- 3.3. Regression based solution -- 4. Experiments -- 4.1. Datasets -- 4.2. Comparison with Hescott's Method -- 5. Conclusion -- References -- Predicting the Effects of Copy-Number Variation in Double and Triple Mutant Combinations Gregory W. Carter, Michelle Hays, Song Li, and Timothy Galitski -- 1. Introduction -- 2. Network Model Inference -- 2.1.1. Yeast Gene Expression Profiling -- 2.1.2. Singular Value Decomposition Analysis -- 2.1.3. Genetic Influences Decomposition -- 2.2. Predictions and Validation for a Multicopy Perturbation -- 2.2.1. Prediction for Multi-Copy Strains -- 2.2.2. Experimental Test of Predictions -- 3. Discussion and Conclusions -- 4. Supplementary Material -- 5. Acknowledgments -- References -- Integrative Network Analysis to Identify Aberrant Pathway Networks in Ovarian Cancer Li Chen, Jianhua Xuan, Jinghua Gu, Yue Wang, Li Chen, Zhen Zhang, Tian-Li Wang, and Ie-Ming Shih -- 1. Introduction -- 2. Materials and method -- 2.1. Integrative framework -- 2.2. Data description -- 2.3. DNA copy number consensus region detection -- 2.4. Network identification by bootstrapping MRF (BMRF) -- 2.5. Network constrained support vector machines (NetSVM) -- 2.6. Classification performance merits and survival analysis -- 3. Results and discussion.
4. Conclusion -- 5. Acknowledgments -- References -- Role of Synthetic Genetic Interactions in Understanding Functional Interactions Among Pathways Shahin Mohammadi, Giorgos Kollias, and Ananth Grama -- 1. Introduction -- 2. Methods -- 2.1. Notations -- 2.2. Performance of local methods for predicting functional similarity of gene pairs -- 2.3. Constructing the neighborhood overlap graph (NOG) -- 2.4. Identifying interaction ports and inferring cross-pathway dependencies -- 3. Results -- 3.1. Datasets -- 3.1.1. Genetic interaction network -- 3.1.2. Functional annotations -- 3.1.3. Availability -- 3.2. Similarity of genetic neighborhood as a predictor of functional similarity -- 3.3. Constructing KEGG crosstalk map -- 4. Discussion -- 5. Acknowledgments -- References -- Discovery of Mutated Subnetworks Associated with Clinical Data in Cancer Fabio Vandin, Patrick Clay, Eli Upfal, and Benjamin J. Raphael -- 1. Introduction -- 2. Methods -- 2.1. Generalized HotNet -- 2.2. Adaptation to Clinical Data -- 2.2.1. Gene Scores -- 2.2.2. Selection of parameters t and -- 2.2.3. The Null Hypothesis Distribution -- 3. Results -- 3.1. Simulated data -- 3.2. Ovarian TCGA data -- 4. Discussion -- 5. Acknowledgements -- References -- INTRINSICALLY DISORDERED PROTEINS: ANALYSIS, PREDICTION, SIMULATION, AND BIOLOGY -- Session Introduction Jianhan Chen, Jianlin Cheng, and A. Keith Dunker -- 1. Introduction -- 2. Papers in this Session -- Analysis of IDPs' function and evolution -- Simulation of IDPs' conformation -- Prediction of IDPs -- Acknowledgements -- Quasi-Anharmonic Analysis Reveals Intermediate States in the Nuclear Co-Activator Receptor Binding Domain Ensemble Virginia M. Burger, Arvind Ramanathan, Andrej J. Savol, Christopher B. Stanly, Pratul K. Agarwal, and Chakra S. Chennubhotla -- 1. Introduction -- 2. Approach -- 3. Molecular Simulations for NCBD.
4. dQAA: Quasi-anharmonic analysis in the dihedral angle space -- 5. Hierarchical clustering in the dQAA-space to identify meta-stable states -- 6. Intermediate states of ligand-free NCBD access ligand-bound conformations -- 7. Conclusions and Future Work -- References -- Efficient Construction of Disordered Protein Ensembles in a Bayesian Framework with Optimal Selection of Conformations Charles K. Fisher, Orly Ullman, and Collin M. Stultz -- 1. Introduction -- 2. Theory -- 2.1. Optimal Structure Selection -- 2.2. Variational Bayesian Weighting -- 2.3. Variational Bayes with Structure Selection -- 2.3. Approximate Confidence Intervals -- 3. Results and Discussion -- 3.1. Validation with Reference Ensembles -- 3.2. (Sa(B-Synuclein Ensemble -- 4. Conclusions -- 5. Acknowledgements -- References -- Correlation Between Posttranslational Modification and Intrinsic Disorder in Protein Jianjiong Gao and Dong Xu -- 1. Background -- 2. Results -- 2.1. Correlation of PTM sites and their predicted disorder scores -- 2.2. Correlation of PTM sites and their spatial fluctuations in NMR 3-D structures -- 2.3. Spatial fluctuation changes in 3-D structure due to PTM -- 3. Discussion -- Acknowledgments -- References -- Intrinsic Disorder Within and Flanking the DNA-Binding Domains of Human Transcription Factors Xin Guo, Martha L. Bulyk, and Alexander J. Hartemink -- 1. Introduction -- 2. Materials and Methods -- 2.1. Constructing the TF and non-TF control sets of proteins -- 2.2. Comparing the TF and non-TF control sets of proteins -- 2.3. Identifying DNA-binding domains (DBDs) and their locations within proteins -- 2.4. Predicting intrinsically disordered regions (IDRs) and their locations within proteins using multiple existing methods -- 2.5. Defining disorder features: spatial relationships of IDRs relative to DBDs within TFs.
2.6. Calculating statistical significance of disorder features -- 3. Results -- 3.1. Comparing the three methods for predicting IDRs within proteins -- 3.2. Assessing significance of order or disorder within and anking human TF DBDs -- 3.3. Investigating detailed spatial relationships of IDRs relative to DBDs within TFs -- 3.4. Analyzing spatial relationships for some DBD classes prevalent in human TFs -- 3.4.1. Zinc ngers -- 3.4.2. Homeobox -- 3.4.3. HLH -- 4. Discussion -- 5. Acknowledgments -- References -- Intrinsic Protein Disorder and Protein-Protein Interactions Wei-Lun Hsu, Christopher Oldfield, Jingwei Meng, Fei Huang, Bin Xue, Vladimir N. Uversky, Pedro Romero, and A. Keith Dunker -- 1. Introduction -- 2. Results -- 2.1 Disordered hub dataset -- 2.2 Functional consequences of MoRF (or ELM) binding -- 2.3 Binding to multiple partners, conservation at structure-matching sites -- 3. Discussion -- 4. Methods -- 4.1 Disordered hub dataset -- 4.2 Sequence and Structure analysis -- References -- Subclassifying Disordered Proteins by the CH-CDF Plot Method Fei Huang, Christopher Oldfield, Jingwei Meng, Wei-lun Hsu, Bin Xue, Vladimir N. Uversky, Pedro Romero, and A. Keith Dunker -- 1. Introduction -- 2. Results -- 2.1 CH-CDF plot -- 2.2 PDB coverage -- 2.3 Sequence window CH-CDF analysis -- 2.4 Match PDB coverage to disorder prediction -- 2.5 Function analysis for each quadrant -- 3. Discussion -- 3.1 Overview -- 3.2 Structural Partitioning by the CH-CDF plot -- 3.2 The rare protein quadrant (Q1) -- 3.3 Disorder subtypes and IDP functions -- 4. Methods -- 4.1 Protein data -- 4.2 PDB Coverage -- 4.2 GO term analysis -- References -- Coevolved Residues and the Functional Association for Intrinsically Disordered Protein Chan-Seok Jeong and Dongsup Kim -- 1. Introduction -- 2. Materials and methods -- 2.1. Data set.
2.2. Multiple sequence alignment construction -- 2.3. Coevolution estimation -- 2.4. Sequence conservation estimation -- 2.5. Disorder conservation estimation -- 2.6. Functional categories -- 3. Results -- 3.1. Distribution of coevolved residues for disordered proteins -- 3.2. Relationship between coevolution and functions -- 4. Discussion -- Acknowledgments -- References -- Cryptic Disorder: An Order-Disorder Transformation Regulates the Function of Nucleophosmin Diana M. Mitrea and Richard W. Kriwacki -- 1. Biological Function and Structural Features of Npm -- 2. Alteration of the electrostatic features of Npm-N through phosphorylation -- 3. In Silico site-directed mutagenesis -- 4. Probing for structural strain in Npm-N -- 5. Mechanistic insights on Npm's order-disorder polymorphism -- 6. Materials and Methods -- References -- Functional Annotation of Intrinsically Disordered Domains by Their Amino Acid Content Using IDD Navigator Ashwini Patil, Shunsuke Teraguchi, Huy Dinh, Kenta Nakai, and Daron M Standley -- 1. Introduction Intrinsically disordered domains -- 2. Methodology -- 2.1. Preparation of IDD dataset -- 2.2. Similarity scores -- 2.2.1. Similarity score based on Euclidean distance -- 2.2.2. BLAST score -- 2.3. Pfam domain and Gene Ontology term prediction -- 2.4. Evaluation of function prediction -- 2.5. Web server -- 3. Results and Discussion -- 3.1 IDD Navigator Function prediction -- 3.2 Comparing different methods in IDD Navigator -- 3.3 Function prediction for IDD clusters -- 3.4 Case Studies -- 3.4.1 GRA15 from T. gondii -- 3.4.2 Cyclon from M. musculus -- 3.4.3 STIM1 from M. musculus -- 3.4.4 ROP16 from T. gondii -- 4. Conclusions -- 5. Acknowledgements -- References -- On the Complementarity of the Consensus-Based Disorder Prediction Zhenling Peng and Lukasz Kurgan -- 1. Introduction -- 2. Methods.
2.1. Considered disorder predictors.
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Intro -- Contents -- Preface -- IDENTIFICATION OF ABERRANT PATHWAY AND NETWORK ACTIVITY FROM HIGH-THROUGHPUT DATA -- Session Introduction Rachel Karchin, Michael F. Ochs, Joshua M. Stuart, and Joel S. Bader -- Introduction -- Genetic interaction networks in model organisms -- Human data and local subnetworks -- Converging problems and challenges -- References -- SSLPred : Predicting Synthetic Sickness Lethality Nirmalya Bandyopadhyayy, Sanjay Ranka, and Tamer Kahveci -- 1. Introduction -- 2. Background -- 3. Methods -- 3.1. Problem Formulation and Notation -- 3.2. Between Pathway Conjectures -- 3.3. Regression based solution -- 4. Experiments -- 4.1. Datasets -- 4.2. Comparison with Hescott's Method -- 5. Conclusion -- References -- Predicting the Effects of Copy-Number Variation in Double and Triple Mutant Combinations Gregory W. Carter, Michelle Hays, Song Li, and Timothy Galitski -- 1. Introduction -- 2. Network Model Inference -- 2.1.1. Yeast Gene Expression Profiling -- 2.1.2. Singular Value Decomposition Analysis -- 2.1.3. Genetic Influences Decomposition -- 2.2. Predictions and Validation for a Multicopy Perturbation -- 2.2.1. Prediction for Multi-Copy Strains -- 2.2.2. Experimental Test of Predictions -- 3. Discussion and Conclusions -- 4. Supplementary Material -- 5. Acknowledgments -- References -- Integrative Network Analysis to Identify Aberrant Pathway Networks in Ovarian Cancer Li Chen, Jianhua Xuan, Jinghua Gu, Yue Wang, Li Chen, Zhen Zhang, Tian-Li Wang, and Ie-Ming Shih -- 1. Introduction -- 2. Materials and method -- 2.1. Integrative framework -- 2.2. Data description -- 2.3. DNA copy number consensus region detection -- 2.4. Network identification by bootstrapping MRF (BMRF) -- 2.5. Network constrained support vector machines (NetSVM) -- 2.6. Classification performance merits and survival analysis -- 3. Results and discussion.

4. Conclusion -- 5. Acknowledgments -- References -- Role of Synthetic Genetic Interactions in Understanding Functional Interactions Among Pathways Shahin Mohammadi, Giorgos Kollias, and Ananth Grama -- 1. Introduction -- 2. Methods -- 2.1. Notations -- 2.2. Performance of local methods for predicting functional similarity of gene pairs -- 2.3. Constructing the neighborhood overlap graph (NOG) -- 2.4. Identifying interaction ports and inferring cross-pathway dependencies -- 3. Results -- 3.1. Datasets -- 3.1.1. Genetic interaction network -- 3.1.2. Functional annotations -- 3.1.3. Availability -- 3.2. Similarity of genetic neighborhood as a predictor of functional similarity -- 3.3. Constructing KEGG crosstalk map -- 4. Discussion -- 5. Acknowledgments -- References -- Discovery of Mutated Subnetworks Associated with Clinical Data in Cancer Fabio Vandin, Patrick Clay, Eli Upfal, and Benjamin J. Raphael -- 1. Introduction -- 2. Methods -- 2.1. Generalized HotNet -- 2.2. Adaptation to Clinical Data -- 2.2.1. Gene Scores -- 2.2.2. Selection of parameters t and -- 2.2.3. The Null Hypothesis Distribution -- 3. Results -- 3.1. Simulated data -- 3.2. Ovarian TCGA data -- 4. Discussion -- 5. Acknowledgements -- References -- INTRINSICALLY DISORDERED PROTEINS: ANALYSIS, PREDICTION, SIMULATION, AND BIOLOGY -- Session Introduction Jianhan Chen, Jianlin Cheng, and A. Keith Dunker -- 1. Introduction -- 2. Papers in this Session -- Analysis of IDPs' function and evolution -- Simulation of IDPs' conformation -- Prediction of IDPs -- Acknowledgements -- Quasi-Anharmonic Analysis Reveals Intermediate States in the Nuclear Co-Activator Receptor Binding Domain Ensemble Virginia M. Burger, Arvind Ramanathan, Andrej J. Savol, Christopher B. Stanly, Pratul K. Agarwal, and Chakra S. Chennubhotla -- 1. Introduction -- 2. Approach -- 3. Molecular Simulations for NCBD.

4. dQAA: Quasi-anharmonic analysis in the dihedral angle space -- 5. Hierarchical clustering in the dQAA-space to identify meta-stable states -- 6. Intermediate states of ligand-free NCBD access ligand-bound conformations -- 7. Conclusions and Future Work -- References -- Efficient Construction of Disordered Protein Ensembles in a Bayesian Framework with Optimal Selection of Conformations Charles K. Fisher, Orly Ullman, and Collin M. Stultz -- 1. Introduction -- 2. Theory -- 2.1. Optimal Structure Selection -- 2.2. Variational Bayesian Weighting -- 2.3. Variational Bayes with Structure Selection -- 2.3. Approximate Confidence Intervals -- 3. Results and Discussion -- 3.1. Validation with Reference Ensembles -- 3.2. (Sa(B-Synuclein Ensemble -- 4. Conclusions -- 5. Acknowledgements -- References -- Correlation Between Posttranslational Modification and Intrinsic Disorder in Protein Jianjiong Gao and Dong Xu -- 1. Background -- 2. Results -- 2.1. Correlation of PTM sites and their predicted disorder scores -- 2.2. Correlation of PTM sites and their spatial fluctuations in NMR 3-D structures -- 2.3. Spatial fluctuation changes in 3-D structure due to PTM -- 3. Discussion -- Acknowledgments -- References -- Intrinsic Disorder Within and Flanking the DNA-Binding Domains of Human Transcription Factors Xin Guo, Martha L. Bulyk, and Alexander J. Hartemink -- 1. Introduction -- 2. Materials and Methods -- 2.1. Constructing the TF and non-TF control sets of proteins -- 2.2. Comparing the TF and non-TF control sets of proteins -- 2.3. Identifying DNA-binding domains (DBDs) and their locations within proteins -- 2.4. Predicting intrinsically disordered regions (IDRs) and their locations within proteins using multiple existing methods -- 2.5. Defining disorder features: spatial relationships of IDRs relative to DBDs within TFs.

2.6. Calculating statistical significance of disorder features -- 3. Results -- 3.1. Comparing the three methods for predicting IDRs within proteins -- 3.2. Assessing significance of order or disorder within and anking human TF DBDs -- 3.3. Investigating detailed spatial relationships of IDRs relative to DBDs within TFs -- 3.4. Analyzing spatial relationships for some DBD classes prevalent in human TFs -- 3.4.1. Zinc ngers -- 3.4.2. Homeobox -- 3.4.3. HLH -- 4. Discussion -- 5. Acknowledgments -- References -- Intrinsic Protein Disorder and Protein-Protein Interactions Wei-Lun Hsu, Christopher Oldfield, Jingwei Meng, Fei Huang, Bin Xue, Vladimir N. Uversky, Pedro Romero, and A. Keith Dunker -- 1. Introduction -- 2. Results -- 2.1 Disordered hub dataset -- 2.2 Functional consequences of MoRF (or ELM) binding -- 2.3 Binding to multiple partners, conservation at structure-matching sites -- 3. Discussion -- 4. Methods -- 4.1 Disordered hub dataset -- 4.2 Sequence and Structure analysis -- References -- Subclassifying Disordered Proteins by the CH-CDF Plot Method Fei Huang, Christopher Oldfield, Jingwei Meng, Wei-lun Hsu, Bin Xue, Vladimir N. Uversky, Pedro Romero, and A. Keith Dunker -- 1. Introduction -- 2. Results -- 2.1 CH-CDF plot -- 2.2 PDB coverage -- 2.3 Sequence window CH-CDF analysis -- 2.4 Match PDB coverage to disorder prediction -- 2.5 Function analysis for each quadrant -- 3. Discussion -- 3.1 Overview -- 3.2 Structural Partitioning by the CH-CDF plot -- 3.2 The rare protein quadrant (Q1) -- 3.3 Disorder subtypes and IDP functions -- 4. Methods -- 4.1 Protein data -- 4.2 PDB Coverage -- 4.2 GO term analysis -- References -- Coevolved Residues and the Functional Association for Intrinsically Disordered Protein Chan-Seok Jeong and Dongsup Kim -- 1. Introduction -- 2. Materials and methods -- 2.1. Data set.

2.2. Multiple sequence alignment construction -- 2.3. Coevolution estimation -- 2.4. Sequence conservation estimation -- 2.5. Disorder conservation estimation -- 2.6. Functional categories -- 3. Results -- 3.1. Distribution of coevolved residues for disordered proteins -- 3.2. Relationship between coevolution and functions -- 4. Discussion -- Acknowledgments -- References -- Cryptic Disorder: An Order-Disorder Transformation Regulates the Function of Nucleophosmin Diana M. Mitrea and Richard W. Kriwacki -- 1. Biological Function and Structural Features of Npm -- 2. Alteration of the electrostatic features of Npm-N through phosphorylation -- 3. In Silico site-directed mutagenesis -- 4. Probing for structural strain in Npm-N -- 5. Mechanistic insights on Npm's order-disorder polymorphism -- 6. Materials and Methods -- References -- Functional Annotation of Intrinsically Disordered Domains by Their Amino Acid Content Using IDD Navigator Ashwini Patil, Shunsuke Teraguchi, Huy Dinh, Kenta Nakai, and Daron M Standley -- 1. Introduction Intrinsically disordered domains -- 2. Methodology -- 2.1. Preparation of IDD dataset -- 2.2. Similarity scores -- 2.2.1. Similarity score based on Euclidean distance -- 2.2.2. BLAST score -- 2.3. Pfam domain and Gene Ontology term prediction -- 2.4. Evaluation of function prediction -- 2.5. Web server -- 3. Results and Discussion -- 3.1 IDD Navigator Function prediction -- 3.2 Comparing different methods in IDD Navigator -- 3.3 Function prediction for IDD clusters -- 3.4 Case Studies -- 3.4.1 GRA15 from T. gondii -- 3.4.2 Cyclon from M. musculus -- 3.4.3 STIM1 from M. musculus -- 3.4.4 ROP16 from T. gondii -- 4. Conclusions -- 5. Acknowledgements -- References -- On the Complementarity of the Consensus-Based Disorder Prediction Zhenling Peng and Lukasz Kurgan -- 1. Introduction -- 2. Methods.

2.1. Considered disorder predictors.

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