Genome Informatics 2008: Proceedings of the 19th International Conference, Gold Coast, Queensland, Australia 1-3 December 2008 (Genome Informatics Series)

CONTENTS......Page 6
Preface......Page 10
Acknowledgments......Page 12
Committees......Page 14
Part A Full Papers......Page 18
1. Introduction......Page 20
2. Approach Overview......Page 21
2.1 Dataset volumes and data management......Page 22
2.2 Data subset extraction and optimal parameter evaluation for mapping......Page 23
3. Pachycladon Transcriptome short-read analysis......Page 24
3.1. Mapping against Arabidopsis ESTs......Page 25
3.2. de novo Assembly......Page 26
3.3. Gene Analysis......Page 28
4. Discussion......Page 29
Acknowledgments......Page 30
References......Page 31
1. Introduction......Page 32
2.1. Background: 3-Gamma and the finder's null distribution......Page 33
2.2. Incorporating local GC content in our confidence p-value......Page 35
3. Results on the Harbison dataset......Page 37
3.2. How well calibrated are these p-values?......Page 38
3.3. Using the p-values to improve our results......Page 39
4.4. Is the predicted motif a known motif?......Page 40
5. Conclusion & Future Research......Page 41
References......Page 42
1. Introduction......Page 44
2.1. Multi-Marker Correlation on Haplotype Data......Page 46
2.2. Calculating r2 Values on Genotype Data......Page 47
3.2. The Algorithm for the 2-Marker Correlation Model......Page 49
3.4. Time Complexity......Page 51
4. Experimental Result......Page 52
5. Conclusion......Page 54
References......Page 55
1. Introduction......Page 59
2.1. Phenotype MicroArray Data and E. coli Strains......Page 60
2.2. Vectorization of Data......Page 62
2.5. Metabolic Pathway Data and Extraction of Path from Graph......Page 63
3.2. Clustering Results......Page 64
3.3. Phenotypic and Metabolic Pathway Relationship......Page 66
Appendix......Page 67
References......Page 69
1. Introduction......Page 70
2. Preliminaries and problem formulation......Page 72
3.1. Canonical representation of trees and the branching operation......Page 73
3.2. Bounding operations......Page 75
4. Alternative problem formulation......Page 77
6. Conclusion......Page 78
Acknowledgments......Page 79
References......Page 81
1. Introduction......Page 82
2. Related Works......Page 84
3.1. Graph Representation......Page 85
3.2.1. Edge-product Graph Construction......Page 86
3.2.2. Maximum Clique Detection......Page 87
3.3. Refinement and Scoring......Page 88
4. Results and Discussions......Page 89
4.1. Search Accuracy......Page 90
References......Page 92
1. INTRODUCTION......Page 94
2.1. Competition between Mutually Exclusive Interaction Partners......Page 96
2.2. Extraction of SPIC......Page 98
3. RESULT......Page 99
3.1. Comparative results with MCODE......Page 101
3.2. Comparative results with LCMA......Page 102
4. CONCLUSION......Page 103
References......Page 104
1. Introduction......Page 106
2.2. Reaction Attributes and Compartmentalization......Page 108
3.1. Data storage......Page 109
3.2. Checking consistency of reaction equation......Page 110
3.4. Lumping Reactions......Page 111
3.6. Finding Network Gaps......Page 112
4. Metabolic Network Properties......Page 114
5. Discussion......Page 116
References......Page 117
Predicting Differences in Gene Regulatory Systems by State Space Models R. Yamaguchi, S. [moto, M. Yamauchi, M. Nagasaki, R. Yoshida, T. Shimamura, Y. Hatanaka, K. Ueno, T. Higuchi, N. Gotoh & S. Miyano......Page 118
1. Introduction......Page 119
2.1. Differentially regulated genes......Page 121
2.2. State Space Models......Page 122
2.3. Prediction of differentially regulated genes by SSM......Page 123
3.1. Time-course gene expression data......Page 124
3.3. Parameter estimation......Page 125
3.4. Differentially regulated genes......Page 126
4. Discussion and Conclusion......Page 128
References......Page 129
1. Introduction......Page 131
2. From single cell to population model......Page 132
3. Algorithm......Page 134
4. Results......Page 136
5. Conclusions......Page 139
Acknowledgement......Page 141
References......Page 142
1. Introduction......Page 143
2.1. Model specification......Page 145
2.2. Parameter learning using EM algorithm......Page 146
2.3. Model analysis......Page 147
3.2. Re-analysis of two cardiovascular disease datasets......Page 148
4. Discussion......Page 151
References......Page 153
1. Introduction......Page 155
2.1. Local interacting score......Page 157
2.2.1. Generating protein groups......Page 158
2.3. The final interacting score of protein pairs......Page 159
3.1. Functional homogeneity and localization coherence......Page 160
3.1.2. Assessing and predicting interactions......Page 161
3.2. Five-fold cross validation......Page 163
4. Discussion and Conclusion......Page 164
References......Page 165
1. Introduction......Page 167
2. The M2 codon substitution model......Page 169
3. The serial codon substitution model, sM2......Page 171
4. Variants of sM2......Page 172
5. Phylogenetic estimation of substitution parameters......Page 173
6. Likelihood Ratio Tests (LRTs)......Page 174
8. An example......Page 175
9. Conclusions......Page 178
References......Page 179
1. Introduction......Page 182
2. A phylogenomic approach for inferring phylogenies......Page 183
2.1. Analysis of EGT in Thalassiosira pseudonana......Page 184
3. EGT of red algal genes in Thalassiosira pseudonana......Page 186
3.1. Examples of EGT in chromalveolates......Page 189
4. Performance and limitations......Page 191
References......Page 192
1. Introduction......Page 194
2.2. Promoter Sequences......Page 195
2.5. Plant Materials, stress treatment, and RT-PCR analysis......Page 196
3.1. In silico Gene Identification and Gene Ontology Analysis......Page 197
3.2. Cross Validation Using Gene Expression Profiling Data......Page 198
3.3 Experimental Validation and Comparison with Other Methods......Page 200
4. Conclusions......Page 201
References......Page 202
1. Introduction......Page 205
2.2. Motif Prediction and Selection......Page 207
2.4. Rule Generation......Page 208
2.6. Performance Evaluation......Page 209
3.1. Overall Performance......Page 210
3.2. Focus on the Best Run......Page 211
4. Concluding Remarks......Page 213
References......Page 215
1. Introduction......Page 217
2. Nonnegative Principal Component Analysis (NPCA)......Page 218
3. NPCA-based Cancer Molecular Pattern Classification......Page 220
4. Experimental Results......Page 221
4.1 Classification results comparisons with those of other algorithms......Page 224
5. Discussions and Conclusions......Page 227
References......Page 228
1. Introduction......Page 229
2.2. Phase shift oj circadian rhythm by light......Page 231
3.2. HFPN model under free-running conditions......Page 232
3.3. Response of HFPN model to a light pulse......Page 233
4.1. Entrainment by light with the extended HFPN model......Page 234
4.2. Jet Lag Simulation: Flying from Tokyo to New York......Page 237
5. Discussion and Future works......Page 238
References......Page 239
Part B Keynote Addresses......Page 242
Abstract......Page 244
References......Page 245
References......Page 246
Abstract......Page 247
Abstract......Page 248
Abstract......Page 249
Abstract......Page 250
Author Index......Page 252