Bio-medical Ontologies Maintenance and Change Management

More documents

Recommendations

Info

42 E. De Francesco et al. to five criteria of analysis, that are, the typologies of recoverable data, the database schema types, thequery types, thequery methods and, finally, the export formats. For each classification criterium, the corresponding classification and the list of associated databases will be also graphically illustrated. In particular, in the graphs that follow, classes are represented by rectangles, whereas circles indicate sets of databases. Databases are indicated by numbers, according to the lists found in the Appendix and at http://siloe.deis.unical.it/ biodbSurvey/. 2.1 Recoverable Data Genomic databases contain a large set of data types. Some archives report only the sequence, the function and the organism corresponding to a given portion of genome, other ones contain also detailed information useful for biological or clinical analysis. As shown in Figure 2, data that are recoverable from genomic databases can been distinguished in six classes: Genomic segments: include all the nucleotide subsequences which are meaningful from a biological point of view, such as genes, clone/clontig sequences, polymorphisms, control regions, motifs and structural features of chromosomes. In detail: – Genes: are DNA subsequences originating functional product such as proteins and regulatory elements. All genomic databases contain information about genes. – Clone/contig regions: are sequences of in vitro copies of DNA regions, used to clone the DNA sequence of a given organism. Examples of databases storing information about clone/contig regions are ArkDB [1], SGD [32] and ZFIN [47]. – Polymorphisms: are frequent changes in nucleotide sequences, usually corresponding to a phenotypic change (e.g., the variety of eye colors in the population). As an example, BeetleBase [3] contains information about polymorphisms. – Control regions: are DNA sequences regulating the gene expression. A few databases store information about control regions. Among them there are Ensembl [13], FlyBase [42], SGD [32], and WormBase [28]. – Motifs (or patterns): are specific DNA segments having important functions and repeatedly occurring in the genome of a given organism. Examples of databases containing information about motifs are AureoList [2], Leproma [20] and VEGA [38]. – Structural features: can be further distinguished in telomeres, centromeres and repeats. · Telomeres: are the terminal regions of chromosomes. Génolevures [33], PlasmoDB [30] and SGD [32] store information about telomeres.
A Summary of Genomic Databases: Overview and Discussion 43 Fig. 2. Recoverable data typologies · Centromeres: represent the conjunction between short arms and long arms of the chromosome. BGI-RISe [51], PlasmoDB [30], RAD [24] and SGD [32] are databases containing information about centromeres. · Repeats: are repetitions of short nucleotide segments, repeatedly occurring in some of the regions of the chromosome. Among the others, CADRE [7], PlasmoDB [30] and ToxoDB [37] store information about repeats.
Page 1 and 2: Amandeep S. Sidhu and Tharam S. Dil
Page 3 and 4: Amandeep S. Sidhu and Tharam S. Dil
Page 5 and 6: Preface The molecular biology commu
Page 7 and 8: Preface VII biomedical systems, and
Page 9 and 10: X Contents Mining Clinical, Immunol
Page 11 and 12: 2 A.S. Sidhu, M. Bellgard, and T.S.
Page 19 and 20: Towards Bioinformatics Resourceomes
Page 25 and 26: 2 The New Waves Towards Bioinformat
Page 27 and 28: 2.4 Semantic Web Towards Bioinforma
Page 43 and 44: A Summary of Genomic Databases: Ove
Page 47: A Summary of Genomic Databases: Ove
Page 59 and 60: Appendix A Summary of Genomic Datab
Page 61 and 62: Protein Data Integration Problem Am
Page 63 and 64: Protein Data Integration Problem 57
Page 69 and 70: Fig. 3. Class Hierarchy of Protein
Page 77 and 78: 72 L. Stanescu, D. Dan Burdescu, an
Page 99 and 100:
94 L. Stanescu, D. Dan Burdescu, an
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
100 L. Stanescu, D. Dan Burdescu, a
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Bio-medical Ontologies Maintenance
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
TextToOnto (Maedche and Volz 2001)
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Extraction of Constraints from Biol
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Classifying Patterns in Bioinformat
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Mining Clinical, Immunological, and
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
Page 239 and 240:
Page 241 and 242:
Substructure Analysis of Metabolic
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
entry compound relation subtype com
Page 255 and 256:
Page 257 and 258:
Running Time 4500 4000 3500 3000 25
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
6 Conclusion Substructure Analysis
Page 265 and 266:
Page 267 and 268:
266 J.Y. Chen, S. Taduri, and F. Ll
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
Completing the Total Wellbeing Puzz
Page 283 and 284:
Page 285 and 286:
Page 287 and 288:
Page 289 and 290:
Page 291 and 292:
Page 293 and 294:
Page 295 and 296:
296 M. Fernandez, M. Villasana, and
Page 297 and 298:
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307 and 308:
Page 309 and 310:
Page 311 and 312:
Page 313 and 314:
Page 315 and 316:
Genetic Algorithm in Ab Initio Prot
Page 317 and 318:
Page 319 and 320:
Page 321 and 322:
Page 323 and 324:
Page 325 and 326:
Page 327 and 328:
Page 329 and 330:
Page 331 and 332:
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Page 339 and 340:
Page 341:
Author Index Apiletti, Daniele 169
show all

Bio-medical Ontologies Maintenance and Change Management

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?