Databases and Systems

Recommendations

Info

PROFILESCAN Comparison of protein or DNA sequences with profile MOTIFGREP MOTIFGREPDB libraries Sequence identification using Regular Expressions Database searching using Regular Expressions 243 Protein Tools BLASTP Heuristic database similarity searching TBLASTN Heuristic database similarity searching (protein query, database translated) TFASTA Heuristic database similarity searching (protein query, database translated) TFASTX Heuristic database similarity searching (protein query, database translated) PRSS Protein sequence similarity statistical significance calculation CHOFAS Protein secondary structure prediction - Chou and Fasman GOR4 Protein secondary structure prediction - Garnier, Osguthorpe, and Robson GREASE Kyte - Doolittle hydropathicity profiles ROBUST Global pairwise sequence alignment SAPS Statistical analysis of protein sequence composition SIMP Smith-Waterman pairwise sequence alignment AASTATS Amino acid composition statistics MSA Multiple sequence alignment PROSITE Searching PROSITE database for protein sequence patterns PROSEARCH Searching PROSITE database for protein sequence patterns MPSSP Combined algorithm protein secondary structure prediction HTH Helix-Turn-Helix identification DSSP Secondary structure and solvent accessibility predictions Nucleic Acids Tools BLASTX Heuristic database similarity searching (translated nucleic acid query, protein database) BLASTN Heuristic database similarity searching (nucleic acid query, nucleic acid database) TBLASTX Heuristic database similarity searching (translated database) nucleic acid query, translated nucleic acid FASTX Heuristic database similarity searching (translated nucleic acid query, protein database) REVCOMP Generate the reverse-complement of a nucleic acid sequence SIXFRAME Translate a nucleic acid sequence in all six reading frames TACG Restriction enzyme recognition site identification TACGXLATE Translate a nucleic acid in a user defined reading frame Alignment Tools
244 MSASHADE Formatting and color-coding multiple sequence alignments TMAP Identification of transmembrane protein segments CLUSTAL_W Phylogenetic tree analyses PROTDIST Evolutionary distance matrix computations PROTPARS Unrooted phylogeny inference References 2. Akiyama, Y. and Kinehisa, M. (1995) Introduction to database services on the GenomeNet. Exp. Med., 13 (in Japanese). See also URL http://www.genome.ad.jp/dbget/dbget.links. html 3. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. (1990). Basic Local Alignment Search Tool. J. Mol. Biol. 215,403-410. 4. Etzold, T., and Argos, P. (1993) SRS - an indexing and retrieval tool for flat file data libraries. CABIOS 9, 49-57. 5. Pearson, W.R., and Lipman, D.J. (1988). Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. USA 85, 2444-2448. 6. Shah, M.B., Guan, X., Einstein, J.R., Matis, S., Xu, Y., Mural, R.J., and Uberbacher, E.C. (1994). User’s guide to GRAIL and GENEQUEST. URL. 7. http://avalon.epm.ornl.gov:80/manuals/grail-genequest.9407.html 8. Wall, L., and Schwartz, R.L. (1991). Programming perl. O’Reilly & Associates, Sebastapol, CA, USA.
Page 2 and 3:
BIOINFORMATICS: Databases and Syste
Page 4 and 5:
BIOINFORMATICS: Databases and Syste
Page 6 and 7:
To Tish, Emily, Miles and Josephine
Page 8 and 9:
TABLE OF CONTENTS INTRODUCTION ....
Page 10 and 11:
INTRODUCTION Stanley Letovsky Bioin
Page 12 and 13:
for wisdom. Where algorithms tend t
Page 14 and 15:
gain performance by distributing qu
Page 16 and 17:
systems described (OPM, BioKleisli,
Page 18 and 19:
DATABASES
Page 20 and 21:
1 NCBI: INTEGRATED DATA FOR MOLECUL
Page 22 and 23:
figure 1, and all flows into the ID
Page 24 and 25:
There are other sets of data that c
Page 26 and 27:
2. Explicit declaration by the hist
Page 28 and 29:
2. W. J. Wilbur, A retrieval system
Page 30 and 31:
2 HOVERGEN: COMPARATIVE ANALYSIS OF
Page 32 and 33:
Figure 1: Phylogenetic tree of the
Page 34 and 35:
fragment. In some cases, a same gen
Page 36 and 37:
Figure 2: Distribution of families
Page 38 and 39:
the user can visualize all the anno
Page 40 and 41:
Selecting orthologous genes with HO
Page 42 and 43:
Thus, HOVERGEN allows one to do exh
Page 44 and 45:
21. Saitou, N. Nei, M. The neighbor
Page 46 and 47:
3 WIT/WIT2: METABOLIC RECONSTRUCTIO
Page 48 and 49:
protein sequence are used (e.g., OR
Page 50 and 51:
After we have accumulated an initia
Page 52 and 53:
Coordinating the Development of Met
Page 54 and 55:
Availability of the Pathways, Softw
Page 56 and 57:
4 ECOCYC: THE RESOURCE AND THE LESS
Page 58 and 59:
epresented as an instance of the cl
Page 60 and 61:
The Gene--Reaction Schematic The ma
Page 62 and 63:
EcoCyc contains extensive informati
Page 64 and 65:
within compound windows uses a conc
Page 66 and 67:
Figure 4: The EcoCyc linear map bro
Page 68 and 69:
Figure 5: The software architecture
Page 70 and 71:
6. Karp. Frame representation and r
Page 72 and 73:
Introduction 5 KEGG: FROM GENES TO
Page 74 and 75:
Binary relations Figure 1 : Level o
Page 76 and 77:
hierarchical text. The genome map i
Page 78 and 79:
(2) Java applets to search, compare
Page 80 and 81:
can be used for gene function assig
Page 82 and 83:
influenzae lacks the upper portion
Page 84 and 85:
Future Directions Figure 4. KEGG as
Page 86 and 87:
Introduction 6 OMIM: ONLINE MENDELI
Page 88 and 89:
The Entry Each OMIM entry is assign
Page 90 and 91:
Searching OMIM is as simple as typi
Page 92 and 93:
How OMIM is Curated OMIM is maintai
Page 94 and 95:
7 GDB: INTEGRATING GENOMIC MAPS Int
Page 96 and 97:
in one of two ways: by being wholly
Page 98 and 99:
Figure 3: Universal Coordinate Disp
Page 100 and 101:
Figure 5: Dispersion of linear (lef
Page 102 and 103:
which minimizes dispersion. The opt
Page 104 and 105:
Figure 10: A piece of an "comprehen
Page 106 and 107:
alignments are better for the speci
Page 108 and 109:
Summary 8 HGMD: THE HUMAN GENE MUTA
Page 110 and 111:
Data coverage and structure 101 By
Page 112 and 113:
Conclusions and Outlook 103 Being b
Page 114 and 115:
Introduction 9 SENSELAB: MODELING H
Page 116 and 117:
107 For the purposes of the rest of
Page 118 and 119:
109 users might advocate creating a
Page 120 and 121:
111 The Associations table describe
Page 122 and 123:
Managing the Object Class Hierarchy
Page 124 and 125:
115 In fig. 2, the Objects and Clas
Page 126 and 127:
117 7. Mirsky JS, Nadkarni PM, Hine
Page 128 and 129:
10 THE MOUSE GENOME DATABASE AND TH
Page 130 and 131:
genetics community fostered the ear
Page 132 and 133:
123 Data acquisition for MGD includ
Page 134 and 135:
125 In February 1998, a ‘cDNA and
Page 136 and 137:
Acknowledgments 127 MGD is funded b
Page 138 and 139:
11 THE EDINBURGH MOUSE ATLAS: BASIC
Page 140 and 141:
expression database under developme
Page 142 and 143:
133 For blastocysts the shape of th
Page 144 and 145:
135 which is a cut at an arbitrary
Page 146 and 147:
137 This defines a viewing directio
Page 148 and 149:
139 plate spline [19] or polynomial
Page 150 and 151:
12 FLYBASE: GENOMIC AND POST- GENOM
Page 152 and 153:
143 Genes, including links via publ
Page 154 and 155:
Public Access to FlyBase Data The c
Page 156 and 157:
147 There are two classes of annota
Page 158 and 159:
149 screens to identify direct or i
Page 160 and 161:
13 MAIZEDB: THE MAIZE GENOME DATABA
Page 162 and 163:
The bins map strategy has been empl
Page 164 and 165:
Clicking on one of the orthologous
Page 166 and 167:
* hyper-text linked to MaizeDB enti
Page 168 and 169:
DISSEMINATION TO EXTERNAL DATABASES
Page 170 and 171:
Appendix 2: part of the Query Form
Page 172 and 173:
Introduction 14 AGIS: USING THE AGR
Page 174 and 175:
Horn Fly--Haematobia Screwworm--Coc
Page 176 and 177:
Figure 2 : Main AGIS Menu 167
Page 178 and 179:
Figure 4 : Available Classes and Su
Page 180 and 181:
Figure 6 : Query by Example and Que
Page 182 and 183:
Figure 8 : Hypertext ACEDB Object a
Page 184 and 185:
15 CGSC: THE E.COLI GENETIC STOCK C
Page 186 and 187:
177 described once for that gene an
Page 188 and 189:
FIGURE 1. A Query for an hsdR- recA
Page 190 and 191:
FIGURE 2. A Query for a lacZ-(amber
Page 192 and 193:
183 These are suggestions that dese
Page 194 and 195:
SYSTEMS
Page 196 and 197:
Introduction 16 OPM: OBJECT-PROTOCO
Page 198 and 199:
189 OPM is a data model whose objec
Page 200 and 201:
The OPM Database Development Tools
Page 202 and 203: ased on this query tree. Further qu
Page 204 and 205: 195 Interface, except that one can
Page 206 and 207: 197 Our strategy of providing suppo
Page 208 and 209: Acknowledgments 199 Between 1992 an
Page 210 and 211: Introduction 17 BIOKLEISLI:INTEGRAT
Page 212 and 213: 203 In the remainder of this chapte
Page 214 and 215: pages : “4267-4274", abstract:
Page 216 and 217: BioKleisli in Action: Querying Biom
Page 218 and 219: 209 savings in execution time is si
Page 220 and 221: References 211 13. NCBI ASN. 1 Spec
Page 222 and 223: Introduction 18 SRS: ANALYZING AND
Page 224 and 225: 215 In SRS, databank structures are
Page 226 and 227: 217 Operands also include named set
Page 228 and 229: Figure 1. The SRS Top Page. Figure
Page 230 and 231: Analyzing Data in SRS with Applicat
Page 232 and 233: 223 other databanks, and define spe
Page 234 and 235: Figure 7. A SWISS-PROT entry with i
Page 236 and 237: Figure 8. The results of a query fo
Page 238 and 239: parser and documentation files, and
Page 240 and 241: 23 1 12. Altschul, S.F., Gish, W.,
Page 242 and 243: Introduction 19 BIOLOGY WORKBENCH:
Page 244 and 245: Figure 1. Organization and Flow of
Page 246 and 247: 237 In order to make wrapper develo
Page 248 and 249: the browser window containing a men
Page 250 and 251: either during the current Workbench
Page 254 and 255: 20 EBI: CORBA AND THE EBI DATABASES
Page 256 and 257: 247 the Object Request Broker (ORB)
Page 258 and 259: 249 Every database entry is represe
Page 260 and 261: 251 Wrappers with and without State
Page 262 and 263: Discussion 25 3 CORBA and IDL minim
Page 264 and 265: Introduction 21 BIOWIDGETS:REUSABLE
Page 266 and 267: Visualization Solutions 257 Turnkey
Page 268 and 269: 259 The architecture also exploits
Page 270 and 271: possible because the bioWidget arch
Page 272 and 273: 263 4. Gish, Warren (1994-1997). un
Page 274 and 275: 22 ACEDB: THE ACE DATABASE MANAGER
Page 276 and 277: 267 On the other hand, most users c
Page 278 and 279: 269 Street, City and State into "ma
Page 280 and 281: 27 1 to a running database after ed
Page 282 and 283: 273 database the class Map refers t
Page 284 and 285: 275 were waiting for the Biowidget
Page 286 and 287: 277 table of 13 columns and 1 milli
Page 288 and 289: Introduction 23 LABBASE: DATA AND W
Page 290 and 291: 28 1 We will use as a running examp
Page 292 and 293: LabBase Details 283 A LabBase objec
Page 294 and 295: When the procedure completes, LabFl
Page 296 and 297: 287 sequence-assembly Worker in our
Page 298 and 299: 289 The Worker converts the object
Page 300 and 301: 291 Genome Research and are beginni
Page 302 and 303:
INDEX
Page 304 and 305:
INDEX A of Agricultural Genome Info
Page 306 and 307:
of vertebrate genes, 21-33 developm
Page 308 and 309:
GenBank database query software of,
Page 310 and 311:
Metabolic Pathway Database, 38 Meta
Page 312 and 313:
conversion to ‘gi’ identifiers
show all

Databases and Systems

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

Delete template?

Save as template?