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VISTA Program to search for TFBSs Human and http://genome.lbl.gov/vista/ 57by combining TRANSFAC mouse rvista/about.shtmlPWMS and comparativesequence analysisFootPrinter Program to predict TFBSs Many species with http://genome.cs.mcgill.ca/ 59by phylogenetic information of cgi-bin/ FootPrinter3.0/footprinting method phylogenetic tree FootPrinterInput2.ploPOSSUM Program to analyze Human and http://www.cisreg.ca/oPOSSUM –overrepresented TFBSs mouse 2/opossum2.phpwithin a given set ofpromotersCisMols Program to identify cis- Human and http://cismols.cchmc. –regulatory modules in mouse org/peak-web/index.jspcoexpressed genepromotersConSite Program to predict TFBSs Human and http://mordor.cgb.ki.se/ 58using orthologous human mouse cgi-bin/CONSITE/consiteand mouse genomicsequences133
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Gene Function Analysis
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METHODS IN MOLECULAR BIOLOGYGene Fu
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PrefaceThis volume of Methods in Mo
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Prefaceixcolleagues demonstrate how
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xiiContentsPART III EXPERIMENTAL ME
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ICOMPUTATIONAL METHODS I
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4 BidautTable 1Input File Format Us
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6 BidautTable 2Folder Layout to Use
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8 Bidaut• alphaA: this is the num
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10 Bidautcomputing the maximum corr
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12 BidautFig. 3. The complete Clutr
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Table 3Some Identified Patterns (5,
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16 BidautFig. 4. This is a comparis
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18 BidautReferences1. Hughes, T. R.
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20 Kirov et al.way to associate gen
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22 Kirov et al.based on a study ass
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24 Kirov et al.1. Retrieve the gene
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26Fig. 1. Functional associations f
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28 Kirov et al.Fig. 2. Pathway anal
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30 Kirov et al.3. Gene symbols usag
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32 Kirov et al.9. OBO_Team, Open Bi
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3Estimating Gene Function With Leas
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Estimating Gene Function With LS-NM
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50 Gonye et al.activity and problem
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52 Gonye et al.Currently, PAINT can
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54 Gonye et al.dynamic nature of th
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Prediction Using PAINT 57represente
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Prediction Using PAINT 59In PAINT,
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Prediction Using PAINT 6114. On the
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Prediction Using PAINT 634.2. Size
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65Fig. 4. Localization of enrichmen
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Prediction Using PAINT 673. Okubo,
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5Prediction of Intrinsic Disorder a
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Prediction of ID and Its Use in Fun
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Table 1Summary of the Web Servers O
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IICOMPUTATIONAL METHODS II
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94 Crabtree et al.genomes, which is
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96 Crabtree et al.Fig. 2. Sybil pro
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98 Crabtree et al.Fig. 3. Computing
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100 Crabtree et al.3.1.5.1. FILTER
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102 Crabtree et al.3. For the sake
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104 Crabtree et al.Fig. 5. Best bid
Page 236: 106 Crabtree et al.17. Some cluster
Page 240: 108 Crabtree et al.19. Chado—The
Page 244: 110 Dateproducts prevents the under
Page 248: 112 DateDetails of these tasks are
Page 252: 114 DateThis step creates additiona
Page 256: 116 Date>hsapiens|gi|20093443 >hsap
Page 260: 118 DateBLAST score from the match
Page 264: Table 1A Sample of Results From Pro
Page 268: 122 DateFig. 1. A network of functi
Page 272: 124 Datedescribed by Verjovsky Marc
Page 276: 126 Dateor contracts put forth by t
Page 280: 8Bioinformatics Tools for Modeling
Page 284: Modeling Transcription Factor Targe
Page 290: 134 DavuluriPWM-based models do not
Page 294: 136 DavuluriTF-map alignments of or
Page 298: 138 Davuluridiscussed which program
Page 302: 140 DavuluriTable 2ER-a-Responsive
Page 306: Table 3Sample Data Matrix Represent
Page 310: Table 3 (Continued)Class MYCMAX MYC
Page 314: 146 DavuluriFig. 3. (A) CART Tree:
Page 318: 148 Davuluri11. Vlieghe, D., Sandel
Page 322: 150 Davuluri44. Berezikov, E., Gury
Page 326: 9Mining Biomedical Data Using MetaM
Page 330: Mining Biomedical Data Using MMTx a
Page 334: Mining Biomedical Data Using MMTx a
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Mining Biomedical Data Using MMTx a
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172 Ho et al.Fig. 1. Artificial exa
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174 Ho et al.allowing for cases whe
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176 Ho et al.A different measure is
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178 Ho et al.3.1.3. LA and Generali
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180 Ho et al.The ECF-statistic can
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182 Ho et al.In the special case of
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184 Ho et al.Fig. 5. An illustratio
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186 Ho et al.Fig. 7. The power curv
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188 Ho et al.this section were not
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190 Ho et al.References1. Schena, M
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IIIEXPERIMENTAL METHODS
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194 Caldwell et al.for sequences th
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196 Caldwell et al.query because it
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198 Caldwell et al.Fig. 1. (A) Prot
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200 Caldwell et al.outside primer o
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202 Caldwell et al.5. Targeting scr
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204 Caldwell et al.will allow the s
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206 Caldwell et al.3.1.6. Plasmid P
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208 Caldwell et al.PCR amplify the
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210 Caldwell et al.8. Thawing cells
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212 Zhang et al.Going one step beyo
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214 Zhang et al.Fig. 2. Generation
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216 Zhang et al.Perform PCR cycles,
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218 Zhang et al.Fig. 4. Schematic m
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220 Zhang et al.Fig. 5. Replacement
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13Construction of Simple and Effici
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DNA Vector-Based shRNA-Expression S
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244 Hust et al.overcome by two appr
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246 Hust et al.Fig. 1. Schematic de
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248 Hust et al.interaction during p
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250 Hust et al.3.4. Titering1. Inoc
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252 Hust et al.10. Shortly before u
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254 Hust et al.activity by preservi
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15A Bacterial/Yeast Merged Two-Hybr
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Screening in Yeast With a Bacterial
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16A Bacterial/Yeast Merged Two-Hybr
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Dual Bait-Compatible Bacterial Two-
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318 Thibodeau-Beganny and Joungbeen
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320 Thibodeau-Beganny and JoungFig.
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324 Thibodeau-Beganny and JoungTypi
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328 Thibodeau-Beganny and JoungPCR
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330 Thibodeau-Beganny and Joung16-1
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332 Thibodeau-Beganny and Joung2. P
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334 Thibodeau-Beganny and Joung11.
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336 IndexKknockin (gene knockin) 19
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