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112 DateDetails of these tasks are described in Methods. These programs have to becoded by the user.3. MethodsBoth the computational methods described in this chapter have commoninitial requirements (described in Subheading 3.1.), such as the need for adatabase of reference genomes, and parsed results of BLAST searches againstthe database. Steps specific to the individual methods are described inSubheading 3.2.3.1. Common Initial Steps for the Phylogenetic Profiling Methodand the Rosetta Stone Method3.1.1. Creating a Database of Reference GenomesBoth methods require a database of genomes against which the querysequences are compared for similarity. It is important to note that this databaseshould contain genomes that are fully sequenced, as opposed to, say, creating adatabase similar to the BLAST nonredundant database, which is essentially arepository of all known protein sequences. Use of a database that contains proteinswithout regarding genome sequence status will generate incorrect profilesand create complications when applying statistical tests of confidence toRosetta stone linkages.Complete protein complements of fully sequenced genomes can be downloadedfrom NCBI (ftp://ftp.ncbi.nih.gov) or from websites of individualgenome sequencing centers (see Note 3). Users should ensure that all aminoacid sequences included in the database possess unique identifiers (see Note 4).The more genomes included in the sequence file, the better the methods willperform. Amino acid sequences of all proteins from the downloaded genomesare concatenated into a single file for ease of use and more accurate calculationof BLAST expectation (E) values. This file can also include proteins encoded bybacterial plasmids, if so desired.For illustration purposes, this database will be referred to as “myDatabaseFile,”populated with the following sequences:>ssolfataricus|gi|15896972MIVPVKNEERVLPRLLDRLVNLEYDKSKYEIIVVEDGSTDRTFQICKEYEIKYN NLIRCYSLPR>ecoli_K12|gi|16127996MRVLKFGGTSVANAERFLRVADILESNARQGQVATVLSAPAKITNHLVAMIEKTISGQDALPNI>hsapiens|gi|20093441
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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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Page 208: IICOMPUTATIONAL METHODS II
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Page 216: 96 Crabtree et al.Fig. 2. Sybil pro
Page 220: 98 Crabtree et al.Fig. 3. Computing
Page 224: 100 Crabtree et al.3.1.5.1. FILTER
Page 228: 102 Crabtree et al.3. For the sake
Page 232: 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
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Page 282: 130 Davuluriinteracting proteins an
Page 286: Table 1Web URLs of Promoter, TF Dat
Page 290: 134 DavuluriPWM-based models do not
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138 Davuluridiscussed which program
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140 DavuluriTable 2ER-a-Responsive
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Table 3Sample Data Matrix Represent
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Table 3 (Continued)Class MYCMAX MYC
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146 DavuluriFig. 3. (A) CART Tree:
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148 Davuluri11. Vlieghe, D., Sandel
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150 Davuluri44. Berezikov, E., Gury
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9Mining Biomedical Data Using MetaM
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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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