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68 Gonye et al.18. Dozmorov, M. G., Kyker, K. D., Saban, R., et al. (2006) Analysis of the interactionof extracellular matrix and phenotype of bladder cancer cells. BMC Cancer 6, 12.19. Keller, M. A., Addya, S., Vadigepalli, R., Banini, B., et al. (2006) Transcriptionalregulatory network analysis of developing human erythroid progenitors reveals patternsof co-regulation and potential transcriptional regulators. Physiol. Genomics28, 114–128.20. Hubbard, T., Barker, D., Birney, E., et al. (2002) The Ensembl genome databaseproject. Nucleic Acids Res. 30, 38–41.21. Kawai, J., Shinagawa, A., Shibata, K., et al. (2001) Functional annotation of fulllengthmouse cDNA collection. Nature 409, 685–690.22. Aerts, S., Thijs, G., Coessens, B., Staes, M., Moreau, Y., and Moor, B. D. (2003)Toucan: deciphering the cis-regulatory logic of coregulated genes. Nucleic AcidsRes. 6, 1753–1764.23. Matys, V., Frickle, E., Geffers, R., et al. (2003) TRANSFAC®: transcriptionalregulation, from patterns to profiles. Nucleic Acids Res. 31, 374–378.24. Quandt, K., Frech, K., Karas, H., Wingender, E., and Werner, T. (1995) MatInd andMatInspector—New fast and versatile tools for detection of consensus matches innucleotide sequence data. Nucleic Acids Res. 23, 4878. A web-based version of thetool is available at http://www.genomatix.de. (last accessed May 5th, 2007).25. Bailey, T. L. and Elkan, C. (1994) Fitting a mixture model by expectation maximizationto discover motifs in biopolymers. Proc. Second Int. Conf. IntelligentSyst. Mol. Biol. 28–36, AAAI Press, Menlo Park, California. http://meme.sdsc.edu(last accessed May 5th, 2007).26. Bury, K. (1999) Statistical Distributions in Engineering. Cambridge UniversityPress, Cambridge, UK.27. Jakt, L. M., Cao, L., Cheah, K. S., and Smith, D. K. (2001) Assessing clusters andmotifs from gene expression data. Genome Res. 11(1), 112–123.28. Elkon, R., Linhart, C., Sharan, R., Shamir, R., and Shiloh, Y. (2003) Genome-Wide in silico identification of transcriptional regulators controlling the cell cyclein human cells. Genome Res. 13, 773–780.29. Benjamini,Y. and Hochberg,Y. (1995) Controlling the False Discovery Rate: a practicaland powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300.
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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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Estimating Gene Function With LS-NM
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Page 126: 52 Gonye et al.Currently, PAINT can
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Page 140: Prediction Using PAINT 59In PAINT,
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Page 162: 70 Uversky et al.in protein functio
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Page 170: Table 1 (Continued)Server name URL
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Page 178: 78 Uversky et al.elucidating compos
Page 182: Table 2Averaged Frequencies of Diff
Page 186: 82 Uversky et al.the box Raw Output
Page 190: 84 Uversky et al.peculiarities of t
Page 194: 86 Uversky et al.Fig. 4. Prediction
Page 198: 88 Uversky et al.13. Uversky, V. N.
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6Sybil: Methods and Software for Mu
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Sybil: Multiple Genome Comparison a
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7Estimating Protein Function Using
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Estimating Protein Function Using P
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130 Davuluriinteracting proteins an
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Table 1Web URLs of Promoter, TF Dat
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134 DavuluriPWM-based models do not
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136 DavuluriTF-map alignments of or
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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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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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