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8 Bidaut• alphaA: this is the number of atoms initially present in the atomic domain correspondingto the A matrix. BDrun estimates the default number of atomsbeing proportional to the data set size and the number of patterns, so thisdefault value will be left as it is.• alphaF: this is the initial number of atoms initially present in the atomicdomain corresponding to the P matrix. BDrun estimates the default number ofatoms being proportional to the data set size and the number of patterns, so thisdefault value will be left as it is.• Nsol: this parameter fixes the number of basis vectors (patterns) to be inferredby BD. The analysis will be started by setting it initially at three and increaseit to 20 during subsequent runs.• Na: number of genes (rows) in the data set.• Nb: number of conditions (columns) in the data set.• data: the data loaded.• uncertFlag: this flag is set to 1 when an uncertainty file has been found and loaded• acc: this is the uncertainty data corresponding to each data value.• additive: when uncertFlag is set to zero, additive and multiplicative values areused to model noise and calculate uncertainty. This is not used here, so leavethis to the default value.• Multiplicative, see additive.• geneName: the gene IDs found in the data set.• xLabels: the condition IDs found in the data set.• Server and Port: this is used to run BD on a remote machine (see Note 1). Thisis left to the default values of “local” and “1099.”• Library: this allows one to choose among several BD variants. This is set to“BDCI.”2. Once the parameters have been set, BD is run by clicking on the [run] button. If allparameters have been set up correctly, BDrun should display the following messages:running BCDI on localusing uncertainty from fileRunning Bayesian Decomposition Version 1.The Markov Chain random walk is expensive, and one should expect a runningtime of about 6 h for five patterns/3000 iterations up to 20 h for 20 patterns/3000iterations on a 3Ghz Pentium4 Linux machine. It is possible to accelerate thisprocess by running multiple instances of BD on remote machines or on a cluster(see Note 1). To verify the proper progression of the algorithm, the messagesspecifically can be monitored to the two stages of annealing and sampling in theBDrun message area:3. During the annealing period, BDrun will display the following messages:Running Bayesian Decomposition1/T = 0.1 of 1.0 942 (179) 134654.68451/T = 0.1 of 1.0 911 (162) 134515.2276…
Page 2: Gene Function Analysis
Page 6: METHODS IN MOLECULAR BIOLOGYGene Fu
Page 12: PrefaceThis volume of Methods in Mo
Page 16: Prefaceixcolleagues demonstrate how
Page 20: xiiContentsPART III EXPERIMENTAL ME
Page 26: ICOMPUTATIONAL METHODS I
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Association Analysis for Large-Scal
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36 Wang and Ochsfunction (9). Herei
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38 Wang and Ochs1. Download the LS-
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40 Wang and OchsFig. 1. The PattRun
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42 Wang and OchsFig. 3. The PattRun
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44 Wang and OchsFig. 4. The gene ta
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46 Wang and Ochsresults posttreatme
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4From Promoter Analysis to Transcri
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Prediction Using PAINT 51even in si
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Prediction Using PAINT 53Fig. 1. A
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Prediction Using PAINT 55first exon
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58 Gonye et al.Fig. 3. A network vi
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60 Gonye et al.exGeneList.txt) is a
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62 Gonye et al.(http://www.tm4.org)
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64 Gonye et al.does not span the en
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66 Gonye et al.4.7. Interpreting th
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68 Gonye et al.18. Dozmorov, M. G.,
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70 Uversky et al.in protein functio
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72 Uversky et al.sequence space and
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Table 1 (Continued)Server name URL
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76 Uversky et al.1.5. When to Use t
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78 Uversky et al.elucidating compos
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Table 2Averaged Frequencies of Diff
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82 Uversky et al.the box Raw Output
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84 Uversky et al.peculiarities of t
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86 Uversky et al.Fig. 4. Prediction
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88 Uversky et al.13. Uversky, V. N.
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90 Uversky et al.50. Vucetic, S., O
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92 Uversky et al.84. Ritter, L. M.,
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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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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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