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Gene Function Inference in Deletion Mutants 94. During the sampling period, BDrun will display the following messages:Sample = 100 of 3000 1573(405) 107000.3768Sample = 200 of 3000 1548 (398) 106874.0760…5. Once the decomposition is over, BDrun displays the following message:Random seed was 1141931207 = 106819.8192 = 1582.8897< F Atoms > = 405.1083log[e]Prob(Data) = 75983.9831Wrote: /home/ghbidaut/data/Rosetta_Dataset/Fr764_228.bdo.6. Two files have been generated by BDrun: Fr764_228.bdo and Fr764_228.gnm.The .bdo file contains the two matrices A and P along with their correspondinguncertainties. The .gnm file contains additional information that is not used here(a list of gene names used by the BDviewer program).7. It is then needed to create an appropriate sub directory to store the analysis result. Herethe layout is followed as detailed previously, and will name it “exp_05.” On MacOSX, the subdirectory can be created by menus, or by the following command line:$ mkdir exp_05.8. The analysis files have to be moved in sub directory created in the previous step.Drag and Drop is possible under MacOS X, or the following command line can beissued from a terminal in Linux or MacOS X.$ mv Fr764_228.bdo Fr764_228.gnm exp_5.9. Back to BDrun, the analysis can restart for six patterns this time, and repeat thesame procedure over again, by changing the “Number of Patterns” value from five tosix and click on the [run] button again. Once the decomposition is finished, thefiles Fr764_228.bdo and Fr764_228.gnm has to be moved to the 6_pat directory.This procedure is repeated for up to 20 patterns to obtain all the necessary data.3.3. Data Visualization and AnalysisClutrFree is a tool whose goal is to aid in pattern interpretation and visualizationthrough advanced visualization techniques and elaborated tree graphs(7). It features an algorithm for comparison of several experiments generated bythe variation of one or several parameters in a given clustering method. In thecontext of this study, the variable parameter is the number of patterns n inferredby BD, to allow estimation of the data dimensionality. Even though ClutrFreecan be used with any clustering algorithm, it is well suited to the exploration ofthe A and P matrices derived by BD. The pattern window is related to the Pmatrix and the gene window is related to the A matrix. The tree display permitsthe navigation across multiple clustering experiments and the visualization ofpatterns, stable and unstable, across the experiments. It is constructed by
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
Page 34: 4 BidautTable 1Input File Format Us
Page 38: 6 BidautTable 2Folder Layout to Use
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Page 64: 2Association Analysis for Large-Sca
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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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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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