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Prediction of ID and Its Use in Functional Proteomics 772. Materials1. The Swiss-Prot database is described in ref. 98 and is available fromhttp://www.expasy.org/sprot/.2. The database of experimentally characterized disordered proteins, DisProt, is availablefrom http://www.disprot.org. The original version of this database is describedin ref. 50.3. PONDRVLXT predictor is described in ref. 51 and is availal from http://www.pondr.com/.4. PONDR VL3-BA is described in ref. 65 and is available from http://www.pondr.com/.5. PONDR VSL is described in refs. 78 and 79 and is available from http://www.pondr.com/.6. CH-plot predictor is available from http://www.pondr.com/. The basic algorithmof this binary classifier is described in ref. 25.7. CDF analysis is available from http://www.pondr.com/. This predictor is describedin refs. 57 and 58.8. α-MoRF predictor is described in ref. 82 and is available from http://www.pondr.com/.9. DisPhos predictor also known as DEPP is described in ref. 83 and is availablefrom http://www.pondr.com/.3. MethodsThe methods outlined next describe the analysis of amino acid sequencesusing the intrinsic disorder knowledge to gain structural and functional informationrelated to a protein, a protein family, or an entire proteome/database.Although numerous predictors of intrinsic disorder are currently available asweb servers (see DisProt website, http://www.disprot.org, for a complete list ofsuch servers), focus will be on utilization of PONDR tools, as they cover a widerange of potential applications of ID concept for structural and functionalanalysis of proteins. Obviously, this analysis could have been carried out withother ID predictors described earlier.3.1. Analysis of Protein Amino Acid CompositionIt has been already pointed out that a specific feature of a probable ID regionis the amino acid compositional bias characterized by a low content of so-calledorder-promoting residues such as C, V, L, I, M, F, Y, and W and a high contentof so-called disorder-promoting residues, including Q, S, P, E, K, G, and A(10,51,60). Therefore, the analysis of the amino acid composition biases canprovide useful information related to the nature of a given protein. The fractionaldifference in amino acid composition between a given protein (or a givenprotein data set) and the set of reference globular proteins is based on therecently elaborated approach (10) and provides a perfect visualization tool for
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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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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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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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