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Sybil: Multiple Genome Comparison and Visualization 105Fig. 7. Using a MST algorithm to remove redundant matches. Protein cluster imagebefore (left) and after (right) applying the MST filter.polypeptides P1 and P2 such that P1 has 100 amino acids and P2 has only 50,but the two match each other perfectly—over the region of the match—andtherefore the cluster is assigned a percent identity score of 100% and a percentcoverage score of 100%. This is not a completely satisfactory result, so in orderto distinguish this case from one in which the polypeptides in a cluster matchperfectly and are of the same length, one frequently calculates and stores a thirdquantity, namely the ratio of the length of the shortest polypeptide in the clusterto the length of the longest polypeptide in the cluster.14. The default sequence “neighborhood” size is configurable and may also bechanged by clicking on the links (“5 kb,” “10 kb,” and so on) that appear abovethe graphical display on the protein cluster report page (see Fig. 2). The amountof additional sequence to display is calculated by taking the extent of the longestgene in the cluster and then adding the specified neighborhood size (e.g., 5 kb) oneither side of it. For the other (shorter) genes in the cluster slightly more sequencemust be displayed on either side in order to make all of the sequences line up atthe left and right edges of the display (assuming that the ends of the contigs arenot reached before the edge of the display).15. The features are retrieved from the chado comparative database using a standardStructured Query Language (SQL) see http://en.wikipedia.org/wiki/SQL range queryon the chado featureloc.fmin and featureloc.fmax columns. This has producedacceptable performance, but in future one may adopt a binning scheme for fasterretrieval of sequence features within a given range, as is done in GBrowse seehttp://www.bioperl.org/wiki/Lincoln_Stein and http://www.bioperl.org/wiki/Gbrowse(23) and the University of California, Santa Cruz (UCSC) Genome Browser (UCSCGenome Bioinformatics Group) (24).16. The Bioperl features (instances of Bio::SeqFeatureI) that are created are “skeleton”features that contain the coordinates and unique identifiers of the featuresread from the database. A mapping is stored that allows each Bioperl feature to bemapped back to the data that were read from the database.
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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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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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