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116 Date>hsapiens|gi|20093443 >hsapiens|gi|14556780raw_score: 220 | E-value: 1e-138 | query_start: 1 |query_end: 105 | subject_start: 15 | subject_end:155 | match_length: 105 | identity_percentage: 78 |similarity_percentage: 91 | query_length: 140 | subject_length:244>hsapiens|gi|20093443 >celegans|gi|85444128raw_score: 132 | E-value: 1e-66 | query_start: 22 |query_end: 80 | subject_start: 107 | subject_end:165 | match_length: 58 | identity_percentage: 70 |similarity_percentage: 88 | query_length: 140 | subject_length:111In this illustration, each line represents a BLAST hit to the query in thedatabase of reference genomes. The output is divided into three columns: thefirst column is the identifier for the query, the second is the identifier for thehit (the subject) in the database, and the third describes details of the match,such as E-values and start–stop coordinates. Herein, besides the requiredattributes, raw scores, sequence identities and similarities, and subjectsequence length are also captured. Users are free to experiment with the variousstand-alone parser programs available for free through the Internet, orwrite their own (see Note 2). One advantage of writing a custom parser programis that it proves helpful in getting acquainted with the raw BLASTresults.3.2. Steps Specific to Individual Methods3.2.1. The Phylogenetic Profiling MethodPhylogenetic profiles for each of the query input sequences can be createdusing the parsed BLAST results. In this protocol, transformed BLAST E-valueswill be used to construct the profile vector, rather than representing presence orabsence of the query in a genome using simple binary values of 0 and 1. Thisuse of BLAST E-values in generating profiles results in profile vectors with ahigher resolution, wherein the similarity or the distance between the vectors canbe measured more accurately.3.2.1.1. GENERATING PHYLOGENETIC PROFILES FROM BLAST DATAGenerating profiles involves checking BLAST results for informationabout best matches to the query sequence, from each genome included in thedatabase. The E-value of this best match is retained, transformed, and used inprofile construction. One method of transforming E-values, as described byPellegrini and coworkers (2), uses the following formulation:
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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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Page 208: IICOMPUTATIONAL METHODS II
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Page 258: Estimating Protein Function Using P
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Page 290: 134 DavuluriPWM-based models do not
Page 294: 136 DavuluriTF-map alignments of or
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Page 302: 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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