View - ResearchGate
View - ResearchGate View - ResearchGate
Estimating Gene Function With LS-NMF 37the Euclidean distance. The inclusion of the gene and array specific standarddeviation (σ ij) improves the recovery of functional information (9).2. Materials1. The source code for LS-NMF including a graphical user interface for loadinginput files and visualizing output files can be downloaded from the Fox ChaseCancer Center Bioinformatics Group at http://bioinformatics.fccc.edu/software/OpenSource/LS-NMF/java/LS-NMF.shtml (see Note 1).2. The ClutrFree visualization and gene oncology analysis tool is available fromhttp://bioinformatics.fccc.edu/software/OpenSource/ClutrFree/clutrFree.shtml.3. The sample data set and associated gene ontology (GO) annotations can bedownloaded from http://bioinformatics.fccc.edu/papers/methodsLS-NMF/data_GO.zip.4. An updated version of the automated sequence annotation pipeline (ASAP II) is availableat http://bioinformatics.fccc.edu/software/OpenSource/ASAP/ASAP.shtml;however, it does require considerable systems administration skills to implement.Users might instead gather GO and other annotations for ClutrFree using differentsystems, such as OntoExpress (10).5. It is often useful to access the organism specific database for the particular dataset. Here GeneDB and the Schizosaccharomyces pombe database is used, http://www.genedb.org/genedb/pombe/.3. MethodsThe procedure for LS-NMF simulation on microarray data sets involvesthree steps:1. Preprocessing microarray data into proper format for LS-NMF analysis (seeNote 2).2. Setting parameters for LS-NMF simulation, and running the simulation with theset of the parameters.3. Interpreting the simulation results.In order to go through the whole procedure in detail, a sample microarray dataset that is a reduced version of the S. pombe cell cycle experiment is provided(11). Every step in the implementation is applied specifically on this sample dataset, so readers can follow the description below step-by-step. For different data,the steps are the same. It is recommended that, users new to bioinformatics toolsapply the process first to the sample data set to learn the procedures.3.1. Downloading Files and Preparing for AnalysisEach of the files noted in the Subheading 2. should be downloaded (with theexception of the ASAP system). This can be done with a typical web browseron any system. The files should be handled in the following manner.
- Page 46: 10 Bidautcomputing the maximum corr
- Page 50: 12 BidautFig. 3. The complete Clutr
- Page 54: Table 3Some Identified Patterns (5,
- Page 58: 16 BidautFig. 4. This is a comparis
- Page 62: 18 BidautReferences1. Hughes, T. R.
- Page 66: 20 Kirov et al.way to associate gen
- Page 70: 22 Kirov et al.based on a study ass
- Page 74: 24 Kirov et al.1. Retrieve the gene
- Page 78: 26Fig. 1. Functional associations f
- Page 82: 28 Kirov et al.Fig. 2. Pathway anal
- Page 86: 30 Kirov et al.3. Gene symbols usag
- Page 90: 32 Kirov et al.9. OBO_Team, Open Bi
- Page 94: 3Estimating Gene Function With Leas
- Page 100: 38 Wang and Ochs1. Download the LS-
- Page 104: 40 Wang and OchsFig. 1. The PattRun
- Page 108: 42 Wang and OchsFig. 3. The PattRun
- Page 112: 44 Wang and OchsFig. 4. The gene ta
- Page 116: 46 Wang and Ochsresults posttreatme
- Page 120: 4From Promoter Analysis to Transcri
- Page 124: Prediction Using PAINT 51even in si
- Page 128: Prediction Using PAINT 53Fig. 1. A
- Page 132: Prediction Using PAINT 55first exon
- Page 138: 58 Gonye et al.Fig. 3. A network vi
- Page 142: 60 Gonye et al.exGeneList.txt) is a
- Page 146: 62 Gonye et al.(http://www.tm4.org)
Estimating Gene Function With LS-NMF 37the Euclidean distance. The inclusion of the gene and array specific standarddeviation (σ ij) improves the recovery of functional information (9).2. Materials1. The source code for LS-NMF including a graphical user interface for loadinginput files and visualizing output files can be downloaded from the Fox ChaseCancer Center Bioinformatics Group at http://bioinformatics.fccc.edu/software/OpenSource/LS-NMF/java/LS-NMF.shtml (see Note 1).2. The ClutrFree visualization and gene oncology analysis tool is available fromhttp://bioinformatics.fccc.edu/software/OpenSource/ClutrFree/clutrFree.shtml.3. The sample data set and associated gene ontology (GO) annotations can bedownloaded from http://bioinformatics.fccc.edu/papers/methodsLS-NMF/data_GO.zip.4. An updated version of the automated sequence annotation pipeline (ASAP II) is availableat http://bioinformatics.fccc.edu/software/OpenSource/ASAP/ASAP.shtml;however, it does require considerable systems administration skills to implement.Users might instead gather GO and other annotations for ClutrFree using differentsystems, such as OntoExpress (10).5. It is often useful to access the organism specific database for the particular dataset. Here GeneDB and the Schizosaccharomyces pombe database is used, http://www.genedb.org/genedb/pombe/.3. MethodsThe procedure for LS-NMF simulation on microarray data sets involvesthree steps:1. Preprocessing microarray data into proper format for LS-NMF analysis (seeNote 2).2. Setting parameters for LS-NMF simulation, and running the simulation with theset of the parameters.3. Interpreting the simulation results.In order to go through the whole procedure in detail, a sample microarray dataset that is a reduced version of the S. pombe cell cycle experiment is provided(11). Every step in the implementation is applied specifically on this sample dataset, so readers can follow the description below step-by-step. For different data,the steps are the same. It is recommended that, users new to bioinformatics toolsapply the process first to the sample data set to learn the procedures.3.1. Downloading Files and Preparing for AnalysisEach of the files noted in the Subheading 2. should be downloaded (with theexception of the ASAP system). This can be done with a typical web browseron any system. The files should be handled in the following manner.