Here, by discussing the disease risk of obesity, we demonstrated another application of DOSim (using functions of calculating similarity between genes and DO-directed gene modules detection and annotation). Previous studies showed that obesity increased the risk of various diseases, such as type 2 diabetes, heart disease and certain types of cancer . In this example, we used obesity related genes (651 genes) that were downloaded from the Phenopedia database. Of the 651 genes, 361 had DO annotations. The similarities between these 361 genes were calculated using the BMA method on the Resnik measure (This is just one example. Users can choose to use any of the others in their applications). A gene similarity matrix S = [s ij ]361 361 was constructed where s ij is the similarity between i th gene and j th gene in the gene set. After that an average linkage hierarchical clustering was performed and then a dynamic tree cutting method was applied (minimal module size is larger than 10) . Finally, 10 different gene modules were obtained (Figure 5, Table 1).
*Precaution When Using a USB ConnectionDisconnect the USB cable that connects the device and computer before installing the driver.Connect the USB cable after installing the driver.Driver and application software files have been compressed.The following instructions show you how to download the compressed files and decompress them.1. To download files, click the file link, select [Save], and specify the directory where you want to save the file.The download will start automatically. 2. Downloaded files are saved in the specified folder in a self-extracting format (.exe format).3. Double-click the files to decompress them. A new folder will be created in the same folder. The new folder will have the same name as the compressed file.4. Double-click the decompressed Setup.exe file to start installation.
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All data underlying Figs. 1, 2, 3, 5, and 6 are reproducible with the Python v2.7 script , which was run using PyPy v2.5.0. These figures as well as Fig. 4 were generated with the Mathematica v10.3.1 notebook; see _figures.nb. SEQC/MAQC-III consortium junction data were downloaded from -S4.zip. BioSample submission dates for 77 SRA runs (0.3% of the samples we studied) were not found on the server, so these runs were excluded from the analyses involving junction discovery dates presented in Figs. 5 and 6.
All 15 mouse TRAP-seq samples were taken from the study SRP031883; individual run accession numbers are provided in the Rail-RNA manifest file These samples were aligned to mm10 on a local computer cluster with Rail-RNA v0.2.3b, and junction output available at _translatome_junctions.tsv.gz may be recovered with the script Junctions were subsequently lifted over to hg19 with the UCSC liftOver utility  using the command-line parameters -ends 2 -minmatch=1.0; that is, we lifted over only the two-base motifs on either end of each intron and required that all four motif bases had mappings in the liftover. The script calls the liftOver utility and writes lifted-over junctions and their presence in human annotation. Lifted-over junctions may be downloaded at _mm10_to_hg19_junctions.tsv.gz, where the format of this file is described in translatome.py. Statistics on the presence of lifted-over junctions in human SRA samples reported in the main text were computed by _final_translatome_stats.sh. 59ce067264