Geners Sub Download
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Click on the \"browse gene sets\" links in the table below to view the gene sets in a collection. Or download the gene sets in a collection by clicking on the links below the \"Download Files\" headings. For a description of the GMT file format see the Data Formats in the Documentation section. The gene sets can be downloaded as NCBI (Entrez) Gene Identifiers or HUGO (HGNC) Gene Symbols. There are also JSON bundles containing the HUGO (HGNC) Gene Symbols along with some useful metadata. An XML file containing all the Human MSigDB gene sets is available as well.
We developed a new method to measure the gene set enrichment. Applications to two simulated datasets and two real datasets show that this method is sensitive to the associations between gene sets and phenotype. The program Sub-GSE can be downloaded from -rcf.usc.edu/fsun.
The speed of Sub-GSE is determined by the number of gene sets and the number of genes inside each gene set. To give an example of the running time, we download the gene expression data with accession number GSE5081 from NCBI which hybridized total RNAs from gastric biopsy specimens of patients with Helicobacter pylori positive (HP+) and Helicobacter pylori negative (HP-) antrum erosions (ER+), and the corresponding, adjacent normal mucosae (ER-). The gene expression data includes 54675 probes and 32 samples. HP+ and HP- are treated as the phenotype. Mappings between the probes and GO categories are from the R package -project.org/ named \"hgu133plus2\". All the probes are mapped to 8310 GO categories in total. We run Sub-GSE on this data set using a computer with Pentium 4 CPU 3.60 GHz/3.59 GHz, 1.00 GB of RAM. It took 12.7 hours when 1000 permutations are required and the strict set size threshold is 1.
Click on the \"browse gene sets\" links in the table below to view the gene sets in a collection. Or download the gene sets in a collection by clicking on the links below the \"Download Files\" headings. For a description of the GMT file format see the Data Formats in the Documentation section. The gene sets can be downloaded as NCBI (Entrez) Gene Identifiers or MGI Gene Symbols. There are also JSON bundles containing the MGI Gene Symbols along with some useful metadata. An XML file containing all the Mouse MSigDB gene sets is available as well.
To further annotate the identified genes, we integrated additional annotations from the Entrez Gene database, including Gene ID, official symbol, gene aliases, chromosome location, functional description, gene ontology (GO), and related pathways. The general information and homologous sequences are crosslinked to the NCBI Entrez and HomoloGene databases [7]. The mRNA expression profile data from both normal and tumor tissues were imported from BioGPS [8]. To obtain comprehensive pathway-related information, we annotated the genes using BioCyc [9] and the KEGG collection of databases [10]. The other useful regulatory information included post-translational modification [11], methylation [12], and protein-protein interactions [13]. All of the included functional and/or genomic features were seamlessly integrated to produce a downloadable output available in a plain text format.
To further explore the connection of CIGs to other cancer genes, we separately mapped 96 and 81 CIGs with and without oncogenic and tumor suppressive roles, respectively. To this end, we downloaded a non-redundant human interactome from the PathCommons database [13], containing 3629 proteins and 36,034 protein-protein interactions. It is of note that we only used those protein-protein interactions collected from pathway databases (HumanCyc, Reactome, and KEGG pathway) [10, 17], which have clear biological significance, rather than physical interactions without experimental validations. By using a sub-network extraction pipeline implemented in our previous study [18], we build a sub-network to link the CIGs with other human genes based on those pathway-based interactions. Briefly, the CIGs were mapped into the prepared pathway-based interactome and the sub-network was extracted according to the shortest paths between those input CIGs and other genes. By calculating the topological properties of the sub-network using the Network Analyzer plugin in Cytoscape 3.4 [19], we were able to explore the potential global network properties of cancer initiation [20]. Here, the node degree distribution was used to characterize the total number of connections for each gene in the network [20], and the shortest path was calculated using the shortest length from one node to another [20]. All network visualization was drawn using Cytoscape 3.4. The node degree was used to depict the node size in the network chart. Also, different colors were used to differentiate those CIGs from our input and the other linker genes bridging those CIGs. The mutational analyses were all conducted using the cancer genomics portal cBio [21].
The complete list of human-coding genes was downloaded from Ensembl [30] Biomart on March 2014 using version Ensembl Genes 75 with genome version GRCh37.p13. Protein-coding genes without HGNC gene symbol, a proper translation start and translation end annotation according to this genome version were discarded. Genes without a valid dN/dS ratio were removed (i.e. without any observed synonymous polymorphisms according to dbSNPv138 and EVS). This last step was done for two reasons: 1) to ensure there is no bias when evaluating dN/dS ratio in our results, 2) to ensure the genes selected in this study have been covered in NGS studies, since any gene without at least one observed synonymous mutation is presumably not sufficiently captured in either exome or whole-genome studies. The Background set overlaps FLAGS completely.
The raw data of RNA-seq was downloaded from the NCBI Sequence Read Archive (SRA: PRJNA248163). Tophat and cufflinks were used to analyze the RNA-seq expression, and the gene expressions were uniformed in fragments per kilobase million (FPKM) [40]. The expression of WOXs was extracted from the total expression data. Heatmap was drawn by Genesis software [41].
Because gene expression is associated with the biological function, we inspected the expression patterns of different WOX genes. RNA-seq data were downloaded from NCBI and analyzed. As shown in Fig. 5a, we found that WOXs were widely expressed in the vegetative (root, stem, and leaf) and reproductive (torus, petal, stamen, pistil, calycle, and -3, -1, 0, 1, 3, 5, 10, 20, 25 and 35 days post-anthesis (DPA) ovule) tissues as well as in the fiber (5, 10, 20, and 25 DPA), indicating that WOXs have diverse biological functions and work in different tissues. We found that some WOXs did not express in the vegetative tissues and had a very low expression levels in the reproductive tissues. For instance, we could not detect GhWOX2_At/Dt, GhWOX3a_At/Dt, and GhWOX3b_At/Dt expression in root, stem, and leaf, and very low expression levels were detected in some of the reproductive tissues. On comparing the expression patterns of the orthologs between At and Dt, we found that the expression patterns and the levels of expression of the two were not always the same; for example, GhWOX8_Dt was expressed in root and leaf but GhWOX8_At was not. GhWOX8_Dt had higher expression levels in 20, 25, and 35 DPA ovule and 10 DPA fiber compared to that in GhWOX8_At. GhWOX13a_At/Dt and GhWOX13b_At/Dt were not only close to each other in the phylogenetic tree (Fig. 2 and Fig. 4a), but also had similar expression patterns (Fig. 5), suggesting that they have a similar biological function.
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You can download files programmatically. Click the purple 'Scripted download' button next to each file for information on how to retrieve that file via the command line or a script. All files for the current and past 3 versions of COSMIC are available for download. Check out our help pages for more information on downloading, and for an explanation of how to find a manifest for all available files.
The manual can be downloaded here. An additional brief overview of conditional, joint and interaction modelling can be found here. Note: the SNP-wise Mean model has been updated in version 1.08 of MAGMA, changing the test statistic used and the way the corresponding p-value is computed. Details on this change are outlined here.
The MAGMA source code can also be downloaded below, which can be used to compile the program on the target system if this is not supported by the provided binaries (note that standard copyright applies; the MAGMA binaries and source code may not be distributed or modified).
CytoHubba provide a user-friendly interface to explore important nodes in biological networks. It computes all eleven methods in one stop shopping way. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010.
The studies of protein-protein interactions will be more powerful when the interactome coverage increases. However, the complexity of the network will also increase, that always hampers computation tasks. After the optimization on the programs, cytoHubba is able to complete all eleven analysis of a small network (e.g. 330 nodes, 360 edges), a middle size one (7,600 nodes, 20,000 edges) and a large set (11,500 nodes, 33,600 edges) in few seconds, around 30 seconds and few minutes, respectively, on a common desktop/ notebook (Cytoscape version 2.6.x / 2.7.x / 2.8.x on Window 7/8 platform; hardware spec as Intel i7, 8 GB of RAM). CytoHubba has been updated several times since 2009 (from v1.0 to v1.6). It is freely accessible in Cytoscape App store ( ). The accumulated downloading number is around 6,500 ( _web/plugins/plugindownloadstatistics.php, statistics on May 2014). And it is used widely to analyze cancer metabolic network[6], innate immune network[7], complex biofilm communities[8] and so on. 153554b96e
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