Metadata-Version: 1.0
Name: GoTermAnalysis
Version: 0.1.5
Summary: Given lists of genes, find its associated gene ontology term enrichment and merge them up
Home-page: http://pypi.python.org/pypi/GoTermAnalysis/
Author: Fan Yu
Author-email: fay19@pitt.edu
License: UNKNOWN
Description: This package is for gene ontology analysis. It has 2 main functions: 1. It receives a gene list, and give back the enrichment and merge result. 2. It can update to the newest gene ontology database.
        
        1. ############################Analysis############################
        How to do gene ontology term analysis?
        
        (1). ######enrichment######
        create an instance for enrichment class, then call the function:
        
        1. inputfile is genelists in a csv file: every row is a list, the first column is drivers of this gene list.  
        2. outputfile_path is the directory to store the enrichment result. The number of outputfiles is same with the numbers of genelists in input file. Each output file is named by the driver of each genelist.
        3. threshold is the significant p-value threshold
        2. top is an optional parameter for picking up the top number of enrichment result (e.g. top 5 or top 10), by default is none. 
        
        Example of how to use this class:
        
        tool = enrichment.Enrichment("localhost", "fanyu", "hellowork", "assocdb", "MLL2-MLL3.targetgenes.v9.csv", "", 0.01)
        tool.enrich_csv()
        
        (2) ######merge######
        Create an instance of GoGraph class:
        
        #Data of Go Ontology structure and gene_Goterm association
        weightGographData = "weightedGoGraph.xml"
        genelist = GeneList_csv_directory
        output = output_directory
        p_value = 0.05
        subGenelistNo = 3
        
        #Create a GoGraph object (Note: every time you use the gotermSummarization(), you need to create a new object)
        gograph = merge.GoGraph("weightedGraph.xml", "MLL2-MLL3.targetgenes.v9.csv", "", 0.01, 3, "localhost", "fanyu", "hellowork", "assocdb")
        gograph.gotermSummarization()
        
        Result is in the output directory
        
        2. ############################Update#############################
        How to update?
        
        (1). #################update database##################
        Before update database, user must complete the following steps: 
        a. download the newest database dump: http://archive.geneontology.org/latest-lite/
        b. add .sql to current database dump file, for example: change "go_20151003-assocdb-data" to "go_20151003-assocdb-data.sql"
        c. log into database on server and type the following command:
        	DROP DATABASE IF EXISTS assocdb
        	CREATE DATABASE IF EXISTS assocdb
        	quit
        d. type the following command: 
                mysql -h localhost -u username -p assocdb <dbdump
           for example: 
                mysql -h localhost -u username -p assocdb <go_20151003-assocdb-data.sql
        e. download 
        
        Then update database as following: 
        create an instance for updating database, then call the function:
        mydb = updateDB.UpdateDB("localhost", "fanyu", "hellowork", "assocdb", "gene_ontology/util/originalData/Homo_sapiens.gene_info.gene_info.txt")
        mydb.update()
        
        
        (2) #################update pubmeds##################
        
        ###download and parse###
        
        example of using download and parse pubmed
        pubmed_directory is the directory that user wants to store the pubmed articles 
        tool = downloadPubMed.DownloadPubMed("localhost", "fanyu", "hellowork", "assocdb")
        tool.parse()
        
        ###Name entity recognition process###
        
        The name entity recognition process this package using is ABNER. It was developed by  Burr Settles, Department of Computer Sciences, University of Wisconsin-Madison. It was written in Java. For more information, you can go to: http://pages.cs.wisc.edu/~bsettles/abner/
        
        Step of use ABNER.
        a. find these 3 files: abner.jar, Tagging.java, Tagging.class. They are wrapping up as extra file in the package. 
        b. when you find it and locate in the path, enter the following command in terminal:
        java -cp .:abner.jar Tagging  inputpath  outputpath
        input path indicates where you pubmeds are, outeutpath indicates where you want to store the pubmeds after ABNER analysis
        
        An example of using ABNER:
        
        java -cp .:abner.jar Tagging  /Users/YUFAN/Desktop/parsedPubMeds  /Users/YUFAN/Desktop/files.xml
        
        3. #################update weights##################
        This part builds a GOterm graph structure, and calculate the new weights in this structure
        
        The input file is parsed pubmeds with ABNER
        The output file is a GO term graph structure
        Stopwords is a txt file contains NLP stop words
        
        The input file path is where the parsed pubmeds are stored 
        The output file path is the directory where user want to store the output GO term graph structure
        
        input_filepath = "../taggedAbstracts/files.xml"
        output_filepath = "weightedGoGraph.xml"
        
        Example of how to update weights:
        g=goStructure.GoStructure("localhost", "fanyu", "hellowork", "assocdb", "files.xml", "")
        g.updateWeights()
        
        
        
Platform: UNKNOWN
