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        "author": "Jim Arnold",
        "author_email": "jimmyjamesarnold@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Interface Engine/Protocol Translator"
        ],
        "description": "# annoPipeline - an API-enabled gene annotation pipeline\n\n***annoPipeline*** uses APIs from [mygene.info](http://mygene.info/) and [Entrez esummary](https://dataguide.nlm.nih.gov/eutilities/utilities.html#esummary) to annotate a user-provided list of gene symbols.\n\nGenerates a pandas DataFrame with gene symbol, gene name, EntrezID, and bibliographic info for up to 5 pubmed publications where a functional reference was provided (more about functional references at [GeneRIF](https://www.ncbi.nlm.nih.gov/gene/about-generif)).\n\nDesigned to be useful for tasks such as:\n* identifying relevant publications for a given function\n* analyzing publications trends for genes belonging to a common pathway\n* identifying influential PIs for a given gene network. \n\n## Reqirements:\n\n* Written for use with Python 3.7, not tested on other versions.\n\n* *annoPipeline* requires:\n    - numpy >= 1.16.2\n    - pandas >= 0.24.2\n    - Biopython >= 1.73\n    - openpyxl >= 2.6.1\n    - requests >= 2.21.0\n\n## To Install:\n```\npip install annoPipeline\n```\n\nOr clone the repo from github.\nThen, in the annoPipeline directory, run:\n```\npython setup.py install\n```\nRequired dependencies will be installed if missing, may take a few seconds.\n\n## Example usage:\n\nExecute the full annotation pipeline on a list of gene symbols like this:\n```python\nimport annoPipeline as ap\n\n# define a list of genes you would like annotated\ngeneList = ['CDK2', 'FGFR1', 'SLC6A4']\n\n# annoPipeline will execute full annotation pipeline (see individual functions below). \ndf = ap.annoPipeline(geneList) # returns pandas df with annotations for gene and bibliographic info.\n```\n- ***ap.annoPipeline*** will default save annotation output to Excel file named by geneList symbols separated by '_'.\n\n### Warning! \nIf querying a **single gene**, still pass as a list. For example:\n```python\nimport annoPipeline as ap\n\ndf = ap.annoPipeline(['CDK2']) # for single gene queries still include [] - will be fixed in later version\n```\n\n\n## v0.0.1 Functionality\n\n### Task 1:\n1.  From the MyGeneInfo API, use the \u201cGene query service\" GET method to return details on a given list of human gene symbols.\n2.  From the returned json, parse out the \u201cname\", \u201csymbol\" and \u201centrezgene\" values and print to screen\n\nUse *queryGenes()*:\n```python\nimport annoPipeline as ap\n\ngeneList = ['CDK2', 'FGFR1', 'SLC6A4']\n\nl1 = ap.queryGenes(geneList) # returns list of dicts where keys are default mygene fields (symbol,name,taxid,entrezgene,ensemblgene)\n```\n\n### Task 2: \n1. \tUsing the appropriate identifier from the above result, send a query to the MyGeneInfo \u201cGene annotation services\" method for each gene\n2.\tFrom the resulting json, collate up to 5 generif descriptions per gene\n3.\tWrite the results to an Excel spreadsheet with columns: gene_symbol, gene_name, entrez_id, generifs\n\nUse *getAnno()*:\n```python\nimport annoPipeline as ap\n\ngeneList = ['CDK2', 'FGFR1', 'SLC6A4']\nl1 = ap.queryGenes(geneList)\nl2 = ap.getAnno(l1, saveExcel=True) # saveExcel defaults False\n```\n- returns pandas df with genes and up to 5 generifs from mygene.info. \n- default **saveExcel**=*False*, to save output to Excel must state *True*\n- if *True*, Excel file will be named by geneList symbols separated by '_'. \n\n### Task 3:\n1.  Use the Pubmed IDs associated with the above generif content to extract additional bibliographic information.\n\nUse *addBibs()*:\n```python\nimport annoPipeline as ap\n\ngeneList = ['CDK2', 'FGFR1', 'SLC6A4']\nl1 = ap.queryGenes(geneList)\nl2 = ap.getAnno(l1)\nl3 = ap.addBibs(l2) # will return df with genes and up to 5 generifs from mygene.info\n```  \n* Currently returns the following bibliographic information when available:\n    * PubDate\n    * Source\n    * Title\n    * LastAuthor\n    * DOI\n    * PmcRefCount\n\n",
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