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Status](https://travis-ci.org/scastlara/ppaxe.svg?branch=master)](https://travis-ci.org/scastlara/ppaxe) \n[![Coverage Status](https://coveralls.io/repos/github/scastlara/ppaxe/badge.svg?branch=master)](https://coveralls.io/github/scastlara/ppaxe?branch=master) \n[![PyPI version](https://badge.fury.io/py/ppaxe.svg)](https://badge.fury.io/py/ppaxe)\n\n\n\n\u003cimg width=\"350\" src=\"ppaxe/logo.png\"/\u003e\n\n-----\n\nTool to retrieve **protein-protein interactions** and calculate protein/gene symbol ocurrence in the scientific literature (PubMed \u0026 PubMedCentral). Contains two python modules (`core` and `report`), and a python script (`ppaxe`).\n\nAvailable for `python 2.7` and `python 3.x`, and also as a standalone [docker image](https://hub.docker.com/r/compgenlabub/ppaxe/).\n\n\u003e **Visit the [PPaxe web application](https://compgen.bio.ub.edu/PPaxe) to use PPaxe on the web.**\n\n## Citation\n\n```\nS. Castillo-Lara, J.F. Abril\nPPaxe: easy extraction of protein occurrence and interactions from the scientific literature\nBioinformatics, AOP November 2018, bty988.\n```\n* [Link to the published paper](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty988/5221011)\n\n\n## Quick Installation\n\nTo download and use the ppaxe Docker image:\n\n```sh\ndocker pull compgenlabub/ppaxe:latest\ndocker run -v /local/path/to/output:/ppaxe/output:rw \\\n              compgenlabub/ppaxe -v -p ./papers.pmids -o ./output.tbl -r ./report\n```\n\nIf you want to install PPaxe manually, go to the [Install ppaxe manually](#install-ppaxe-manually) section.\n\n\n## Usage\n\n```\nusage: ppaxe [-h] -p PMIDS [-d DATABASE] [-o OUTPUT] [-r REPORT] [-i IP] [-v]\n             [-e]\n\nCommand-line tool to retrieve protein-protein interactions from the scientific\nliterature.\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -p PMIDS, --pmids PMIDS\n                        Text file with a list of PMids or PMCids\n  -d DATABASE, --database DATABASE\n                        Download whole articles from database \"PMC\", or only\n                        abstracts from \"PUBMED\".\n  -o OUTPUT, --output OUTPUT\n                        Output file to print the retrieved interactions in\n                        tabular format.\n  -r REPORT, --report REPORT\n                        Print html report with the specified name.\n  -i IP, --ip IP        Change the IP address of the StanfordCoreNLP server.\n                        Default: http://localhost:9000\n  -v, --verbose         Increase output verbosity.\n  -e, --exclude         Exclude protein symbols not annotated in dictionary.\n```\n\n\n### ppaxe classes\n\n```python\nfrom ppaxe import core as ppcore\nfrom ppaxe import report\n\n# Perform query to PubMedCentral\npmids = [\"28615517\",\"28839427\",\"28831451\",\"28824332\",\"28819371\",\"28819357\"]\nquery = ppcore.PMQuery(ids=pmids, database=\"PMC\")\nquery.get_articles()\n\n# Retrieve interactions from text\nfor article in query:\n    article.extract_interactions()\n\n# Get the predictions\nfor prediction in article.predictions:\n  print(prediction.to_html())\n\n# Print html report\n# Will create 'report_file.html'\nsummary = report.ReportSummary(query)\nsummary.make_report(\"report_file\")\n```\n\n### ppaxe script\n\n```sh\n# Will read PubMed ids in pmids.txt, predict the interactions\n# in their fulltext from PubMedCentral, and print a tabular output\n# and an html report\nppaxe -p pmids.txt -d PMC -v -o output.tbl -r report\n\n# Or with docker image\ndocker run -v /local/path/to/output:/ppaxe/output:rw compgenlabub/ppaxe -v -p pmids.txt -o output.tbl -r report\n```\n\n### Report\n\nThe report output (`option -r`) will contain a simple summary of the analysis, the interactions retrieved (including the sentences from which they were retrieved), a table with the protein/gene counts and a graph visualization made using [cytoscape.js](http://js.cytoscape.org/).\n\n\u003cimg src=\"https://raw.githubusercontent.com/scastlara/ppaxe/master/ppaxe/data/report1-example.png\"/\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/scastlara/ppaxe/master/ppaxe/data/report2-example.png\"/\u003e\n\n\n\n## Install ppaxe manually\n\n* **Prerequisites**\n\n```sh\nxml.dom\nnumpy\npycorenlp\ncPickle\nscipy\n```\n\nYou can install this package manuallly using _pip_. However, before doing so, you have to download the [Random Forest predictor](https://www.dropbox.com/s/t6qcl19g536c0zu/RF_scikit.pkl?dl=0) and place it in `ppaxe/data`.\n\n```sh\n# Clone the repository\ngit clone https://github.com/scastlara/ppaxe.git\n\n# Download pickle with RF\nwget https://www.dropbox.com/s/t6qcl19g536c0zu/RF_scikit.pkl?dl=0 -O ppaxe/ppaxe/data/RF_scikit.pkl\n\n# Install\npip install ppaxe\n```\n\n* **Download StanfordCoreNLP**\n\nIn order to use the package you will need a [StanfordCoreNLP](https://stanfordnlp.github.io/CoreNLP) server setup with the [Protein/gene Tagger](https://www.dropbox.com/s/ec3a4ey7s0k6qgy/FINAL-ner-model.AImed%2BMedTag%2BBioInfer.ser.gz?dl=0).\n\n```sh\n # Download StanfordCoreNLP\n wget http://nlp.stanford.edu/software/stanford-corenlp-full-2017-06-09.zip\n unzip stanford-corenlp-full-2017-06-09.zip\n\n # Download the Protein tagger\n wget https://www.dropbox.com/s/ec3a4ey7s0k6qgy/FINAL-ner-model.AImed%2BMedTag%2BBioInfer.ser.gz?dl=0 -O FINAL-ner-model.AImed+MedTag+BioInfer.ser.gz\n\n # Download English tagger models\n wget http://nlp.stanford.edu/software/stanford-english-corenlp-2017-06-09-models.jar -O stanford-corenlp-full-2017-06-09/stanford-english-corenlp-2017-06-09-models.jar\n\n # Change the location of the tagger in ppaxe/data/server.properties if necessary\n # ...\n\n # Start the StanfordCoreNLP server\n cd stanford-corenlp-full-2017-06-09/\njava -mx1000m -cp ./stanford-corenlp-3.8.0.jar:stanford-english-corenlp-2017-06-09-models.jar edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -serverProperties ~/ppaxe/ppaxe/data/server.properties\n```\n\nOnce the server is up and running and ppaxe has been installed, you are good to go.\n\nBy default, ppaxe will assume the server is available at localhost:9000. If you want to change the address, set up the server with the appropiate port and change the address in ppaxe by assigning the new address to the variable ppaxe.ppcore.NLP:\n\n* **Start the server**\n\n```sh\n# Change the location of the ner tagger in server.properties manually\njava -mx10000m -cp ./stanford-corenlp-3.8.0.jar:stanford-english-corenlp-2017-06-09-models.jar edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port your_port -serverProperties ppaxe/data/server.properties\n```\n\n* **Use the ppaxe package**\n\n```python\nfrom ppaxe import core as ppcore\nfrom pycorenlp import StanfordCoreNLP\n\nppcore.NLP = StanfordCoreNLP(your_new_adress)\n\n# Do whatever you want\n```\n\n## Using the Gene dictionary\n\nBy default, PPaxe uses the [HGNC](https://www.genenames.org/) dictionary of gene symbols to normalize the protein/gene symbols found in the article. The `ppaxe` command-line tool has the option `-e` that restricts all the results to only those proteins that match against the HGNC database. Users can change this file (located at `ppaxe/data/HGNC_gene_dictionary.txt`) in order to restrict their searches to only specific genes or proteins, or to normalize gene names using a different dictionary.\n\n\n## Documentation\n\nRefer to the [wiki](https://github.com/scastlara/ppaxe/wiki/Documentation) of the package.\n\n## Running the tests\n\nTo run the tests:\n\n```\npython -m pytest -v tests\n```\n\n## Authors\n\n* **Sergio Castillo-Lara** - at the [Computational Genomics Lab](https://compgen.bio.ub.edu)\n\n\n## License\n\nThis project is licensed under the GNU GPL3 license - see the [LICENSE](LICENSE) file for details\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscastlara%2Fppaxe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscastlara%2Fppaxe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscastlara%2Fppaxe/lists"}